# Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues

Authors: Mohammed Maray; Junaid Shuja
Journal: Mobile Information Systems
 (2022)

---

## Abstract

Cloud and mobile edge computing (MEC) provides a wide range of computing services for mobile applications. In particular, mobile edge computing enables a computing and storage infrastructure provisioned closely to the end-users at the edge of a cellular network. The small base stations are deployed to establish a mobile edge network that can be coined with cloud infrastructure. A large number of enterprises and individuals rely on services offered by mobile edge and clouds to meet their computational and storage demands. Based on user behavior and demand, the computational tasks are first offloaded from mobile users to the mobile edge network and then executed at one or several specific base stations in the mobile edge network. The MEC architecture has the capability to handle a large number of devices that in turn generate high volumes of traffic. In this work, we first provide a holistic overview of MCC/MEC technology that includes the background and evolution of remote computation technologies. Then, the main part of this paper surveys up-to-date research on the concepts of offloading mechanisms, offloading granularities, and computational offloading techniques. Furthermore, we discuss the offloading mechanism in the static and dynamic environment along with optimization techniques. We further discuss the challenges and potential future directions for MEC research.


---

## Body

## 1. Introduction

There has been huge advancement and evolution in the field of computing technology. Despite the enhancements, the computational capacity and energy consumption of the ecosystems such as smartphones or Internet-of-Things (IoT) devices are nowhere near that of powerful computing machines that use powerful CPUs. The growth of intensive and real-time applications, such as applications with Augmented Reality, Multimedia, Video Editing, Face Recognition, and Gaming, has increased the computational requirement and energy consumption of these ecosystems. The limitations of ecosystems such as low battery power, low capacity to store data, and most of all limited processing capacity need to be tackled at a fundamental level in the era of intensive applications development [1]. Computation offloading mechanism has been the best available solution up to now, driving the ecosystems to offload the intensive computational functions to remote computation resources such as edge-based servers as shown in Figure 1, which has huge computation resources and can perform the operations faster than local ecosystem resources [2–6].Figure 1
Computational offloading in cloud and cloudlet.History of remote computation pointed toward the early 1990s when remote execution and inter process communications were beginning to emerge to utilize the resources in cluster computers at fullest and management of message-passing traffic [7, 8]. Despite the benefit of remote computation, the parallel running challenges diminished the popularity of the concept at that time. Nevertheless, the development of Internet provided a new pathway to develop further the concept of remote execution, which enabled the establishment of a new foundation called Service Oriented Architecture (SOA). Mobile Web Services (MWS) include SOA along with portable devices, which enabled enhancement of the computation capability and saved energy by allowing the mutual share of services and software between mobile devices and other devices [9]. However, its reliance on a static network produced the drawback of unstable performance.Later, studies suggested the use of nearby mobile resources for ad hoc services to improve performance by using short-range wireless communications. Furthermore, the use of the ad hoc wireless network was exploited to develop Mobile Ad hoc Network (MANET) during the mid-1990s. MANET provides a self-configuring mobile network device, but possesses problems in the adaptability of network functions due to the mobility of devices within the network environment, and provided limited resources from a collaborative pool developed with other mobile devices. Development of pervasive computing in later years initiated the wide-range computation platform, which enabled computation migration between any devices through the use of various sorts of networks in the mobile state of users [10]. This further opened possibilities of merging the computation resources to provide continuous services through a wide range of resources such as desktop, mobile devices, and servers. In recent years though, the initiation of cloud computing has opened up a new reliable technological basis for performance enhancements in mobile devices [11, 12].Computation offloading with Mobile Cloud Computing (MCC) started late (2009), and it was based only on the mobile devices side and the main cloud server side to offload tasks [13, 14]. However, in the computation offloading technique of MCC, the main cloud side is not close to the mobile devices side during offloading operations, which leads to the latency problem on the middle-ware of media connection and the significant defect of rendering the user mobility impossible during the offloading task [15–18]. At the end of (2014), Mobile Edge Computing (MEC) was introduced as a means to help resolve the latency problem that happens during the offloading process in MCC [19]. The characteristic feature of MEC is the need for small latency and offer of high workload capacity while being near to the user and their devices [5]. The transmission and computational delays are found to be very small in the MECs, as these are nearest to the users unlike the remote resources of traditional computation offloading in MCC.Computation offloading technique nowadays is popularly used to tackle the smartphone limitations and provide effective computation [20]. Traditional client-server architecture, grid computing, or multiprocessor system are some of the conventional systems migrating their computation to their nearest server for the reduction of resources utilization, enhancement of the performance, and load balancing [12]. Since its introduction, the utilization of the computation offloading technique has been stretched beyond its initial scope. The computation offloading technique of mobile devices differs from the traditional computation offloading technique in the sense that it does not utilize only the resources available nearest to the mobile device. Instead, the offloading is done in an environment which is exclusively outside of the nearest computing environment available.Computation offloading significantly improves the performance of MEC and MCC while minimizing execution latency and energy. Similarly, MEC offloading facilitates the computational requirements of end devices by bringing computing nodes to the network edge. Computation offloading has been debated in several studies and surveys, targeting the taxonomy of MEC and MCC separately. To the best of the author’s knowledge, joint computation offloading techniques and taxonomy in both MCC and MEC scenarios have not been surveyed. To address this issue, we focused on the state-of-the-art in both MCC and MEC and comparison between them with detailed taxonomy. A comparison of this survey with existing surveys is presented in Table1.Table 1
Comparison with existing surveys.
Ref.ContributionComputing platformsConcentration[21]Survey of mobility aware MEC offloadingMECMobility prediction[22]Survey of wireless powered MEC offloadingMECWPT-based networks[23]Survey of offloading algorithmsMCC and MECAlgorithms[24]Survey of multiobjective decision-making frameworksMCCMultiobjective frameworksThis studySurvey of MEC and MCC offloading frameworksMCC and MECComputing environments, optimization scenarios, and granularityWe present the following novel contributions in the survey:(i)
We formulate a detailed taxonomy of computation offloading in both computation resource of MEC and MCC scenarios including environments, optimization scenarios, granularities, and issues.(ii)
We provide a detailed literature review of computation offloading in MCC and MEC.(iii)
We compare the listed literature review with different parameters.(iv)
We debate the challenges and future research directions for MEC and MCCC offloading.The rest of the article is organized as follows: Section2 presents the detailed taxonomy of the research domain. Section 3 provides a detailed discussion on the state-of-the-art studies in MCC and MEC. Section 4 provides the comparison between computation offloading in both MCC and MEC. Section 5 debates the challenges and future research direction, and finally, we conclude the survey in Section 6.


## 2. Taxonomy

This section provides the detailed taxonomy of computation offloading in MCC, Fog, and MEC. The purpose of this taxonomy is to provide the knowledge of multiple offloading environments, and optimization scenarios with issues and granularities. Figure2 illustrates the taxonomy with major classifications as (a) computation resource, (b) environment (static and dynamic), (c) optimization scenarios, (d) granularities, and (e) applications of computation offloading. The parameters of the taxonomy are explained as follows.Figure 2
Taxonomy of computation offloading.


### 2.1. Computation Resource

We introduce the concepts of the remote computation resources, i.e., mobile cloud computing, fog computing, and mobile edge computing as shown in Figure3. We will introduce concepts of the remote computation resources as follows:Figure 3
Remote computation resources levels.


#### 2.1.1. Mobile Computing

Mobile computing is the execution of data and applications in portable devices and mobile devices, while the transfer of data between two or more mobile devices is known as mobile communication. Software, information, applications, and another form of technological instructions are deployed within a small portable device, which is distributed widely and connected through various sorts of wireless connections. The distributed resources, which are centrally located within each device used, are connected through the use of mobile computing technology. The increasing popularity of mobile devices among people has increased expectations of quality and service level which they offer [25–27].


#### 2.1.2. Cloud Computing

Cloud computing is the centralized computation of the computing services within a single environment, allocating the necessary portion of that environment as per service demand in one of three types of service: Software As a Service (SAAS), Platform As a Service (PAAS), or Infrastructure As a Service (IAAS) [28]. The services provided by the cloud are purely dependent upon what services have been demanded by the users. Service is determined by the type of device that shares resources and the offloaded functions and contents from user devices. This gave birth to the concept of mobile cloud computing. Mobile cloud computing (MCC) is the distributed computation of mobile applications, by offloading some of the computational functions to the cloud via a network, within the single environment of the cloud providing the resources as per each user’s need. This produces an opportunistic use of mobile edge computing (MEC) surrounding resources for the improvement of MCC functionality in the network issues since the edge is closed to the mobile devices and the cloud is so far away from the mobile devices [29].


#### 2.1.3. Mobile Cloud Computing (MCC)

Mobile cloud computing (MCC) is an emerging and innovative technology utilizing the unified resources of different clouds thus exploiting the elastic nature of the cloud computation, providing unlimited ever present services to mobile devices regardless of the location of service demands, and accommodating client service level demands [30]. These services are mutually shared between cloud side and mobile devices side through the network. MCC provides for a wide range of mobile device users an environment where computation processing and storage of mobile device data are done in the cloud which has been allocated exclusively to the particular mobile device rather than within the device concerned, regardless of the kinds of mobile devices being used which provided the MCC services [31]. The driving force behind the development of MCC is to enable limitless computation in mobile devices while minimizing the challenges inherent in the current mobile computation technology.


#### 2.1.4. Fog Computing

Fog computing is a remote computing paradigm that acts as an intermediate layer between the cloud and cloudlet, so that cloud-based services can be extended closer to ecosystems [32, 33]. Cloud data centers often fail to meet the storage and processing demands of billions of geodistributed IoT devices and sensors with the consequence of congested networks, high latency in service delivery, and poor Quality of Service (QoS). Edge computing backed by powerful computing resources can reduce the network latency and render the nearby cloudlet accessible by edge users through a one-hop high-speed wireless local area network. To reduce the delay during offloading, cloudlets will be the right offloading decision to get the task result fast with the minimum delay, and the cloudlet will be in the edge layer that is closest to the edge users. The fog computing will be in the middle layer between the edge layer (cloudlets) and the cloud layer (cloud) [34, 35].


#### 2.1.5. Mobile Edge Computing (MEC)

Mobile edge computing (MEC) is an innovative architecture, which enables the functionalities of cloud computing at the edge of the mobile network. The main idea regarding MEC is to bring resources of cloud computing near the end-user and serve the request of the end-user locally. MEC helps the computation offloading process to get low latency during offloading tasks and reduces the traffic in the network as low requests are accelerated to the cloud server. The MEC architecture is proposed by ETSI where they presumed that cloud functionality, such as storage and computation, would be integrated with edge network devices such as small cell access points, macro base stations, radio network controllers, and macro base station [36]. The idea of Cloudlets was produced in 2009 as a trusted local rich computation resource or multicore rich resources, which are well linked to the Internet through wireless LAN and are available for use by nearby mobile devices users [37]. Cloudlets use a Wi-Fi network to offload computational tasks, which helps save a considerable amount of energy from mobile devices compared to offloading through a cellular 3G/LTE network [38, 39]. The cloudlet mechanism extends the battery life of the mobile device, thus reducing network latency; on the other hand, it improves the quality of experience (QoE) of the end-user [40]. Therefore, deployment of the cloudlets will be similar to that of Wi-Fi hotspot configuration and will be close to the edge users.The above discussion reveals that MCC servers may be used for offloading of application which requires huge computational and storage resources. MEC servers may be used for the latency critical applications due to the presence in closer proximity with fewer computational and storage resources.


### 2.2. Optimisation Scenarios

In the edge environments, allocating the best place to offload the tasks is a challenging task because multiple criteria must be taken into account, including limitation of resources and proximity of cloudlets [41]. Methods designed to solve this problem falls into the following categories: classical optimization, metaheuristic, game theory, and machine learning.


#### 2.2.1. Classical Optimization

Classical approaches can produce better accuracy at the expense of high computational time-consuming. In case the problem is nonlinear, or has a huge size, classical approaches are stuck in local optima.


#### 2.2.2. Metaheuristic Optimization

Consequently, researchers moved towards the use of metaheuristics as it provides a nearly optimal solution with a reasonable computation [42]. Recently, many solutions are advanced regarding the optimization of the offloading process in the edge networks. Offloading task to MCC/MEC platforms has been received lots of attention from the research community [43–48]. However, published studies have not considered the optimisation of execution latency subject to task precedence with task constraints and user mobility; this has also been observed in recent work [20, 49–52].Existing work on offloading optimization of the assignment oftasks to edge resources can be categorised according to the optimization objective as follows: (i) minimizing the response time (delay) in task execution [12, 53–57] and (ii) maximising the energy savings of user equipment [46, 58–63]. Some studies also considered both energy consumption and delay, opting to strike a balance [64–68]. A comparative summary of related works is shown in Table 2.Table 2
Comparative summary of related works.
FrameworkObjectiveTasks dependencyUser mobilityPlatformOptimisation algorithmRashidi and Sharifian [41]Delay & energyIndependentNo-mobilityMCCQDM & GA & ACO algorithmsYang et al. [69]DelayIndependentMobilityMECLocation-based offloading schemeYang et al. [70]Delay & energyDependentNo-mobilityMECASO and pro-ITG algorithmsMirjalili and Lewis [71]Delay & energyIndependentNo-mobilityMCCWhale optimization algorithm (WOA) algorithmYang et al. [72]Delay & energyIndependentNo-mobilityMECArtificial fish swarm (AFSA)Kao et al. [73]DelayDependentNo-mobilityMECFully polynomial time approximation (FPTAS)Wang et al. [53]DelayIndependentMobilityMECHeuristic algorithmZhang et al. [74]Delay & energyIndependentNo-mobilityMECWeight-sum functionPeng et al. [75]Delay & energyIndependentNo-mobilityMCCWhale optimization algorithm (WOA)Huang et al. [76]Delay & energyIndependentNo-mobilityMECAnt colony system (ACS)Yuyi et al. [66]Delay & energyIndependentNo-mobilityMECGame theoryBi et al. [77]Delay & energyIndependentNo-mobilityMECHybrid metaheuristic algorithmShu et al. [78]DelayDependentNo-mobilityMECHeuristic algorithmKai et al. [79]DelayIndependentNo-mobilityMECSuccessive convex approximation (SCA)Sun et al. [80]Delay& energyIndependentNo-mobilityMECLyapunov optimization algorithmBi et al. [81]Delay& energyIndependentNo-mobilityMECMixed integer nonlinear programming (MINLP)Shan et al. [82]Delay& energyIndependentNo-mobilityMECCov-AHP & Nash equilibrium algorithmErana Veerappa Dinesh and Valarmathi [83]Delay & energyIndependentNo-mobilityMCCEnergy estimation model (RG-EEM)Raj [84]Delay & energyIndependentNo-mobilityMCCAnt colony optimization (ACO) algorithmGu et al. [85]DelayIndependentNo-mobilityMCCParticle swarm optimization (MPSO) algorithmSundararaj [86]DelayIndependentNo-mobilityMCCQueue ant colony (QAnt-Bee) algorithmLiu et al. [87]Delay & energyIndependentNo-mobilityMECLyapunov optimization algorithmLiu et al. [88]DelayDependentNo-mobilityMECGenDoc algorithmHuy Hoang et al. [89]DelayIndependentMobilityMECHeuristic algorithmThananjeyan [90]Delay & energyIndependentMobilityMECComputational intensity (CI)


#### 2.2.3. Game Theory

Game theory is one of the most important optimization approaches which is also called science of strategy. Using this approach, authors of [91] solved the resource allocation problem in computation offloading with a very low computational complexity. Another study solved the problem of resource scheduling mechanism for cooperative cloudlets to reduce the operator’s cost and preserve the user experience in MEC with a centralized controller [92].


#### 2.2.4. Machine Learning

Machine learning including deep learning are widely recognized as efficient optimization approaches succeeding the traditional optimization schemes. These approaches make better decisions while offloading of tasks at the MEC or MCC servers. Besides, resource allocation including channel access, CPU cycles, time allocations, and other resources allocation problems may be effectively solved through these schemes [93].


### 2.3. Offloading Environment

The offloading environment dynamics may be divided into the application modules and optimization objectives.


#### 2.3.1. Application Modules

Frameworks in mobile cloud computing focused on the problems regarding offloading decision-making and application partitioning in offloading tasks from mobile devices to the main cloud without considering user mobility or changes that could happen in the network connection during offloading operations [18, 94, 95]. They have solved the offloading decision-making problem and the application partitioning problem using a mechanism that consists of three key elements: (a) Partitioner, (b) Profiler, and (c) Solver [96]. This mechanism helps to decide whether it is favorable to offload the task to the cloud side or just execute it locally on the mobile device [97]. The process of computation offloading in MCC is depicted in Figure 4.Figure 4
Process of computation offloading in mobile cloud computing.(1) Partitioner. The partitioner is used to annotate which portion of the application is considered an offloadable task. An annotated partition is achieved through results from application analysis made on codes of computation. It is determined based on whether the codes are accessing native resources of the mobile environment, or not. The mobile environment comprises native resources that include access to I/O interfaces, GPS, Camera, native services that include the particular mobile environment, or any other hardware embedded in mobile devices [97].(2) Profiler. Profiler is used to monitor offloading parameters that will help the framework solver to make the final decision whether to offload the task or not. Therefore, the profiler will be an important factor in making the final decision in the solver part. Profiler can monitor decision parameters such as CPUs or energy power. Some frameworks used monitor software such as ThinkAir framework, which uses Power Tutor software to track various programme-related parameters. It extracts overall the execution time for a particular method, CPU cycles, and memory allocation of a particular thread, method call numbers, and executed instruction numbers [95]. Other some frameworks used monitor device such as CloneCloud framework utilises Monsoon Power device to monitor three system variables: CPU activity (active and idle state), Screen (on/off state), and Network interface during active state (transferring/receiving) or idle state. They translate these variables into power draw via function P from (CPU, Scr, Net) triples to an energy value. This generates two cost model, once when CPU ON locally on mobile device, and when the CPU idle during offloading process [94]. The cost of power consumption can be estimated as(1)Cci,0≡PCPUON,ScrON,NetIdle×Ti.The second cost model of power consumption with clone in server with screen ON and idle CPU in mobile device to calculate only energy consumption with network active during migration operation is as follows:(2)Cci,1≡PCPUIdle,ScrON,NetIdle.(3) Solver. The solver of the computation offloading frameworks in MCC is the part that makes the feasible offloading decision based on the available partitions and decision metric developed by using parameters from profiler or directly utilizing profiler parameters to optimise solution of the decision. The solver can be categorised according to its location, whether it is located on the mobile device, in the remote cloud/server, or in both [97]. In this work, the solver is used for assigning a value to decision variable based on minimization of expected cost of a particular partitioned application. It is used to make a final decision about the offloading by the framework and is located in the user of the mobile device [94]. Another work uses a linear program solver in both side of mobile device and cloud as shown in Figure 5 to solve a global optimization problem developed by using input such as annotation and graphs from the annotated call graph model developed for partitioning model of the framework. The energy used during local execution, remote execution, and time spent for local and remote execution are taken as decision-making metrics for the solver as shown in Figure 6.Figure 5
Tasks with dependencies.Figure 6
Solver in mobile device side and server side of MAUI framework.The [98] application [98] call graph G = (V, E) is used by the solver to develop the optimization problem as a call stack to execute the programme, where (V) represents the sets of vertex as a method of the call stack, and each vertex v belongs to the set (V). The edges are represented as e = (u, v), indicating the invocation from method (u) to method (v). Energy and time taken to execute a vertex (v) as a method locally on mobile device is represented by Evl, and Tvl, respectively, and time taken to execute the method remotely in the cloud is represented as Tvr. For edge e = (u, v), the time and energy cost taken to transfer the necessary states of the program during a call from (u) to (v) is annotated as Bu,v and Cu,v, respectively. Each method is also annotated with parameter rv to indicate if it is able to migrate to server side or not. The MAUI solver uses the indicator variable Iv, and when it is (0) it shows that method (v) is executed locally on a mobile device. Otherwise, (1) value indicates it is executed remotely in the server side. (L) is a default latency constraint: if all methods (v) are executed on the mobile device, the total execution of L must not exceed 5% more than the local latency on the mobile device. MAUI solver solves the 0-1 Integer Linear programming ILP problem as follows:(3)Maximize∑v∈VIv×Evl−∑u,v∈EIv−Iv×Cu,v,s.t.∑v∈V1−Iv×Tvl+Iv×Tvr,+∑u,v∈EIv−Iv×Bu,v<=L,Iv<=rv,∀v∈V.Other intensive frameworks in MCC have used the same mechanism of binary variable decision without considering network fluctuations [18, 94–96, 98]. Mobile edge computing was produced to overcome the network fluctuations that will happen after offloading decision is made [52].


#### 2.3.2. Optimization Objectives

Recent works in the mobile edge computing dealing with the dynamic computation offloading are focusing on the task offloading optimization problem in the edge. The objectives of computation offloading may be categorised into three categories: (a) reduction of delays (minimising latency in tasks offloading in the edge, (b) saving energy, and (c) reducing ISP cost.(1) Minimising Latency. Through dynamic computation offloading of compute intensive tasks MCC server, the quality of computation experience may be greatly improved in the context of execution latency. However, transmission latency may degrade the performance in MCC. To this end, the MEC plays a crucial role by placing computing nodes at the edge of the network. This scenario effectively reduces transmission latency while preserving execution time [99].(2) Saving Energy. Dynamic computation offloading effectively saves energy by allowing timely offloading decisions based on different parameters, including the computational power of the end nodes, the computation node, and the channel conditions. To further enhance the battery lives of edge nodes, dynamic computation offloading allows energy harvesting approaches to work together with offloading [22].(3) Reducing ISP Cost. The services are migrated nearer to the user in MEC, cloudlets, and fog computing. As a result, the user requests do not travel through multiple ISPs reducing network congestion and ISP cost to service user requests [99].


### 2.4. Granularities

Offloading granularities enable different levels of tasks to be offloaded in the cloud or on the edge server. These granularities can be classified into two categories, including fine grain and coarse grain.


#### 2.4.1. Fine Grain

First defines a fine-grained mobile code offloading structure [100, 101], which is also known as partial offloading scheme. This approach relies on developers to annotate the offloading parts, within an application, and the main aim of this approach is to improve the efficiency of energy utilization in mobile devices. This aim is achieved by offloading annotated parts such as methods or threads to gain energy utilization efficiency. Fine-grain granularity is a useful offloading type for applications that have tasks that use the hardware of the mobile device and that cannot be offloaded outside the mobile devices, such as using the speaker or screen of the mobile device.


#### 2.4.2. Coarse Grain

Coarse-grained offloading is the second granularity of this approach. In this approach, full application/program, or a process, or a whole virtual machine is offloaded to the remote computing resources and it is called full offloading approach [25, 102].


### 2.5. Offloading Applications

There are multiple applications of offloading exists in MEC and MCC. These applications are categorised into local computation, real-time applications, resource hungry applications, periodic offloading, and nonperiodic offloading. In the following sections, we discuss each category.


#### 2.5.1. Local Computation

The offload of resource-critical tasks to the computing server effectively maximises the computation rate in terms of bits. This mechanism also minimises the overall computation rate in terms of computing bits. However, some applications require local computation because of their extreme sensitivity in the context of latency. These applications prefer to be executed at the end nodes [103].


#### 2.5.2. Real-Time Applications

Although some applications are time sensitive, these applications may still be offloaded at the end server for effective computation while guaranteeing the scheduling of tasks. However, it is necessary to schedule these tasks in an effective timely manner to minimise the latency that may affect these applications.


#### 2.5.3. Resource Hungry Applications

Resource hungry applications, including augmented reality, virtual reality, and multimedia applications, require huge computational and storage resources. These applications may be effectively executed at the mobile cloud servers with required resource. However, latency constraints in these cloud-based systems may degrade the performance. MEC significantly plays a crucial role to enhance the performance of resource hungry applications by minimizing the latency. In this mechanism, less resource of edge servers may be improved with some resource management techniques [104].


#### 2.5.4. Periodic/Nonperiodic Offloading

Some applications require periodic computation of data. These applications may be for traffic monitoring and surveillance. Due to continuous monitoring, these applications offload their computational tasks at the end server to save the battery lives of sensor nodes which involve in periodic monitoring of environment. Offloading may also be nonperiodic, in which an end device offloads its compute intensive task when it requires. These applications do not need periodic computation of tasks [105, 106].


### 2.6. Offloading Network

Network state largely impacts the computational offloading while migrating tasks to computational servers. These states should be considered while migration of compute intensive tasks. We consider the following networks in this study.


#### 2.6.1. Wireless Sensor Networks

Wireless sensor networks (WSNs) are dynamic networks with both mobile and stationary sensors placed to monitor physical conditions of an area. Due to ad hoc nature of WSN, the sensor nodes often require the amalgamation with MEC and MCC to complete computational tasks. Edge servers can be placed near a WSN that needs to offload data and computation periodically [20].


#### 2.6.2. Cellular Network

The users of cellular networks utilize different social media applications of watch video content hosted on distant cloud data centers. The hosting of the content in CDNs and edge networks facilitates the ISP to offload traffic from core network while servicing user requests from the access network [107, 108]. The traffic offload from core network lowers the ISP costs and reduces the access latency.


#### 2.6.3. IoT

Internet of Things (IoT) is a set of computing devices connected with the network for accessibility. Sensors and embedded devices with communication interfaces provide automation over the network with easy to use software interfaces. The IoTs are often resource-constrained necessitating merger with MEC and MCC.


#### 2.6.4. Vehicular Networks

Vehicles moving at high-speed consist of infotainment and navigation applications with users often resorting to mobile devices for data services. MEC, fog, and cloudlets services residing in road side units (RSU) can be employed for offloading data and compute services from vehicular networks [109].


## 2.1. Computation Resource

We introduce the concepts of the remote computation resources, i.e., mobile cloud computing, fog computing, and mobile edge computing as shown in Figure3. We will introduce concepts of the remote computation resources as follows:Figure 3
Remote computation resources levels.


### 2.1.1. Mobile Computing

Mobile computing is the execution of data and applications in portable devices and mobile devices, while the transfer of data between two or more mobile devices is known as mobile communication. Software, information, applications, and another form of technological instructions are deployed within a small portable device, which is distributed widely and connected through various sorts of wireless connections. The distributed resources, which are centrally located within each device used, are connected through the use of mobile computing technology. The increasing popularity of mobile devices among people has increased expectations of quality and service level which they offer [25–27].


### 2.1.2. Cloud Computing

Cloud computing is the centralized computation of the computing services within a single environment, allocating the necessary portion of that environment as per service demand in one of three types of service: Software As a Service (SAAS), Platform As a Service (PAAS), or Infrastructure As a Service (IAAS) [28]. The services provided by the cloud are purely dependent upon what services have been demanded by the users. Service is determined by the type of device that shares resources and the offloaded functions and contents from user devices. This gave birth to the concept of mobile cloud computing. Mobile cloud computing (MCC) is the distributed computation of mobile applications, by offloading some of the computational functions to the cloud via a network, within the single environment of the cloud providing the resources as per each user’s need. This produces an opportunistic use of mobile edge computing (MEC) surrounding resources for the improvement of MCC functionality in the network issues since the edge is closed to the mobile devices and the cloud is so far away from the mobile devices [29].


### 2.1.3. Mobile Cloud Computing (MCC)

Mobile cloud computing (MCC) is an emerging and innovative technology utilizing the unified resources of different clouds thus exploiting the elastic nature of the cloud computation, providing unlimited ever present services to mobile devices regardless of the location of service demands, and accommodating client service level demands [30]. These services are mutually shared between cloud side and mobile devices side through the network. MCC provides for a wide range of mobile device users an environment where computation processing and storage of mobile device data are done in the cloud which has been allocated exclusively to the particular mobile device rather than within the device concerned, regardless of the kinds of mobile devices being used which provided the MCC services [31]. The driving force behind the development of MCC is to enable limitless computation in mobile devices while minimizing the challenges inherent in the current mobile computation technology.


### 2.1.4. Fog Computing

Fog computing is a remote computing paradigm that acts as an intermediate layer between the cloud and cloudlet, so that cloud-based services can be extended closer to ecosystems [32, 33]. Cloud data centers often fail to meet the storage and processing demands of billions of geodistributed IoT devices and sensors with the consequence of congested networks, high latency in service delivery, and poor Quality of Service (QoS). Edge computing backed by powerful computing resources can reduce the network latency and render the nearby cloudlet accessible by edge users through a one-hop high-speed wireless local area network. To reduce the delay during offloading, cloudlets will be the right offloading decision to get the task result fast with the minimum delay, and the cloudlet will be in the edge layer that is closest to the edge users. The fog computing will be in the middle layer between the edge layer (cloudlets) and the cloud layer (cloud) [34, 35].


### 2.1.5. Mobile Edge Computing (MEC)

Mobile edge computing (MEC) is an innovative architecture, which enables the functionalities of cloud computing at the edge of the mobile network. The main idea regarding MEC is to bring resources of cloud computing near the end-user and serve the request of the end-user locally. MEC helps the computation offloading process to get low latency during offloading tasks and reduces the traffic in the network as low requests are accelerated to the cloud server. The MEC architecture is proposed by ETSI where they presumed that cloud functionality, such as storage and computation, would be integrated with edge network devices such as small cell access points, macro base stations, radio network controllers, and macro base station [36]. The idea of Cloudlets was produced in 2009 as a trusted local rich computation resource or multicore rich resources, which are well linked to the Internet through wireless LAN and are available for use by nearby mobile devices users [37]. Cloudlets use a Wi-Fi network to offload computational tasks, which helps save a considerable amount of energy from mobile devices compared to offloading through a cellular 3G/LTE network [38, 39]. The cloudlet mechanism extends the battery life of the mobile device, thus reducing network latency; on the other hand, it improves the quality of experience (QoE) of the end-user [40]. Therefore, deployment of the cloudlets will be similar to that of Wi-Fi hotspot configuration and will be close to the edge users.The above discussion reveals that MCC servers may be used for offloading of application which requires huge computational and storage resources. MEC servers may be used for the latency critical applications due to the presence in closer proximity with fewer computational and storage resources.


## 2.1.1. Mobile Computing

Mobile computing is the execution of data and applications in portable devices and mobile devices, while the transfer of data between two or more mobile devices is known as mobile communication. Software, information, applications, and another form of technological instructions are deployed within a small portable device, which is distributed widely and connected through various sorts of wireless connections. The distributed resources, which are centrally located within each device used, are connected through the use of mobile computing technology. The increasing popularity of mobile devices among people has increased expectations of quality and service level which they offer [25–27].


## 2.1.2. Cloud Computing

Cloud computing is the centralized computation of the computing services within a single environment, allocating the necessary portion of that environment as per service demand in one of three types of service: Software As a Service (SAAS), Platform As a Service (PAAS), or Infrastructure As a Service (IAAS) [28]. The services provided by the cloud are purely dependent upon what services have been demanded by the users. Service is determined by the type of device that shares resources and the offloaded functions and contents from user devices. This gave birth to the concept of mobile cloud computing. Mobile cloud computing (MCC) is the distributed computation of mobile applications, by offloading some of the computational functions to the cloud via a network, within the single environment of the cloud providing the resources as per each user’s need. This produces an opportunistic use of mobile edge computing (MEC) surrounding resources for the improvement of MCC functionality in the network issues since the edge is closed to the mobile devices and the cloud is so far away from the mobile devices [29].


## 2.1.3. Mobile Cloud Computing (MCC)

Mobile cloud computing (MCC) is an emerging and innovative technology utilizing the unified resources of different clouds thus exploiting the elastic nature of the cloud computation, providing unlimited ever present services to mobile devices regardless of the location of service demands, and accommodating client service level demands [30]. These services are mutually shared between cloud side and mobile devices side through the network. MCC provides for a wide range of mobile device users an environment where computation processing and storage of mobile device data are done in the cloud which has been allocated exclusively to the particular mobile device rather than within the device concerned, regardless of the kinds of mobile devices being used which provided the MCC services [31]. The driving force behind the development of MCC is to enable limitless computation in mobile devices while minimizing the challenges inherent in the current mobile computation technology.


## 2.1.4. Fog Computing

Fog computing is a remote computing paradigm that acts as an intermediate layer between the cloud and cloudlet, so that cloud-based services can be extended closer to ecosystems [32, 33]. Cloud data centers often fail to meet the storage and processing demands of billions of geodistributed IoT devices and sensors with the consequence of congested networks, high latency in service delivery, and poor Quality of Service (QoS). Edge computing backed by powerful computing resources can reduce the network latency and render the nearby cloudlet accessible by edge users through a one-hop high-speed wireless local area network. To reduce the delay during offloading, cloudlets will be the right offloading decision to get the task result fast with the minimum delay, and the cloudlet will be in the edge layer that is closest to the edge users. The fog computing will be in the middle layer between the edge layer (cloudlets) and the cloud layer (cloud) [34, 35].


## 2.1.5. Mobile Edge Computing (MEC)

Mobile edge computing (MEC) is an innovative architecture, which enables the functionalities of cloud computing at the edge of the mobile network. The main idea regarding MEC is to bring resources of cloud computing near the end-user and serve the request of the end-user locally. MEC helps the computation offloading process to get low latency during offloading tasks and reduces the traffic in the network as low requests are accelerated to the cloud server. The MEC architecture is proposed by ETSI where they presumed that cloud functionality, such as storage and computation, would be integrated with edge network devices such as small cell access points, macro base stations, radio network controllers, and macro base station [36]. The idea of Cloudlets was produced in 2009 as a trusted local rich computation resource or multicore rich resources, which are well linked to the Internet through wireless LAN and are available for use by nearby mobile devices users [37]. Cloudlets use a Wi-Fi network to offload computational tasks, which helps save a considerable amount of energy from mobile devices compared to offloading through a cellular 3G/LTE network [38, 39]. The cloudlet mechanism extends the battery life of the mobile device, thus reducing network latency; on the other hand, it improves the quality of experience (QoE) of the end-user [40]. Therefore, deployment of the cloudlets will be similar to that of Wi-Fi hotspot configuration and will be close to the edge users.The above discussion reveals that MCC servers may be used for offloading of application which requires huge computational and storage resources. MEC servers may be used for the latency critical applications due to the presence in closer proximity with fewer computational and storage resources.


## 2.2. Optimisation Scenarios

In the edge environments, allocating the best place to offload the tasks is a challenging task because multiple criteria must be taken into account, including limitation of resources and proximity of cloudlets [41]. Methods designed to solve this problem falls into the following categories: classical optimization, metaheuristic, game theory, and machine learning.


### 2.2.1. Classical Optimization

Classical approaches can produce better accuracy at the expense of high computational time-consuming. In case the problem is nonlinear, or has a huge size, classical approaches are stuck in local optima.


### 2.2.2. Metaheuristic Optimization

Consequently, researchers moved towards the use of metaheuristics as it provides a nearly optimal solution with a reasonable computation [42]. Recently, many solutions are advanced regarding the optimization of the offloading process in the edge networks. Offloading task to MCC/MEC platforms has been received lots of attention from the research community [43–48]. However, published studies have not considered the optimisation of execution latency subject to task precedence with task constraints and user mobility; this has also been observed in recent work [20, 49–52].Existing work on offloading optimization of the assignment oftasks to edge resources can be categorised according to the optimization objective as follows: (i) minimizing the response time (delay) in task execution [12, 53–57] and (ii) maximising the energy savings of user equipment [46, 58–63]. Some studies also considered both energy consumption and delay, opting to strike a balance [64–68]. A comparative summary of related works is shown in Table 2.Table 2
Comparative summary of related works.
FrameworkObjectiveTasks dependencyUser mobilityPlatformOptimisation algorithmRashidi and Sharifian [41]Delay & energyIndependentNo-mobilityMCCQDM & GA & ACO algorithmsYang et al. [69]DelayIndependentMobilityMECLocation-based offloading schemeYang et al. [70]Delay & energyDependentNo-mobilityMECASO and pro-ITG algorithmsMirjalili and Lewis [71]Delay & energyIndependentNo-mobilityMCCWhale optimization algorithm (WOA) algorithmYang et al. [72]Delay & energyIndependentNo-mobilityMECArtificial fish swarm (AFSA)Kao et al. [73]DelayDependentNo-mobilityMECFully polynomial time approximation (FPTAS)Wang et al. [53]DelayIndependentMobilityMECHeuristic algorithmZhang et al. [74]Delay & energyIndependentNo-mobilityMECWeight-sum functionPeng et al. [75]Delay & energyIndependentNo-mobilityMCCWhale optimization algorithm (WOA)Huang et al. [76]Delay & energyIndependentNo-mobilityMECAnt colony system (ACS)Yuyi et al. [66]Delay & energyIndependentNo-mobilityMECGame theoryBi et al. [77]Delay & energyIndependentNo-mobilityMECHybrid metaheuristic algorithmShu et al. [78]DelayDependentNo-mobilityMECHeuristic algorithmKai et al. [79]DelayIndependentNo-mobilityMECSuccessive convex approximation (SCA)Sun et al. [80]Delay& energyIndependentNo-mobilityMECLyapunov optimization algorithmBi et al. [81]Delay& energyIndependentNo-mobilityMECMixed integer nonlinear programming (MINLP)Shan et al. [82]Delay& energyIndependentNo-mobilityMECCov-AHP & Nash equilibrium algorithmErana Veerappa Dinesh and Valarmathi [83]Delay & energyIndependentNo-mobilityMCCEnergy estimation model (RG-EEM)Raj [84]Delay & energyIndependentNo-mobilityMCCAnt colony optimization (ACO) algorithmGu et al. [85]DelayIndependentNo-mobilityMCCParticle swarm optimization (MPSO) algorithmSundararaj [86]DelayIndependentNo-mobilityMCCQueue ant colony (QAnt-Bee) algorithmLiu et al. [87]Delay & energyIndependentNo-mobilityMECLyapunov optimization algorithmLiu et al. [88]DelayDependentNo-mobilityMECGenDoc algorithmHuy Hoang et al. [89]DelayIndependentMobilityMECHeuristic algorithmThananjeyan [90]Delay & energyIndependentMobilityMECComputational intensity (CI)


### 2.2.3. Game Theory

Game theory is one of the most important optimization approaches which is also called science of strategy. Using this approach, authors of [91] solved the resource allocation problem in computation offloading with a very low computational complexity. Another study solved the problem of resource scheduling mechanism for cooperative cloudlets to reduce the operator’s cost and preserve the user experience in MEC with a centralized controller [92].


### 2.2.4. Machine Learning

Machine learning including deep learning are widely recognized as efficient optimization approaches succeeding the traditional optimization schemes. These approaches make better decisions while offloading of tasks at the MEC or MCC servers. Besides, resource allocation including channel access, CPU cycles, time allocations, and other resources allocation problems may be effectively solved through these schemes [93].


## 2.2.1. Classical Optimization

Classical approaches can produce better accuracy at the expense of high computational time-consuming. In case the problem is nonlinear, or has a huge size, classical approaches are stuck in local optima.


## 2.2.2. Metaheuristic Optimization

Consequently, researchers moved towards the use of metaheuristics as it provides a nearly optimal solution with a reasonable computation [42]. Recently, many solutions are advanced regarding the optimization of the offloading process in the edge networks. Offloading task to MCC/MEC platforms has been received lots of attention from the research community [43–48]. However, published studies have not considered the optimisation of execution latency subject to task precedence with task constraints and user mobility; this has also been observed in recent work [20, 49–52].Existing work on offloading optimization of the assignment oftasks to edge resources can be categorised according to the optimization objective as follows: (i) minimizing the response time (delay) in task execution [12, 53–57] and (ii) maximising the energy savings of user equipment [46, 58–63]. Some studies also considered both energy consumption and delay, opting to strike a balance [64–68]. A comparative summary of related works is shown in Table 2.Table 2
Comparative summary of related works.
FrameworkObjectiveTasks dependencyUser mobilityPlatformOptimisation algorithmRashidi and Sharifian [41]Delay & energyIndependentNo-mobilityMCCQDM & GA & ACO algorithmsYang et al. [69]DelayIndependentMobilityMECLocation-based offloading schemeYang et al. [70]Delay & energyDependentNo-mobilityMECASO and pro-ITG algorithmsMirjalili and Lewis [71]Delay & energyIndependentNo-mobilityMCCWhale optimization algorithm (WOA) algorithmYang et al. [72]Delay & energyIndependentNo-mobilityMECArtificial fish swarm (AFSA)Kao et al. [73]DelayDependentNo-mobilityMECFully polynomial time approximation (FPTAS)Wang et al. [53]DelayIndependentMobilityMECHeuristic algorithmZhang et al. [74]Delay & energyIndependentNo-mobilityMECWeight-sum functionPeng et al. [75]Delay & energyIndependentNo-mobilityMCCWhale optimization algorithm (WOA)Huang et al. [76]Delay & energyIndependentNo-mobilityMECAnt colony system (ACS)Yuyi et al. [66]Delay & energyIndependentNo-mobilityMECGame theoryBi et al. [77]Delay & energyIndependentNo-mobilityMECHybrid metaheuristic algorithmShu et al. [78]DelayDependentNo-mobilityMECHeuristic algorithmKai et al. [79]DelayIndependentNo-mobilityMECSuccessive convex approximation (SCA)Sun et al. [80]Delay& energyIndependentNo-mobilityMECLyapunov optimization algorithmBi et al. [81]Delay& energyIndependentNo-mobilityMECMixed integer nonlinear programming (MINLP)Shan et al. [82]Delay& energyIndependentNo-mobilityMECCov-AHP & Nash equilibrium algorithmErana Veerappa Dinesh and Valarmathi [83]Delay & energyIndependentNo-mobilityMCCEnergy estimation model (RG-EEM)Raj [84]Delay & energyIndependentNo-mobilityMCCAnt colony optimization (ACO) algorithmGu et al. [85]DelayIndependentNo-mobilityMCCParticle swarm optimization (MPSO) algorithmSundararaj [86]DelayIndependentNo-mobilityMCCQueue ant colony (QAnt-Bee) algorithmLiu et al. [87]Delay & energyIndependentNo-mobilityMECLyapunov optimization algorithmLiu et al. [88]DelayDependentNo-mobilityMECGenDoc algorithmHuy Hoang et al. [89]DelayIndependentMobilityMECHeuristic algorithmThananjeyan [90]Delay & energyIndependentMobilityMECComputational intensity (CI)


## 2.2.3. Game Theory

Game theory is one of the most important optimization approaches which is also called science of strategy. Using this approach, authors of [91] solved the resource allocation problem in computation offloading with a very low computational complexity. Another study solved the problem of resource scheduling mechanism for cooperative cloudlets to reduce the operator’s cost and preserve the user experience in MEC with a centralized controller [92].


## 2.2.4. Machine Learning

Machine learning including deep learning are widely recognized as efficient optimization approaches succeeding the traditional optimization schemes. These approaches make better decisions while offloading of tasks at the MEC or MCC servers. Besides, resource allocation including channel access, CPU cycles, time allocations, and other resources allocation problems may be effectively solved through these schemes [93].


## 2.3. Offloading Environment

The offloading environment dynamics may be divided into the application modules and optimization objectives.


### 2.3.1. Application Modules

Frameworks in mobile cloud computing focused on the problems regarding offloading decision-making and application partitioning in offloading tasks from mobile devices to the main cloud without considering user mobility or changes that could happen in the network connection during offloading operations [18, 94, 95]. They have solved the offloading decision-making problem and the application partitioning problem using a mechanism that consists of three key elements: (a) Partitioner, (b) Profiler, and (c) Solver [96]. This mechanism helps to decide whether it is favorable to offload the task to the cloud side or just execute it locally on the mobile device [97]. The process of computation offloading in MCC is depicted in Figure 4.Figure 4
Process of computation offloading in mobile cloud computing.(1) Partitioner. The partitioner is used to annotate which portion of the application is considered an offloadable task. An annotated partition is achieved through results from application analysis made on codes of computation. It is determined based on whether the codes are accessing native resources of the mobile environment, or not. The mobile environment comprises native resources that include access to I/O interfaces, GPS, Camera, native services that include the particular mobile environment, or any other hardware embedded in mobile devices [97].(2) Profiler. Profiler is used to monitor offloading parameters that will help the framework solver to make the final decision whether to offload the task or not. Therefore, the profiler will be an important factor in making the final decision in the solver part. Profiler can monitor decision parameters such as CPUs or energy power. Some frameworks used monitor software such as ThinkAir framework, which uses Power Tutor software to track various programme-related parameters. It extracts overall the execution time for a particular method, CPU cycles, and memory allocation of a particular thread, method call numbers, and executed instruction numbers [95]. Other some frameworks used monitor device such as CloneCloud framework utilises Monsoon Power device to monitor three system variables: CPU activity (active and idle state), Screen (on/off state), and Network interface during active state (transferring/receiving) or idle state. They translate these variables into power draw via function P from (CPU, Scr, Net) triples to an energy value. This generates two cost model, once when CPU ON locally on mobile device, and when the CPU idle during offloading process [94]. The cost of power consumption can be estimated as(1)Cci,0≡PCPUON,ScrON,NetIdle×Ti.The second cost model of power consumption with clone in server with screen ON and idle CPU in mobile device to calculate only energy consumption with network active during migration operation is as follows:(2)Cci,1≡PCPUIdle,ScrON,NetIdle.(3) Solver. The solver of the computation offloading frameworks in MCC is the part that makes the feasible offloading decision based on the available partitions and decision metric developed by using parameters from profiler or directly utilizing profiler parameters to optimise solution of the decision. The solver can be categorised according to its location, whether it is located on the mobile device, in the remote cloud/server, or in both [97]. In this work, the solver is used for assigning a value to decision variable based on minimization of expected cost of a particular partitioned application. It is used to make a final decision about the offloading by the framework and is located in the user of the mobile device [94]. Another work uses a linear program solver in both side of mobile device and cloud as shown in Figure 5 to solve a global optimization problem developed by using input such as annotation and graphs from the annotated call graph model developed for partitioning model of the framework. The energy used during local execution, remote execution, and time spent for local and remote execution are taken as decision-making metrics for the solver as shown in Figure 6.Figure 5
Tasks with dependencies.Figure 6
Solver in mobile device side and server side of MAUI framework.The [98] application [98] call graph G = (V, E) is used by the solver to develop the optimization problem as a call stack to execute the programme, where (V) represents the sets of vertex as a method of the call stack, and each vertex v belongs to the set (V). The edges are represented as e = (u, v), indicating the invocation from method (u) to method (v). Energy and time taken to execute a vertex (v) as a method locally on mobile device is represented by Evl, and Tvl, respectively, and time taken to execute the method remotely in the cloud is represented as Tvr. For edge e = (u, v), the time and energy cost taken to transfer the necessary states of the program during a call from (u) to (v) is annotated as Bu,v and Cu,v, respectively. Each method is also annotated with parameter rv to indicate if it is able to migrate to server side or not. The MAUI solver uses the indicator variable Iv, and when it is (0) it shows that method (v) is executed locally on a mobile device. Otherwise, (1) value indicates it is executed remotely in the server side. (L) is a default latency constraint: if all methods (v) are executed on the mobile device, the total execution of L must not exceed 5% more than the local latency on the mobile device. MAUI solver solves the 0-1 Integer Linear programming ILP problem as follows:(3)Maximize∑v∈VIv×Evl−∑u,v∈EIv−Iv×Cu,v,s.t.∑v∈V1−Iv×Tvl+Iv×Tvr,+∑u,v∈EIv−Iv×Bu,v<=L,Iv<=rv,∀v∈V.Other intensive frameworks in MCC have used the same mechanism of binary variable decision without considering network fluctuations [18, 94–96, 98]. Mobile edge computing was produced to overcome the network fluctuations that will happen after offloading decision is made [52].


### 2.3.2. Optimization Objectives

Recent works in the mobile edge computing dealing with the dynamic computation offloading are focusing on the task offloading optimization problem in the edge. The objectives of computation offloading may be categorised into three categories: (a) reduction of delays (minimising latency in tasks offloading in the edge, (b) saving energy, and (c) reducing ISP cost.(1) Minimising Latency. Through dynamic computation offloading of compute intensive tasks MCC server, the quality of computation experience may be greatly improved in the context of execution latency. However, transmission latency may degrade the performance in MCC. To this end, the MEC plays a crucial role by placing computing nodes at the edge of the network. This scenario effectively reduces transmission latency while preserving execution time [99].(2) Saving Energy. Dynamic computation offloading effectively saves energy by allowing timely offloading decisions based on different parameters, including the computational power of the end nodes, the computation node, and the channel conditions. To further enhance the battery lives of edge nodes, dynamic computation offloading allows energy harvesting approaches to work together with offloading [22].(3) Reducing ISP Cost. The services are migrated nearer to the user in MEC, cloudlets, and fog computing. As a result, the user requests do not travel through multiple ISPs reducing network congestion and ISP cost to service user requests [99].


## 2.3.1. Application Modules

Frameworks in mobile cloud computing focused on the problems regarding offloading decision-making and application partitioning in offloading tasks from mobile devices to the main cloud without considering user mobility or changes that could happen in the network connection during offloading operations [18, 94, 95]. They have solved the offloading decision-making problem and the application partitioning problem using a mechanism that consists of three key elements: (a) Partitioner, (b) Profiler, and (c) Solver [96]. This mechanism helps to decide whether it is favorable to offload the task to the cloud side or just execute it locally on the mobile device [97]. The process of computation offloading in MCC is depicted in Figure 4.Figure 4
Process of computation offloading in mobile cloud computing.(1) Partitioner. The partitioner is used to annotate which portion of the application is considered an offloadable task. An annotated partition is achieved through results from application analysis made on codes of computation. It is determined based on whether the codes are accessing native resources of the mobile environment, or not. The mobile environment comprises native resources that include access to I/O interfaces, GPS, Camera, native services that include the particular mobile environment, or any other hardware embedded in mobile devices [97].(2) Profiler. Profiler is used to monitor offloading parameters that will help the framework solver to make the final decision whether to offload the task or not. Therefore, the profiler will be an important factor in making the final decision in the solver part. Profiler can monitor decision parameters such as CPUs or energy power. Some frameworks used monitor software such as ThinkAir framework, which uses Power Tutor software to track various programme-related parameters. It extracts overall the execution time for a particular method, CPU cycles, and memory allocation of a particular thread, method call numbers, and executed instruction numbers [95]. Other some frameworks used monitor device such as CloneCloud framework utilises Monsoon Power device to monitor three system variables: CPU activity (active and idle state), Screen (on/off state), and Network interface during active state (transferring/receiving) or idle state. They translate these variables into power draw via function P from (CPU, Scr, Net) triples to an energy value. This generates two cost model, once when CPU ON locally on mobile device, and when the CPU idle during offloading process [94]. The cost of power consumption can be estimated as(1)Cci,0≡PCPUON,ScrON,NetIdle×Ti.The second cost model of power consumption with clone in server with screen ON and idle CPU in mobile device to calculate only energy consumption with network active during migration operation is as follows:(2)Cci,1≡PCPUIdle,ScrON,NetIdle.(3) Solver. The solver of the computation offloading frameworks in MCC is the part that makes the feasible offloading decision based on the available partitions and decision metric developed by using parameters from profiler or directly utilizing profiler parameters to optimise solution of the decision. The solver can be categorised according to its location, whether it is located on the mobile device, in the remote cloud/server, or in both [97]. In this work, the solver is used for assigning a value to decision variable based on minimization of expected cost of a particular partitioned application. It is used to make a final decision about the offloading by the framework and is located in the user of the mobile device [94]. Another work uses a linear program solver in both side of mobile device and cloud as shown in Figure 5 to solve a global optimization problem developed by using input such as annotation and graphs from the annotated call graph model developed for partitioning model of the framework. The energy used during local execution, remote execution, and time spent for local and remote execution are taken as decision-making metrics for the solver as shown in Figure 6.Figure 5
Tasks with dependencies.Figure 6
Solver in mobile device side and server side of MAUI framework.The [98] application [98] call graph G = (V, E) is used by the solver to develop the optimization problem as a call stack to execute the programme, where (V) represents the sets of vertex as a method of the call stack, and each vertex v belongs to the set (V). The edges are represented as e = (u, v), indicating the invocation from method (u) to method (v). Energy and time taken to execute a vertex (v) as a method locally on mobile device is represented by Evl, and Tvl, respectively, and time taken to execute the method remotely in the cloud is represented as Tvr. For edge e = (u, v), the time and energy cost taken to transfer the necessary states of the program during a call from (u) to (v) is annotated as Bu,v and Cu,v, respectively. Each method is also annotated with parameter rv to indicate if it is able to migrate to server side or not. The MAUI solver uses the indicator variable Iv, and when it is (0) it shows that method (v) is executed locally on a mobile device. Otherwise, (1) value indicates it is executed remotely in the server side. (L) is a default latency constraint: if all methods (v) are executed on the mobile device, the total execution of L must not exceed 5% more than the local latency on the mobile device. MAUI solver solves the 0-1 Integer Linear programming ILP problem as follows:(3)Maximize∑v∈VIv×Evl−∑u,v∈EIv−Iv×Cu,v,s.t.∑v∈V1−Iv×Tvl+Iv×Tvr,+∑u,v∈EIv−Iv×Bu,v<=L,Iv<=rv,∀v∈V.Other intensive frameworks in MCC have used the same mechanism of binary variable decision without considering network fluctuations [18, 94–96, 98]. Mobile edge computing was produced to overcome the network fluctuations that will happen after offloading decision is made [52].


## 2.3.2. Optimization Objectives

Recent works in the mobile edge computing dealing with the dynamic computation offloading are focusing on the task offloading optimization problem in the edge. The objectives of computation offloading may be categorised into three categories: (a) reduction of delays (minimising latency in tasks offloading in the edge, (b) saving energy, and (c) reducing ISP cost.(1) Minimising Latency. Through dynamic computation offloading of compute intensive tasks MCC server, the quality of computation experience may be greatly improved in the context of execution latency. However, transmission latency may degrade the performance in MCC. To this end, the MEC plays a crucial role by placing computing nodes at the edge of the network. This scenario effectively reduces transmission latency while preserving execution time [99].(2) Saving Energy. Dynamic computation offloading effectively saves energy by allowing timely offloading decisions based on different parameters, including the computational power of the end nodes, the computation node, and the channel conditions. To further enhance the battery lives of edge nodes, dynamic computation offloading allows energy harvesting approaches to work together with offloading [22].(3) Reducing ISP Cost. The services are migrated nearer to the user in MEC, cloudlets, and fog computing. As a result, the user requests do not travel through multiple ISPs reducing network congestion and ISP cost to service user requests [99].


## 2.4. Granularities

Offloading granularities enable different levels of tasks to be offloaded in the cloud or on the edge server. These granularities can be classified into two categories, including fine grain and coarse grain.


### 2.4.1. Fine Grain

First defines a fine-grained mobile code offloading structure [100, 101], which is also known as partial offloading scheme. This approach relies on developers to annotate the offloading parts, within an application, and the main aim of this approach is to improve the efficiency of energy utilization in mobile devices. This aim is achieved by offloading annotated parts such as methods or threads to gain energy utilization efficiency. Fine-grain granularity is a useful offloading type for applications that have tasks that use the hardware of the mobile device and that cannot be offloaded outside the mobile devices, such as using the speaker or screen of the mobile device.


### 2.4.2. Coarse Grain

Coarse-grained offloading is the second granularity of this approach. In this approach, full application/program, or a process, or a whole virtual machine is offloaded to the remote computing resources and it is called full offloading approach [25, 102].


## 2.4.1. Fine Grain

First defines a fine-grained mobile code offloading structure [100, 101], which is also known as partial offloading scheme. This approach relies on developers to annotate the offloading parts, within an application, and the main aim of this approach is to improve the efficiency of energy utilization in mobile devices. This aim is achieved by offloading annotated parts such as methods or threads to gain energy utilization efficiency. Fine-grain granularity is a useful offloading type for applications that have tasks that use the hardware of the mobile device and that cannot be offloaded outside the mobile devices, such as using the speaker or screen of the mobile device.


## 2.4.2. Coarse Grain

Coarse-grained offloading is the second granularity of this approach. In this approach, full application/program, or a process, or a whole virtual machine is offloaded to the remote computing resources and it is called full offloading approach [25, 102].


## 2.5. Offloading Applications

There are multiple applications of offloading exists in MEC and MCC. These applications are categorised into local computation, real-time applications, resource hungry applications, periodic offloading, and nonperiodic offloading. In the following sections, we discuss each category.


### 2.5.1. Local Computation

The offload of resource-critical tasks to the computing server effectively maximises the computation rate in terms of bits. This mechanism also minimises the overall computation rate in terms of computing bits. However, some applications require local computation because of their extreme sensitivity in the context of latency. These applications prefer to be executed at the end nodes [103].


### 2.5.2. Real-Time Applications

Although some applications are time sensitive, these applications may still be offloaded at the end server for effective computation while guaranteeing the scheduling of tasks. However, it is necessary to schedule these tasks in an effective timely manner to minimise the latency that may affect these applications.


### 2.5.3. Resource Hungry Applications

Resource hungry applications, including augmented reality, virtual reality, and multimedia applications, require huge computational and storage resources. These applications may be effectively executed at the mobile cloud servers with required resource. However, latency constraints in these cloud-based systems may degrade the performance. MEC significantly plays a crucial role to enhance the performance of resource hungry applications by minimizing the latency. In this mechanism, less resource of edge servers may be improved with some resource management techniques [104].


### 2.5.4. Periodic/Nonperiodic Offloading

Some applications require periodic computation of data. These applications may be for traffic monitoring and surveillance. Due to continuous monitoring, these applications offload their computational tasks at the end server to save the battery lives of sensor nodes which involve in periodic monitoring of environment. Offloading may also be nonperiodic, in which an end device offloads its compute intensive task when it requires. These applications do not need periodic computation of tasks [105, 106].


## 2.5.1. Local Computation

The offload of resource-critical tasks to the computing server effectively maximises the computation rate in terms of bits. This mechanism also minimises the overall computation rate in terms of computing bits. However, some applications require local computation because of their extreme sensitivity in the context of latency. These applications prefer to be executed at the end nodes [103].


## 2.5.2. Real-Time Applications

Although some applications are time sensitive, these applications may still be offloaded at the end server for effective computation while guaranteeing the scheduling of tasks. However, it is necessary to schedule these tasks in an effective timely manner to minimise the latency that may affect these applications.


## 2.5.3. Resource Hungry Applications

Resource hungry applications, including augmented reality, virtual reality, and multimedia applications, require huge computational and storage resources. These applications may be effectively executed at the mobile cloud servers with required resource. However, latency constraints in these cloud-based systems may degrade the performance. MEC significantly plays a crucial role to enhance the performance of resource hungry applications by minimizing the latency. In this mechanism, less resource of edge servers may be improved with some resource management techniques [104].


## 2.5.4. Periodic/Nonperiodic Offloading

Some applications require periodic computation of data. These applications may be for traffic monitoring and surveillance. Due to continuous monitoring, these applications offload their computational tasks at the end server to save the battery lives of sensor nodes which involve in periodic monitoring of environment. Offloading may also be nonperiodic, in which an end device offloads its compute intensive task when it requires. These applications do not need periodic computation of tasks [105, 106].


## 2.6. Offloading Network

Network state largely impacts the computational offloading while migrating tasks to computational servers. These states should be considered while migration of compute intensive tasks. We consider the following networks in this study.


### 2.6.1. Wireless Sensor Networks

Wireless sensor networks (WSNs) are dynamic networks with both mobile and stationary sensors placed to monitor physical conditions of an area. Due to ad hoc nature of WSN, the sensor nodes often require the amalgamation with MEC and MCC to complete computational tasks. Edge servers can be placed near a WSN that needs to offload data and computation periodically [20].


### 2.6.2. Cellular Network

The users of cellular networks utilize different social media applications of watch video content hosted on distant cloud data centers. The hosting of the content in CDNs and edge networks facilitates the ISP to offload traffic from core network while servicing user requests from the access network [107, 108]. The traffic offload from core network lowers the ISP costs and reduces the access latency.


### 2.6.3. IoT

Internet of Things (IoT) is a set of computing devices connected with the network for accessibility. Sensors and embedded devices with communication interfaces provide automation over the network with easy to use software interfaces. The IoTs are often resource-constrained necessitating merger with MEC and MCC.


### 2.6.4. Vehicular Networks

Vehicles moving at high-speed consist of infotainment and navigation applications with users often resorting to mobile devices for data services. MEC, fog, and cloudlets services residing in road side units (RSU) can be employed for offloading data and compute services from vehicular networks [109].


## 2.6.1. Wireless Sensor Networks

Wireless sensor networks (WSNs) are dynamic networks with both mobile and stationary sensors placed to monitor physical conditions of an area. Due to ad hoc nature of WSN, the sensor nodes often require the amalgamation with MEC and MCC to complete computational tasks. Edge servers can be placed near a WSN that needs to offload data and computation periodically [20].


## 2.6.2. Cellular Network

The users of cellular networks utilize different social media applications of watch video content hosted on distant cloud data centers. The hosting of the content in CDNs and edge networks facilitates the ISP to offload traffic from core network while servicing user requests from the access network [107, 108]. The traffic offload from core network lowers the ISP costs and reduces the access latency.


## 2.6.3. IoT

Internet of Things (IoT) is a set of computing devices connected with the network for accessibility. Sensors and embedded devices with communication interfaces provide automation over the network with easy to use software interfaces. The IoTs are often resource-constrained necessitating merger with MEC and MCC.


## 2.6.4. Vehicular Networks

Vehicles moving at high-speed consist of infotainment and navigation applications with users often resorting to mobile devices for data services. MEC, fog, and cloudlets services residing in road side units (RSU) can be employed for offloading data and compute services from vehicular networks [109].


## 3. State of the Art

In this section, we present the state-of-the-art studies in detail. We divide this literature review into (I) computation offloading in MCC and (II) computation offloading in MEC. The purpose of this division is to highlight the difference between the offload mechanism and the characteristics between MCC and MEC.


### 3.1. Computation Offloading in MCC

The authors in [41] proposed a novel framework that involved the queue-based algorithm and hybrid heuristic in optimizing the task assignment process in MCC [41]. The architecture of the framework was divided into two main stages. In the first stage, a queue model is used to represent the clouds and cloudlets in queue structure to reduce the drop rate of the user’s tasks. In this stage, the queue-based decision marker (QDM) unit is utilised to estimate the probability of appointing each task to a cloudlet or public cloud. This is done to minimise the mean response time. The inputs of this unit are the capacity of cloudlets/cloud, all the user requests, and the initial queue. The functionality of this unit is dependent on the model-driven from the queue theory. The QDM output and the duration of communication between each user and cloudlets/cloud are the input of the subsequent stage. In second stage, two-nature inspired algorithms including genetic algorithm (GA) and ant colony optimization (ACO) are hybridized to empower the searching process in finding near-optimal task assignment that considers the duration of communication between each user and cloudlets/cloud with the eventual desired outcome being the minimizing the consumption time of offloadable tasks and power consumption in the mobile battery.In [110], computation offloading in MCC is formulated as an optimization problem. Grey wolf optimizer (GWO) [111] is an optimization algorithm inspired by hunting behavior and leadership hierarchy of GWO in nature. In this paper, the researchers proposed an adaptation version of GWO to find the best solution for the computation offloading for the MCC workflow. In practise, GWO iteratively generated candidate solutions that attempt to minimise the task execution time in workflow and energy consumption in mobile devices. Focusing specifically on a mobile cloud environment, researchers exerted tremendous efforts to gain high-quality assurance and optimal utilization of resources for mobile devices.Authors in [75] proposed a joint optimization approach based on dynamic voltage and frequency scaling technique and whale optimization algorithm (WOA) [71], to optimise task completing time and energy consumption of mobile devices. In estimating these two optimization objectives, several factors are considered, which are the position of execution of the task, the sequence of execution of the task, and the operating voltage and frequency. Moreover, the fitness function utilised in WOA is multiobjective, where weight scores are assigned for both task completion time and energy consumption. The experimental results proved that the joint optimization approach is a promising and effective approach capable of providing adequate solutions for running the mobile cloud system in a seamless manner with respect to saving energy and parallel task scheduling.In a recent article [86], an efficient hybridisation model based on the queue ant colony optimization and the artificial bee colony optimization Algorithm, called (QAnt-Bee), was proposed as a means of assigning offloaded tasks to the most accurate cloudlets in the MCC environment by optimizing the processing delay of tasks and energy consumption, and the rejected rate of offloaded tasks. The resource allocation is considered a complete NP-hard problem.Gu et al. [85] proposed an improved version of particle swarm optimization (MPSO) to more effectively optimise the allocation of resources from task offloading plans in a shorter time. In MPSO, a task movement strategy that allows the movement of task position in current cloudlet to another one. In the context of optimization, this strategy allows the solution to exchange their variables in order to increase the exploration rate and thus avoid becoming stuck in local optima. The experimental results showed that the MPSO algorithm could produce better and more effective solutions compared to PSO.


### 3.2. Computation Offloading in MEC

In mobile edge computing (MEC), several algorithms have been applied to solve the problem of offloading tasks along with the allocation of transmit power. The article [72] studied the problem of computation offloading for MEC in 5G systems. In particular, this paper focused on improving the energy consumption of system entities offloading the required tasks. The problem was formulated as an optimization problem where the energy consumption is to be minimized, taking into account the delay requirements. In the formulation model, both task transmission (fronthaul and backhaul) and task computation at the MEC server were considered. To solve this problem, the authors proposed using an artificial fish swarm algorithm (AFSA). This heuristic algorithm provides global convergence, obtaining the global optimization solution for the problem under consideration. The efficiency of the proposed algorithm was evaluated and compared with other related algorithms.This paper [53] studied the problem of task assignment in MEN for multitasking and multiuser situations. In particular, this paper considered minimizing the delay in the execution of the task in MEN. The problem was formulated as an optimization problem in which task properties, user mobility, and network constraints were considered as a constraint satisfaction problem. Then, the authors proposed a heuristic algorithm to solve this problem. The proposed algorithm proceeds as follows. First, users send a message, which includes general information about their tasks, to the central MEN controller. Particularly, this message contains the data size, execution load, local execution time, and the likely output data size. The central controller then allocates each task to an sBS where the delay is the shortest. An sBS, which needs to execute two or more tasks, performs the task with the minimal execution time. Furthermore, the central controller re-allocates those tasks which are not under execution. The process continues until each task is allocated to the optimal sBS. If the local execution time remains shorter than that of the optimal sBS, the task is executed locally at the user end. It should be noted that the proposed algorithm considers user mobility prediction during the allocation process. A set of simulation experiments were conducted to evaluate the performance of the proposed algorithm, and the results showed that the task execution delay is significantly reduced when user mobility is considered.This paper [74] studies the problem of task offload along with transmit power allocation in MEC systems. It found that both execution latency and energy consumption were considered to be reduced so that overall performance is enhanced. The problem was formulated as an optimization problem aiming at minimizing the weighted sum of execution delay and energy consumption. This paper first used the flow shop scheduling to achieve the optimal task offloading for a given transmit power. Furthermore, it employed convex optimization to determine the optimal transmit power for a given task offloading decision. The results showed that delay performance improves when both radio and computational resources are relatively balanced. In addition, the proposed algorithm significantly reduces energy consumption while offering near-optimal delay performance.This paper [76] studies the problem of task offloading and resource allocation in MEC. The problem was formulated as a bilevel optimization problem in which the offloading decision was considered as the upper-level optimization problem, whereas the resource allocation was considered as the lower-level optimization problem. Furthermore, the objective of the upper-level problem is to minimise the energy consumption of all users, and the objective of the lower-level problem is to minimise the total computations of all users. This bilevel problem, then, was solved using a bilevel optimization approach. In particular, ACS (ant colony system is a probabilistic technique for solving computational problems which can be reduced by finding good paths through graphs) is used first to generate offloading decisions for the upper-level optimization problem. If these decisions are considered feasible, then the monotonic optimization method is employed to calculate the optimal allocations of resources. The performance of the obtained joint solution is evaluated. This process continues until the best combinations have been achieved. The simulation results showed that the probabilistic technique provides efficient solutions for two sets of instances with about 400 mobile users.Researchers [66] consider the problem of task offloading along with resource allocation in MEC systems. The aim is to minimise both the energy consumption and the monetary cost for mobile users. The problem was considered from game theory perspectives. Therefore, the authors proposed a game model that includes a cloud and wireless resource allocation algorithm. The simulation results showed that the proposed algorithm minimises the cost with low complexity. Furthermore, compared to existing algorithms, the larger the size of the task data, the less energy consumption and the completion time is. There are other studies that employ the same classification of the optimization objective, such as those that work to minimise the delay [54], those that work to maximise energy savings [59–62], and those that work with both objectives [65–67, 112].Studies in minimizing execution delays in tasks of MEC like this work of reducing the delay in execution task for a single user, which uses the single-dimensioned search algorithm. The result of this algorithm is a policy in making an offloading decision based on the queue state of the application buffer. Alongside with this property of wireless media was considered as well [54].Another study considers the variety in spatial position of Latency while offloading. The sBs chosen by the users are responsible for the execution of tasks offloaded, but the results obtained in the user devices are sent through another sBs having the highest RSSI of the wireless connections. Although the consideration of spatial diversity is notable, the work is done considering offloading of a single task only [55].On the other hand, user mobility affects the scheduling on the edge so this work proposed a framework to reduce the task execution scheduling in mobile edge network during user mobility. They have considered the information of user mobility and the information of tasks and sBSs resources and have used lightweight heuristics solution to get fast scheduling during task offloading on sBSs with different users equations [53]. The main objective of the task scheduling in this framework is to maximise the using of MEC to reduce the delay time with all users during offloading tasks to the sBS. They have considered a set of users as U within which each user (i) has own computation task (j) that will be assigned to a set of base station as sBS. In the route of user mobility, there is a sequence of sBS in the user path Pi and (k) belongs to one of the paths in Pi that contains a set of sBS. Each task of Ti,j should be executed in the edge once time only along the user trajectory Pi. The problem modeled as an optimization problem is as follows:(4)maxDe1U∑i∈U∑j∈Tidi,jti,jl−ti,jedge,s.t.∀ti,j,∑k∈Pidi,j,ke<=1.In studies regarding optimization of maximising energy saving, this study demonstrated a framework to reduce energy consumption from mobile devices by optimizing the transfer time and the size of the data loaded to the edge network AP during the offloading process [113]. Another work considers the dynamicity in the state of the channel in transmitting tasks through wireless means and presents a scheme for tasks scheduling and offloading them. The scheme is designed in such a way that it can properly use wireless connections and user buffers, so that the energy consumption in task execution is reduced [59].In another work, a framework is demonstrated to offload computation, in mobile edge computation, for multiple devices, and a design is constructed in order to minimise the energy consumption in these devices [60]. Another study has advanced the work on this topic by considering the possible occurrence of collisions and interference due to multiple users trying in accessing single sBs, which can incur high-energy consumption in the user devices. In a way, the offloading was modeled in the game theory with multiple users and shown that this is always compatible with Nash equilibrium [61]. This work minimized mobile device energy consumption by centralising the framework for the multiuser MEC system. They have used orthogonal frequency division multiple access (OFDMA) and time division multiple access (TDMA) with the purpose of reducing the energy consumption of mobile devices [62]. Finally, another work regarding optimization of energy consumption used a framework to harvest mobile device energy from a base station or able to offload tasks to sBSs for the same purpose of saving energy [46].In this study, the offloading scheduling task has been optimized along with optimizing the allocation of the power that is transmitted in the MEC systems for several numbers of independent tasks. To reduce the delayed weighted sum in computation along with the consumption of energy, there has been proposed an algorithm, namely, low complexity suboptimal algorithm. It has been illustrated in this work that the implementation of this algorithm has reached minimum latency in execution with significant energy saving in a device. To find out the optimal tradeoff between complexity and delay, a lightweight approximation is used [66]. This work shows the use of a sequential game model with multiple stages to realise the concurrent requirements regarding energy and delay at the same time [67].Some other studies perform a combined optimization of energy consumption and execution delays in the tasks. Through these, it is seen that minimization in the task delays, most of which can be executed faster in mobile devices than in the edge network, contributes to the high power consumption in MEC. Some of these works designate a level limit of energy consumption and minimise the delays in the tasks without crossing the set limit. For example, this work presents a flexible offload scheme, considering the single user, to decrease the execution delay in energy harvesting devices, where these devices increase the complexity of offload algorithms [65].


## 3.1. Computation Offloading in MCC

The authors in [41] proposed a novel framework that involved the queue-based algorithm and hybrid heuristic in optimizing the task assignment process in MCC [41]. The architecture of the framework was divided into two main stages. In the first stage, a queue model is used to represent the clouds and cloudlets in queue structure to reduce the drop rate of the user’s tasks. In this stage, the queue-based decision marker (QDM) unit is utilised to estimate the probability of appointing each task to a cloudlet or public cloud. This is done to minimise the mean response time. The inputs of this unit are the capacity of cloudlets/cloud, all the user requests, and the initial queue. The functionality of this unit is dependent on the model-driven from the queue theory. The QDM output and the duration of communication between each user and cloudlets/cloud are the input of the subsequent stage. In second stage, two-nature inspired algorithms including genetic algorithm (GA) and ant colony optimization (ACO) are hybridized to empower the searching process in finding near-optimal task assignment that considers the duration of communication between each user and cloudlets/cloud with the eventual desired outcome being the minimizing the consumption time of offloadable tasks and power consumption in the mobile battery.In [110], computation offloading in MCC is formulated as an optimization problem. Grey wolf optimizer (GWO) [111] is an optimization algorithm inspired by hunting behavior and leadership hierarchy of GWO in nature. In this paper, the researchers proposed an adaptation version of GWO to find the best solution for the computation offloading for the MCC workflow. In practise, GWO iteratively generated candidate solutions that attempt to minimise the task execution time in workflow and energy consumption in mobile devices. Focusing specifically on a mobile cloud environment, researchers exerted tremendous efforts to gain high-quality assurance and optimal utilization of resources for mobile devices.Authors in [75] proposed a joint optimization approach based on dynamic voltage and frequency scaling technique and whale optimization algorithm (WOA) [71], to optimise task completing time and energy consumption of mobile devices. In estimating these two optimization objectives, several factors are considered, which are the position of execution of the task, the sequence of execution of the task, and the operating voltage and frequency. Moreover, the fitness function utilised in WOA is multiobjective, where weight scores are assigned for both task completion time and energy consumption. The experimental results proved that the joint optimization approach is a promising and effective approach capable of providing adequate solutions for running the mobile cloud system in a seamless manner with respect to saving energy and parallel task scheduling.In a recent article [86], an efficient hybridisation model based on the queue ant colony optimization and the artificial bee colony optimization Algorithm, called (QAnt-Bee), was proposed as a means of assigning offloaded tasks to the most accurate cloudlets in the MCC environment by optimizing the processing delay of tasks and energy consumption, and the rejected rate of offloaded tasks. The resource allocation is considered a complete NP-hard problem.Gu et al. [85] proposed an improved version of particle swarm optimization (MPSO) to more effectively optimise the allocation of resources from task offloading plans in a shorter time. In MPSO, a task movement strategy that allows the movement of task position in current cloudlet to another one. In the context of optimization, this strategy allows the solution to exchange their variables in order to increase the exploration rate and thus avoid becoming stuck in local optima. The experimental results showed that the MPSO algorithm could produce better and more effective solutions compared to PSO.


## 3.2. Computation Offloading in MEC

In mobile edge computing (MEC), several algorithms have been applied to solve the problem of offloading tasks along with the allocation of transmit power. The article [72] studied the problem of computation offloading for MEC in 5G systems. In particular, this paper focused on improving the energy consumption of system entities offloading the required tasks. The problem was formulated as an optimization problem where the energy consumption is to be minimized, taking into account the delay requirements. In the formulation model, both task transmission (fronthaul and backhaul) and task computation at the MEC server were considered. To solve this problem, the authors proposed using an artificial fish swarm algorithm (AFSA). This heuristic algorithm provides global convergence, obtaining the global optimization solution for the problem under consideration. The efficiency of the proposed algorithm was evaluated and compared with other related algorithms.This paper [53] studied the problem of task assignment in MEN for multitasking and multiuser situations. In particular, this paper considered minimizing the delay in the execution of the task in MEN. The problem was formulated as an optimization problem in which task properties, user mobility, and network constraints were considered as a constraint satisfaction problem. Then, the authors proposed a heuristic algorithm to solve this problem. The proposed algorithm proceeds as follows. First, users send a message, which includes general information about their tasks, to the central MEN controller. Particularly, this message contains the data size, execution load, local execution time, and the likely output data size. The central controller then allocates each task to an sBS where the delay is the shortest. An sBS, which needs to execute two or more tasks, performs the task with the minimal execution time. Furthermore, the central controller re-allocates those tasks which are not under execution. The process continues until each task is allocated to the optimal sBS. If the local execution time remains shorter than that of the optimal sBS, the task is executed locally at the user end. It should be noted that the proposed algorithm considers user mobility prediction during the allocation process. A set of simulation experiments were conducted to evaluate the performance of the proposed algorithm, and the results showed that the task execution delay is significantly reduced when user mobility is considered.This paper [74] studies the problem of task offload along with transmit power allocation in MEC systems. It found that both execution latency and energy consumption were considered to be reduced so that overall performance is enhanced. The problem was formulated as an optimization problem aiming at minimizing the weighted sum of execution delay and energy consumption. This paper first used the flow shop scheduling to achieve the optimal task offloading for a given transmit power. Furthermore, it employed convex optimization to determine the optimal transmit power for a given task offloading decision. The results showed that delay performance improves when both radio and computational resources are relatively balanced. In addition, the proposed algorithm significantly reduces energy consumption while offering near-optimal delay performance.This paper [76] studies the problem of task offloading and resource allocation in MEC. The problem was formulated as a bilevel optimization problem in which the offloading decision was considered as the upper-level optimization problem, whereas the resource allocation was considered as the lower-level optimization problem. Furthermore, the objective of the upper-level problem is to minimise the energy consumption of all users, and the objective of the lower-level problem is to minimise the total computations of all users. This bilevel problem, then, was solved using a bilevel optimization approach. In particular, ACS (ant colony system is a probabilistic technique for solving computational problems which can be reduced by finding good paths through graphs) is used first to generate offloading decisions for the upper-level optimization problem. If these decisions are considered feasible, then the monotonic optimization method is employed to calculate the optimal allocations of resources. The performance of the obtained joint solution is evaluated. This process continues until the best combinations have been achieved. The simulation results showed that the probabilistic technique provides efficient solutions for two sets of instances with about 400 mobile users.Researchers [66] consider the problem of task offloading along with resource allocation in MEC systems. The aim is to minimise both the energy consumption and the monetary cost for mobile users. The problem was considered from game theory perspectives. Therefore, the authors proposed a game model that includes a cloud and wireless resource allocation algorithm. The simulation results showed that the proposed algorithm minimises the cost with low complexity. Furthermore, compared to existing algorithms, the larger the size of the task data, the less energy consumption and the completion time is. There are other studies that employ the same classification of the optimization objective, such as those that work to minimise the delay [54], those that work to maximise energy savings [59–62], and those that work with both objectives [65–67, 112].Studies in minimizing execution delays in tasks of MEC like this work of reducing the delay in execution task for a single user, which uses the single-dimensioned search algorithm. The result of this algorithm is a policy in making an offloading decision based on the queue state of the application buffer. Alongside with this property of wireless media was considered as well [54].Another study considers the variety in spatial position of Latency while offloading. The sBs chosen by the users are responsible for the execution of tasks offloaded, but the results obtained in the user devices are sent through another sBs having the highest RSSI of the wireless connections. Although the consideration of spatial diversity is notable, the work is done considering offloading of a single task only [55].On the other hand, user mobility affects the scheduling on the edge so this work proposed a framework to reduce the task execution scheduling in mobile edge network during user mobility. They have considered the information of user mobility and the information of tasks and sBSs resources and have used lightweight heuristics solution to get fast scheduling during task offloading on sBSs with different users equations [53]. The main objective of the task scheduling in this framework is to maximise the using of MEC to reduce the delay time with all users during offloading tasks to the sBS. They have considered a set of users as U within which each user (i) has own computation task (j) that will be assigned to a set of base station as sBS. In the route of user mobility, there is a sequence of sBS in the user path Pi and (k) belongs to one of the paths in Pi that contains a set of sBS. Each task of Ti,j should be executed in the edge once time only along the user trajectory Pi. The problem modeled as an optimization problem is as follows:(4)maxDe1U∑i∈U∑j∈Tidi,jti,jl−ti,jedge,s.t.∀ti,j,∑k∈Pidi,j,ke<=1.In studies regarding optimization of maximising energy saving, this study demonstrated a framework to reduce energy consumption from mobile devices by optimizing the transfer time and the size of the data loaded to the edge network AP during the offloading process [113]. Another work considers the dynamicity in the state of the channel in transmitting tasks through wireless means and presents a scheme for tasks scheduling and offloading them. The scheme is designed in such a way that it can properly use wireless connections and user buffers, so that the energy consumption in task execution is reduced [59].In another work, a framework is demonstrated to offload computation, in mobile edge computation, for multiple devices, and a design is constructed in order to minimise the energy consumption in these devices [60]. Another study has advanced the work on this topic by considering the possible occurrence of collisions and interference due to multiple users trying in accessing single sBs, which can incur high-energy consumption in the user devices. In a way, the offloading was modeled in the game theory with multiple users and shown that this is always compatible with Nash equilibrium [61]. This work minimized mobile device energy consumption by centralising the framework for the multiuser MEC system. They have used orthogonal frequency division multiple access (OFDMA) and time division multiple access (TDMA) with the purpose of reducing the energy consumption of mobile devices [62]. Finally, another work regarding optimization of energy consumption used a framework to harvest mobile device energy from a base station or able to offload tasks to sBSs for the same purpose of saving energy [46].In this study, the offloading scheduling task has been optimized along with optimizing the allocation of the power that is transmitted in the MEC systems for several numbers of independent tasks. To reduce the delayed weighted sum in computation along with the consumption of energy, there has been proposed an algorithm, namely, low complexity suboptimal algorithm. It has been illustrated in this work that the implementation of this algorithm has reached minimum latency in execution with significant energy saving in a device. To find out the optimal tradeoff between complexity and delay, a lightweight approximation is used [66]. This work shows the use of a sequential game model with multiple stages to realise the concurrent requirements regarding energy and delay at the same time [67].Some other studies perform a combined optimization of energy consumption and execution delays in the tasks. Through these, it is seen that minimization in the task delays, most of which can be executed faster in mobile devices than in the edge network, contributes to the high power consumption in MEC. Some of these works designate a level limit of energy consumption and minimise the delays in the tasks without crossing the set limit. For example, this work presents a flexible offload scheme, considering the single user, to decrease the execution delay in energy harvesting devices, where these devices increase the complexity of offload algorithms [65].


## 4. Offloading Comparison between MCC and MEC

Consider a scenario where a robot has to be alarmed before it hits an obstacle. In this use case, it is necessary to execute the obstacle recognition task quickly with minimum latency which is only possible in MEC paradigm. If this task is offloaded to the MCC server, the alarm may be delayed due to latency. On the contrary, some applications require huge computational power and are more suitable for MCC offload where cloud servers efficiently serve applications with enough storage and computational powers.Mobile edge computing was produced to overcome some limitations of mobile cloud computing such as the latency problem during offloading to the main cloud and the energy consuming which accompanied the latency in MCC and the assumption of stable network environment during offloading process in MCC. Latency problem is one of the main limitations regarding mobile cloud computing. It costs a substantial amount of latency to transfer the migration data to the cloud. The latency in transferring the data in MCC is mainly raised through three resources, which include the latency between connected access points and mobile devices, between the access point and the core network, and between the core network and the cloud server. Latency between connected APs and mobile devices depends on various factors, such as the quality of the wireless channel, loss of path, the number of users sharing bandwidth, and interference. While transferring the data to the core network from the access point, the main reason for latency is backhaul in link capacity due to the low data rate. The latency between the cloud server and core network depends on the latency of a wide area network, which relies on the number of hops and the distance between them.Consider a scenario where a robot has to be alarmed before it hits to an obstacle. In this use case, it is necessary to execute the alarmed task quickly with minimum latency which is only possible in MEC paradigm. If this task is offloaded to the MCC server, the alarmed may be delayed due to latency. Although some applications require huge computational power such as prediction and pattern matching, these applications may not be executed at the MEC servers due to limited resources. Hence, MCC efficiently serves these applications with enough storage and computational powers.Once the offloaded task reaches the cloud server, the server undertakes the entire computation required task and transfer the task result back to the mobile device through the core network and APs. In contrast, in the case of MEC, large part or whole tasks are handled in edge side. This results in a significant reduction in latency when transferring data to the cloud server side from APs through the core network. Through the deployment of mobile edge computing, latency can be reduced from 60% to 90% as per the field trial conducted by China Telecom. They showed that MEC compared to MCC could reduce latency by up to 88% for improved reality application [114, 115]. In the case of energy consumption which is accompanied by the latency in MCC, computational tasks are offloaded by the mobile devices to the cloud server through the APs and core network experiencing significant latency. For meeting the latency requirements of real-time applications and intensive computation applications, the mobile device offloads a small portion of the task while performing a large portion of tasks locally on the mobile device, resulting in a high consumption of the battery power of the mobile device.On the contrary, in MEC, a lower latency enables offloading of a larger portion of or all computation tasks to the edge side, which will help reduce the energy consumption of the mobile battery. MEC helps to extend the lifetime of the battery of mobile devices, and the MEC saves 42% of energy consumption compared to MCC as stated on [116]. Finally, computation offloading in MCC considers the network as a stable environment, which means that after the offloading decision is made, the task will migrate to the main cloud side without considering the network fluctuations that could happen during the offloading process, such as user mobility during offloading, which will disconnect the connection between the mobile device side and the cloud side [94, 98, 117]. On the contrary, MEC helps to get the best solution in the worst case of network fluctuations during task offloading and researchers in the area of MEC try to find solutions in various network issues that affect the computation offloading mechanism [53, 118–122].


## 5. Research Challenges and Future Directions

Effective computation offloading suffers from multiple issues which should be considered for better MEC and MCC performance. Task offloading in edge networks has received a lot of attention from the research community, as we have seen in the related works in Table2. However, existing related works still have open issues that need to be addressed in respect of offloadable tasks in the distributed environment. In this section, we highlight some important issues and recommend future directions in the promising area of computation offloading.


### 5.1. Dependencies

Offloading applications with concurrent tasks to MEC makes offloading more complex. As noted in multiple studies on task scheduling [123–125], applications in ecosystems (mobile devices) could consist of several tasks with dependencies, which are modeled as directed acyclic graph DAGs. A task-call graph is used to determine the dependencies among tasks in the application [51]. As we observed in existing MCC/MEC works, current studies have not solved the problem of offloading dependent tasks to the edge when considering user mobility in a distributed environment and task constraints [70, 73, 78, 88].


### 5.2. Mobility

Mobility awareness is still a significant problem in mobile cloud/edge computing networks because of sending/receiving jobs from different edge nodes (?). Most existing work on edge network task assignment makes the assumption that users are stationary during task assignment and that communication between mobile devices and edge nodes is always available [126–128]. The authors of [46] discussed the assignment of tasks with resource allocation for multiple users in a single edge server while assuming that users can access the edge server anytime and anywhere, which is unrealistic in the real world. Another study [128] suggested an online solution for the deployment of stream based on the task assignment of multiuser systems in the edge.They predict the application response time using a queuing theory-based model and then develop an optimization model to reduce the delay. However, they do not consider the user mobility in the edge. Another study [53] assumes that all properties are known in advance: task attributes, network conditions, and user mobility (with independent offloadable tasks). They develop an optimization model to reduce the latency in task execution, and they consider user mobility with a centralized server as a (static environment) with predefined properties. However, such scenarios are limited in a distributed edge environment. Ultimately, current work in edge networks (summarised in Table 2) has not considered distributed offloading for offloadable jobs of dependent tasks with task constraints with multioffloading systems in the edge network during user mobility.


### 5.3. Heterogeneous Task and Computing Nodes

Most of the previous works focus on the assumption of homogeneous tasks. Through these assumptions, offloading becomes simpler. However, in real scenarios, computation tasks are heterogeneous; e.g., some are preemptive and others are nonpreemptive. Similarly, computing nodes of MEC and MCC have different computing powers in the terms of CPU cycles. These heterogeneous environments act as limitations of computation offloading which needs to be considered [14, 104].


### 5.4. Security and Privacy

There are multiple security concerns during computation offloading. One is integrity, confidentiality, and authentication between the computing nodes and the end devices. In this context, security issues are similar in MCC and MEC. However, the limited features and computing power of MEC servers make edge computing less efficient in the security context [109]. The other aspect is the lower efficiency of edge devices to execute security algorithms. To address this problem, these security functions should be offloaded to the MEC server. This mechanism requires more security protections due to the dynamic environments and heterogeneous nature of end devices [129, 130]. Offloading security tasks on the MEC server opens up many privacy issues, and protecting end-node privacy is more challenging compared to MCC [30].


### 5.5. Decision among Local and Remote Computation

Wireless fading channels and dynamic environments make the decision between local and remote computation more challenging. It is necessary to decide on the offloading decision before transferring the tasks to the computing servers to enhance the overall computation rate of MCC or MEC. Machine learning and reinforcement mechanism may be applied to predict the fading nature of wireless channels and dynamic environments. However, these mechanisms open up some more research challenges including online and offline training for less efficient end devices [131].


### 5.6. Decision among Partial and Full Offloading

Most of the previous work focuses on partial offloading schemes where a resource-critical part of a task is offloaded to the MCC or MEC server. These studies also considered local computation based on dynamic channel conditions and used hybrid schemes for offloading. However, the hybrid scheme of partial offloading and local computation is not always efficient. In particular, the decision between partial and local computation depends on the parameters of the system, such as the number of bits to be computed at distance from the computing servers [103]. Similarly, in dynamic fading environments, full offloading effectively maximises the performance of MEC and MCC in terms of computation rate. However, the decision between partial and full task offloading needs to be further explored.


## 5.1. Dependencies

Offloading applications with concurrent tasks to MEC makes offloading more complex. As noted in multiple studies on task scheduling [123–125], applications in ecosystems (mobile devices) could consist of several tasks with dependencies, which are modeled as directed acyclic graph DAGs. A task-call graph is used to determine the dependencies among tasks in the application [51]. As we observed in existing MCC/MEC works, current studies have not solved the problem of offloading dependent tasks to the edge when considering user mobility in a distributed environment and task constraints [70, 73, 78, 88].


## 5.2. Mobility

Mobility awareness is still a significant problem in mobile cloud/edge computing networks because of sending/receiving jobs from different edge nodes (?). Most existing work on edge network task assignment makes the assumption that users are stationary during task assignment and that communication between mobile devices and edge nodes is always available [126–128]. The authors of [46] discussed the assignment of tasks with resource allocation for multiple users in a single edge server while assuming that users can access the edge server anytime and anywhere, which is unrealistic in the real world. Another study [128] suggested an online solution for the deployment of stream based on the task assignment of multiuser systems in the edge.They predict the application response time using a queuing theory-based model and then develop an optimization model to reduce the delay. However, they do not consider the user mobility in the edge. Another study [53] assumes that all properties are known in advance: task attributes, network conditions, and user mobility (with independent offloadable tasks). They develop an optimization model to reduce the latency in task execution, and they consider user mobility with a centralized server as a (static environment) with predefined properties. However, such scenarios are limited in a distributed edge environment. Ultimately, current work in edge networks (summarised in Table 2) has not considered distributed offloading for offloadable jobs of dependent tasks with task constraints with multioffloading systems in the edge network during user mobility.


## 5.3. Heterogeneous Task and Computing Nodes

Most of the previous works focus on the assumption of homogeneous tasks. Through these assumptions, offloading becomes simpler. However, in real scenarios, computation tasks are heterogeneous; e.g., some are preemptive and others are nonpreemptive. Similarly, computing nodes of MEC and MCC have different computing powers in the terms of CPU cycles. These heterogeneous environments act as limitations of computation offloading which needs to be considered [14, 104].


## 5.4. Security and Privacy

There are multiple security concerns during computation offloading. One is integrity, confidentiality, and authentication between the computing nodes and the end devices. In this context, security issues are similar in MCC and MEC. However, the limited features and computing power of MEC servers make edge computing less efficient in the security context [109]. The other aspect is the lower efficiency of edge devices to execute security algorithms. To address this problem, these security functions should be offloaded to the MEC server. This mechanism requires more security protections due to the dynamic environments and heterogeneous nature of end devices [129, 130]. Offloading security tasks on the MEC server opens up many privacy issues, and protecting end-node privacy is more challenging compared to MCC [30].


## 5.5. Decision among Local and Remote Computation

Wireless fading channels and dynamic environments make the decision between local and remote computation more challenging. It is necessary to decide on the offloading decision before transferring the tasks to the computing servers to enhance the overall computation rate of MCC or MEC. Machine learning and reinforcement mechanism may be applied to predict the fading nature of wireless channels and dynamic environments. However, these mechanisms open up some more research challenges including online and offline training for less efficient end devices [131].


## 5.6. Decision among Partial and Full Offloading

Most of the previous work focuses on partial offloading schemes where a resource-critical part of a task is offloaded to the MCC or MEC server. These studies also considered local computation based on dynamic channel conditions and used hybrid schemes for offloading. However, the hybrid scheme of partial offloading and local computation is not always efficient. In particular, the decision between partial and local computation depends on the parameters of the system, such as the number of bits to be computed at distance from the computing servers [103]. Similarly, in dynamic fading environments, full offloading effectively maximises the performance of MEC and MCC in terms of computation rate. However, the decision between partial and full task offloading needs to be further explored.


## 6. Conclusion and Future Work

Overall, there are a large number of offloading techniques in edge networks. Two of the biggest categories are (i) offloading in a static environment and (ii) offloading in a dynamic environment of the edge. The optimization aim in both of these techniques can be categorised into three kinds: (i) optimization of minimizing the delay of the tasks offloading in the edge, (ii) optimization of maximising the energy saving of UE during task offloading to the edge, and (iii) the combination of the optimization of energy consumption and execution delays of the tasks in the edge. Existing related works in MCC/MEC still have open issues with (i) dependent tasks offloading in the edge with the distributed environment and (ii) mobility-awareness problems in the edge with the distributed environment. In future work, we aim to develop heuristic and fully distributed offloading algorithms to minimise the average completion time of offloadable dependent tasks with task constraints while factoring in user mobility, which affects reachability to the edge nodes.



---

*Source: 1121822-2022-06-28.xml*