With the emergence of high quality and rich multimedia content, the end user demands of content processing and delivery are increasing rapidly. In view of increasing user demands and quality of service (QoS), cloud computing offers a huge amount of online processing and storage resources which can be exploited on demand. Moreover, the current high speed 4G mobile network i.e. Long Term Evolution (LTE) enables leveraging of the cloud resources. Mobile Cloud Computing (MCC) is an emerging paradigm comprising three heterogeneous domains of mobile computing, and wireless networks. MCC aims to enhance computational capabilities of resource-constrained mobile devices towards rich user experience. Decreasing cloud cost and latency is attracting the research community to exploit the cloud computing resource to offload and process multimedia content in the cloud. High bandwidth and low latency of LTE makes it a suitable candidate for delivering of rich multimedia cloud content back to the user. The convergence of cloud and LTE give rise to an end-to-end communication framework which opens up the possibility for new applications and services. In addition to cloud and network, end user and application constitute the other entities of the end-to-end communication framework. End user quality of service and particular application profile dictate about resource allocation in the cloud and the wireless network. This research formulates different building blocks of the end-to-end communications and introduces a new paradigm to exploit the network and cloud resources for the end user. In this way, we employ a multi-objective optimization strategy to propose and simulate an end-to-end communication framework which promises to optimize the behavior of MCC based end-to-end communication to deliver appropriate quality of service (QoS) with utilization of minimum cloud and network resources. Then we apply application partitioning and offloading schemes to offload certain parts of an application to the cloud to improve energy efficiency and response time. As deliverables of this research, behavior of different entities (cloud, LTE based mobile network, user and application context) have been modeled. In addition, a comprehensive application partitioning and offloading framework has been proposed in order to minimize the cloud and network resources to achieve user required QoS. Keywords: Long Term Evolution (LTE), Cloud computing, Application partitioning and offloading, Image Retrieval.
In this chapter, we will formulate the research questions and corresponding research objectives in view of the current demands of the end to end communications. In addition, we will summarize our contributions of this research. Motivation Over the recent various innovative multimedia applications, services and devices have emerged in the consumer market and have allowed people to explore and consume more media contents while on the go [1]. We are experiencing a media revolution. With the emergence of rich complex mobile applications, the processing and storage hardware demands of the end user are increasing in comparison to the available mobile hardware resources. An approach to leverage this increase in the demand is by thin client approach [2], where the end user mobile resources are assumed to be scarce and surrogate model [3] e.g. cloud computing is used to leverage the imbalance of the resources. Cloud computing [4] has emerged as a flourishing framework and the cloud computing platform have proved itself to become a unique framework providing various services, high computational ability, huge storage and bandwidth at competitive cost. In the near future, we foresee the need of many possible scenario's involving resource (computational and storage) constrained mobile devices to access and process highly intensive computational tasks like 3D rendering [5], video streaming [6] and gaming [7]. Moore's law [8] predicts that the storage and processing abilities of the cloud will continue to increase while corresponding costs will go down over the next few years. This enables cloud computing to be favorable candidate to handle computational intensive tasks for mobile devices. Mobile Cloud Computing (MCC) [9] provides an efficient computational framework for mobile terminals i.e. User Equipment (UE) to offload it's computationally intensive tasks to remote server. Mobile-cloud interaction opens up the further avenues for research if cooperative communication among the mobile nodes is also considered [10]. Cloud process the offloaded content and deliver it to the UE. Content offloading and delivery represents an end-to-end communication (E2E) scenario in which mobile terminal interacts with the cloud through the network. In this way, the offered end user QoS depends upon different parameters of cloud (cost, latency) and network (throughput, bandwidth, channel errors, number of users) [11]. Moreover, application profile (service requirements including jitter,
Chromosomes are sorted and put into fronts based on Pareto Non dominated sets. Within a Pareto front, the chromosomes are ranked based on the Euclidean distance between solutions. Generally, solutions which are far away (not crowded) from other solutions are given a higher preference during selection. This is done in order to make a diverse solution set and to avoid a crowded solution set (local minima or maxima). The best N (population) chromosomes are picked from the current population and put into a mating pool. In the mating pool, tournament selection, crossover and mutation is done. Crossover refers to breaking two chromosomes from a predefined point and joining them together to create a new chromosome. While mutation refers to random single bit change in a chromosome. In this way, new population is created in mating pool. The mating pool and current population is combined. The resulting set is sorted, and the best N chromosomes make it into the new population. Go to step 2, unless maximum number of generations have been reached. The solution set is the highest ranked Pareto non dominated set from the latest population. Population size and number of generations has a influence on the efficiency of the algorithm. Pareto Multi-objective Optimization in Cloud and Network In literature a lot of efforts are been found in the context of optimizing resources in the network and cloud. Some author's has opted pareto optimization methods. In [91], authors apply MOP on LTE based cellular multi-user scenario and maximize the user throughput by marginalizing the Inter-cell interference cancellation coordination (ICIC). Similarly in [92], authors propose an adaptive algorithm to allocate resources in multi-user cellular scenario at bit and subcarrier level, considering objective of rate maximization within the total power constraint using NSGA-II algorithm. Similarly, authors in [93] attempt to improve the energy efficiency of cellular system by dynamically switching the LTE base stations based on user demands. In computing, pareto based multi-optimization has been used to dynamically distribute and schedule the cloud resources (CPU, RAM, Virtual Machines). In [94], authors manage to balance the load on cloud resources using a pareto optimization based scheduler. Also they have applied ant colony optimization to improve the task scheduling of the cloud. In this way, scheduler adapts its strategy according to the changing environment and types of
its QoS requirements. Figure 31: Contextual Model and their corresponding parameters Cost-benefit approach has been adopted to design the objective function of each entity in the end to end communications. For example, SNR allocated to user is cost in network perspective, while throughput achieved is corresponding benefit. The relation between is not linear. Cost investment after a knee point (Q) does not translate into proportional benefit. Figure 3.2 shows a scenario involving knee point. After Q, the cost investment at the input resources does not translate into proportional benefits at the output. Aim of the optimization is to find Q of all of the objective functions of each entity and use them to maximize/minimize the objective function. Figure 32: Cost-Benefit optimization model and Knee Point We have to exploit all those knee points in the end to end communication scenario. In this way, we aim to design the objective functions such that we obtain maximum benefit from the minimum cost investment. In other words, objective functions maximizes the term denoted in the equation below. It finds those feasible solutions from the complete solution set. (3.1) Maximizing a single objective function is simple, but scenarios where multiple objective functions are involved and input/output parameters are conflicting in the objective functions, then finding the optimum solution which satisfy all the objective functions is quite difficult. This eventually promises to deliver required QoS with minimum system resource investments. An overview of the end-to-end communication system has been depicted in Figure 3.3. The Figure the interaction of mobile network and cloud computing infrastructure in view of user and application context. Corresponding parameters of each module has been described. In section 3.1-5, we elaborate the behavior of each contextual model in view of the mentioned parameters. Then in section 3.6, we define the outer blue boundary depicted in Figure 3.3, i.e. multi-objective optimization. In the end, in view of the simulation results, we formulate and propose a comprehensive single user optimization model in section 3.7. Figure 33: Contextual Models in optimization domain
Summary In this chapter, the aim was to expand and improve the application and offloading framework by taking offline parameters the application contextual model and topological structure of an application. Multiple topologies of applications graph have been analysed with various values of edges and vertices. We application various graph theory metrics which provides reasonable information about the likelihood any application to be partitioned. In this way, we have defined offloading likelihood index which takes the structural information into account and predicts how much likely an application to be partitioned without knowledge of resources of wireless network and cloud. We also simulated the effect of different kind of errors on our image retrieval application. We integrate offloading likelihood index and error response into the partitioning and offloading framework and further improved. In the end, we have test improvised version of partitioning and offloading framework in different scenarios involving various parameters of wireless network, cloud and user. & Conclusion As the mobile network and user demands are evolving, researchers have turned their focus towards network edge i.e. investing more resources on the base stations. This provides a unique opportunity to exploit a lot of computational capacity at the base stations by leveraging the cloud computing resources. In this thesis, we have outlined an effort to propose and simulate an end-to-end communication framework and then further expanded the framework by focusing on application partitioning and offloading framework. A optimization methodology has been applied on the given framework. The proposed methodology aims to minimize the resources in network and cloud in view of application and user context maintaining the appreciable user QoS. In Chapter 3, we propose a novel end to end multimedia communications framework in which mobile network, cloud, mobile user and application interact with each other and are connected to each other through a block mechanism. Setup is named as CANU comprising Cloud, Application, Network and User entities in an end to end communications. Mathematical model of each entity presented in CANU has been developed, and integrated in a proposed CANU framework. A multiobjective optimization algorithm NSGA-II has been applied on the CANU framework to optimize the behavior of the different models.
The designed approach for optimization is centralized. It piles up complexity, if the system scales up (no. of users or applications increases). As discussed, a possible alternative is a decentralized approach, which comprises an N-agent system, such that each agent influence about the global optimal solution. However large communication overhead is a problem in this approach. Parameters for decision making such as bandwidth, security, response time and failure rate are sometimes hard to measure or acquire in a timely manner in practical systems. Therefore, the way to estimate and measure parameters need to be further investigated. 4G LTE communication network as a network model is used, but due to cost concerns there are other alternatives e.g. wifi and bluetooth which can provide good connectivity and throughput in certain scenarios. In designed methodology, a simplistic user QoS has been considered, in which user has equal preferences in terms of choosing solutions concerning the resources invested in cloud and network. This does not take into account, if user has some inherent preferences like high bandwidth demand in view of its subscriber context. Effect of signaling channels overhead has not been considered. Similarly, the link between LTE evolved packet core (EPC) and the cloud is also assumed to be perfect. Round robin scheduler was assumed in the network context. Other scheduler e.g. proportional fair, max SINR and best CQI has not been simulated. Effect of handovers has not been taken into account. User high mobility can cause excessive handovers, which might lead to missing QoS latency requirements. Future Work In view of the critical review, we outline the following directions of the future work. Rate of change of channel conditions, type of channel (Gaussian, Rayleigh, Pedestrian, Vehicular), cloud and network load strongly influence the optimal solution. In this way, a comprehensive single user optimization model is proposed in the section 3.4, which can be extended further to accommodate multi user scenario in future to extend the current optimization framework. In the similar way, the single user optimization framework can be extended to a scenario by scaling up the network and cloud resources. In view of the increased complexity of multi-user model dynamic programming and Collaborative game theory