Multi‐objective constraint and hybrid optimisation‐based VM migration in a community cloud

IET Computers & Digital TechniquesVolume 14, Issue 1 p. 37-45 Research ArticleFree Access Multi-objective constraint and hybrid optimisation-based VM migration in a community cloud Pradeepa Parthiban, Corresponding Author Pradeepa Parthiban pradeepaselva@gmail.com PSNA College of Engineering and Technology, Dindigul, Tamilnadu, IndiaSearch for more papers by this authorPushpalakshmi Raman, Pushpalakshmi Raman PSNA College of Engineering and Technology, Dindigul, Tamilnadu, IndiaSearch for more papers by this author Pradeepa Parthiban, Corresponding Author Pradeepa Parthiban pradeepaselva@gmail.com PSNA College of Engineering and Technology, Dindigul, Tamilnadu, IndiaSearch for more papers by this authorPushpalakshmi Raman, Pushpalakshmi Raman PSNA College of Engineering and Technology, Dindigul, Tamilnadu, IndiaSearch for more papers by this author First published: 25 September 2019 https://doi.org/10.1049/iet-cdt.2018.5243Citations: 1AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract The growing demand for the cloud community market towards attracting and sustaining the incoming and the available cloud users is addressed actively to meet the competitive environment. There is a good scope for improving the provider capabilities in the cloud in order to satisfy the users with attractive benefits. The study introduces an effective virtual machine (VM) migration strategy using an optimisation algorithm in such a way to facilitate the user selection of the providers based on their budgetary requirements in running their own platforms. The constraints associated with the selection of the provider include cost, revenue, and resource, which are altogether confined as an elective factor. The optimisation algorithm employed for the VM migration is referred to as Taylor series-based salp swarm algorithm (Taylor-SSA) that is the integration of the Taylor series with SSA. The evaluation of the method is progressed using three setups by varying the number of providers and users. The cost, the revenue, and the resource of the proposed method are analysed and concluded that the proposed method acquired a minimal cost, maximal resource gain and revenue. 1 Introduction Cloud computing serves as an effective platform for distributed sources, offering highly scalable and virtualised hardware or software services over the internet [[1]-[3]]. Cloud computing renders on-demand services to the clients through a fault-tolerant and energy-efficient approach [[4], [5]]. The software service offered by the cloud is referred as the software as a service, hardware service is referred as the infrastructure as a service (IaaS), and platform as a service on the basis of the demand. The computing services provided by IaaS through the virtual machines (VMs) reduce the maintenance and installation cost of the customers [[6]]. The service providers of the cloud, such as Google, Amazon, and so on, have extended the data centres to meet the rising demands of the cloud users. A large number of data centres render several hundreds of computational hosts and number of speed switches with a large number of devices that insists on consuming a huge amount of energy [[2]]. Generally, the cloud models are classified into four types, such as public, hybrid, private, and community. When public users use the cloud in the pay-to-use concept, it is referred to as a public cloud. On the other hand, private clouds or internal data centres are used by the organisation or business [[7]], whereas the community cloud provides the infrastructure among various organisations [[8]]. Hybrid cloud gives flexibility for applications with better scalability and tolerance towards fault [[5], [9]]. The community cloud is grouped between the public and private clouds based on targeted consumers. However, a community cloud is similar to the private cloud excluding the computational and infrastructure resources of the organisations sharing common security, privacy, and regulatory considerations [[10]]. The community cloud intends to integrate the resources obtained from grid computing, digital ecosystems, and green computing, using cloud computing through the advanced self-management computing scheme [[5]]. The services for continuous applications include web services, which was a critical crisis in most of the community cloud data centres. On the other hand, physical machines (PMs) available in data centres require undergoing a lot of maintenance, which cannot inactivate the services running in the VMs. In order to minimise the delay associated with waiting, migration of the VMs to the active VM is allowable [[11]]. Even though the service rendered is continuous with minimal delay, there are huge impacts on the performance of executing the VMs, and it requires additional cost for computation [[12], [13]] and thereby, creating significance on the trade-off between migration cost and delay [[11]]. VM migration is a major function while installing the current cloud in order to support the administrative functions and serves the load balancing. There are two options for migration, such as spend-resume and live migration. The VM fails operation when the suspend-resume is employed, and hence, the details from the VM's RAM are copied to the targeted PM such that the function of the particular VM is centred in that PM [[14]]. There are a lot of cloud providers in a competitive environment, such that multiple users interact in the cloud environment. Hence, the service providers follow an optimal price strategy to sustain the existing users and to attract new users so as to increase the revenue. On the other hand, the final revenue is improved by the cloud providers through their effective co-operation among themselves. Depending on the service level agreement, a provider outsources the request from the user to the partner in order to minimise the operational cost and revenue. On the other hand, whenever the resources requested by the user are greater than the service provided by a provider, the request can be met by some other provider. Thus, the user selects the provider, who best satisfies the needs of the user with the specified budget [[15]]. The primary intention of the research establishes the VM migration method in the community cloud using a novel optimisation algorithm. Importantly, the location to migrate the VM is decided based on the proposed service provider selection strategy. The selection of the provider is decided based on multiple constraints, such as resource availability, revenue cost, bandwidth, and user choice. Accordingly, effective searching criteria are formulated to find the optimal service based on the constraints. The searching criteria are formulated as optimisation problems, which are solved using an algorithm named Taylor series-based salp swarm algorithm (Taylor-SSA). The proposed Taylor-SSA is designed by introducing Taylor series concept in SSA and the fitness function for the optimal VM migration is carried out by considering various parameters, such as under-loaded/resource availability revenue cost/provider point of view, cost/user point of view, bandwidth, and user choice about the target resource. Finally, the cloud manager migrates the VM in the community cloud using the proposed Taylor-SSA-based VM migration scheme. Contribution of the study: The major contribution of this study is the development of the Taylor-SSA by integrating the Taylor series and the SSA. The fitness function is developed by considering the multiple constraints, such as resource availability, revenue cost, operational cost, bandwidth, and user choice. The paper is organised as follows: Section 1 gives a detailed background of cloud computing and VM migration in the cloud. The review of the existing works is discussed in Section 2 along with the challenges of the work. The proposed method for migration of the VMs in the cloud is elaborated in Section 3, and Section 4 gives the result of the proposed method. Finally, Section 5 summarises the paper. 2 Literature review The section reviews eight existing methods and elaborates the demerits of the methods that stay as a motivation for the research. Tsakalozos et al. [[14]] developed an on-demand virtual disk, which adjusted the network intelligently but did not assist the real-time service. Rodrigues et al. [[16]] developed an analytic model for the service delay in edge cloud computing systems that were capable of dealing with different types of services, but the multiple cloudlets could be satisfied. Rahmanian et al. [[2]] modelled a penalty-aware and cost-efficient resource management that minimised the total operational cost, but the shutdowns in the host were not always the best under all scenarios. Melhem et al. [[17]] modelled an algorithm, median absolute deviation Markov chain host detection algorithm, to decrease the level agreement. The demerit of the method was that the service was interrupted and affected by network overhead. Mansouri et al. [[18]] developed an optimal algorithm to decide the placement of objects that remained as cost-saving, but the method suffered from time complexity. Zhang et al. [[19]] modelled a network-aware VM migration using a genetic algorithm and artificial bee colony. The method exhibits less network cost, but the running time was maximal. Paulraj et al. [[6]] modelled VM migration in cloud data centres that enhanced the migrations and load balancing, but the network bandwidth consumption was high. Wang et al. [[11]] developed a heuristic algorithm to minimise the delay that attained effective trade-off between delay and migration cost. The failure of the method was regarding data dependency. Truong-Huu and Tham [[15]] modelled a discrete choice model for the cloud providers, which was scalable. However, the execution time was relatively high. Krishna, J. V., et al. [[24]] developed a VMM algorithm by proposing Lion-Whale optimization by the incorporation of Lion optimization algorithm and Whale Optimization Algorithm. For the optimal VMM, Lion-Whale VMM used a fitness function that considered resource utilization, energy consumption, and migration cost. Polepally, V and Chatrapati, K.S [[25]] designed a technique, called Modified Exponential Gravitational Search Algorithm based on Virtual Machine Migration strategy (MEGSA-VMM), based on the idea of gravitation. The technique adopted MEGSA algorithm that employed gravitational search algorithm and exponential weighted moving average theory, considering various constraints of Quality of-Service (QoS). 2.1 Challenges At present, a lot of applications are available to deal with cloud computing, but with greater computation. The applications make use of the huge amount of resources leading to the generation of a large number of datasets, which reports an exponential growth. Thus, the main challenges faced by the methods include: availing the resources and services with less delay, better demand-power management, and better tolerance to a fault in cloud data centres [[20]]. VM migration needs to address three challenges. (i) Migration of the data in the memory: When the migrated VM continues to run after suspension, the running states including the details of the central processing unit, memory, and device are sent to the target. (ii) Migration of the storage data: When the storage is not reached by the target server during the migration, the virtual disks belonging to the migrated VM are transferred to the destination site as well, because remotely accessing disk data not only results in big disk I/O latency but also may violate, service-level agreement. (iii) Network connection continuity: Once the VM migrates to a new location, there is a need for some methods for attaining the reachability of the users. For the live migration, it is essential to keep the connections live at the time of the migration [[21]]. 3 System model of the cloud community market This study considers the cloud community market, where the providers offer infrastructure-based services so that the users are capable of establishing their own platform through the use of the providers in the cloud community market. VM migration refers to the migration of the resources among the providers so that the user request is satisfied and the selection of the resources is based on the budgetary requirements of the user and the available resources in the provider. The schematic diagram of VM migration using the proposed hybrid optimisation algorithm is shown in Fig. 1. The cloud community model consists of a number of providers, and each provider has a different number of resources (VMs). In order to ensure the low-level system maintenance, fault management, load balancing, and reduce energy consumption, VM migration is important. Here, the VM migration is performed between the providers by the proposed Taylor-SSA based on several constraints, such as available resources, revenue cost, operational cost, bandwidth, and choice of the user. Fig. 1Open in figure viewer VM migration using the proposed hybrid optimisation Let us consider there are P number of providers in the community cloud market with v number of VMs or resource types belonging to each service provider. A total of U numbers of users are available in the community cloud market. The total number of PMs, total number of VMs, and total number of users in the community cloud model are given in (1)–(3), respectively (1) (2) (3)The users request the providers for the resource based on the budget of the user and let us denote the budgetary constraint of the kth user as gk. Each user possesses their own budget and chooses the resource in such a way to meet their requirements. The learning factor of the ith provider is denoted as . Each of the providers in the community cloud possesses various types of resources and assumes as the jth resource type available in the ith provider. Moreover, the benefit of the user for using the respective VM running under a provider is determined and represented in the matrix format such that represents the user benefit of the jth VM running on the ith provider that specifies the relative capacity of using the resource to meet the demands of the user. Likewise, each VM needs certain costs to run under a provider and is the price of the jth VM on the ith provider. The per-unit price of the VMs varies from provider-to-provider based on the capacity of the resource type and the price of running each VM under each provider is denoted as h. The users request for the resources in bundles and they possess the option to choose the provider based on their own budget. The request to the resource is given as the set of resources used to run the user task rather than requesting for the individual resources. In other words, the user request refers to the total number of resources required from each type of resource present under a provider. The request from a user k is denoted as (4)where b refers to the total number of instances belonging to resource j. On the other hand, the active or idle state of the provider is denoted as and the individual state of the ith provider is denoted as . Let be the bandwidth of the ith provider. 4 Objective model for VM migration in the cloud community market The proposed objective model is based on the elective gain of the provider, which is decided based on the budgetary constraint of the kth user and the operational cost. The elective gain becomes zero if the provider is unable to offer the service within the budgetary constraints of the user. Once the budgetary constraints are satisfied, the gain factor is based on the elective factor. The elective gain is given as (5)where is the budgetary constraint of the kth user and M specifies the operational cost of the provider. The elective factor is denoted as and the steps to formulate the elective factor are described below. 4.1 Elective factor The elective factor should be a maximum value that facilitates the maximum utilisation of the resources, minimum cost, maximum revenue, and maximum probability for a corresponding provider. The maximum probability for the provider assists in choosing the corresponding provider to run the operation while satisfying the user requirements. The elective factor is given as (6)where is the resource availability of the ith provider for the kth user, symbolises the operational cost of the ith provider to operate the task of the kth user, signifies the bandwidth of the ith provider, refers to the probability of the kth user to select the ith provider, and indicates the revenue of the ith provider. 4.2 Available resources in the provider The resources available for the provider should essentially satisfy the user requirements, and it is formulated as (7)where refers to the jth resource type available in the the ith provider and denotes the jth resources of the ith provider used by the kth user, and the type of the resources used by the user is updated in the request assignment matrix for every iteration. The denominator in (7) symbolises the maximum of the resource type offered by the provider. 4.3 Operational cost of the provider The resource capacity and ability to manage the operational resources vary among the providers available in the community cloud market that varies the operational cost among the providers. As per the learning curve model, doubling the operational units leads to a decrease in the marginal cost by a fixed factor. Thus, learning factor is nothing, but the subtraction of the fixed factor from one. It is clear that the provider corresponding to the greater learning factor requires higher operational cost when compared to the provider with lower learning rate even when the production units of both the providers are the same. For the individual state of the provider , the active instance specifies the resource types operating under the ith provider in order to satisfy the user. The operational cost of the provider with respect to the active instances is given as (8)where specifies the operation cost of the first active jth VM of the ith provider and denotes the learning factor of provider i, which is the same for all the resource types operating under a provider. On the contrary, the providers additionally sustain some basic services for the idle resources, which are based on the maximum resources offered by a provider and operational cost of idle resources. Thus, the operational cost of the provider based on the idle instances is given as (9)Therefore, the total operational cost of the ith provider is given as (10) (11)where denotes the operation cost of the ith active provider and denotes the operation cost of the ith idle provider. J refers to the normalisation factor. 4.4 Revenue of the provider in the community cloud market The revenue of the provider is computed based on the gross revenue and the total operational cost of the provider i. The revenue of the provider is given as (12)where refers to the gross revenue of the provider that is computed based on the sum of the product of the individual state of the provider i and operational cost of running the task of the users. The revenue of the provider is based on the operational cost of the ith active provider, the operational cost of the ith idle provider, and individual state of the ith provider while accepting the request from the kth user. 4.5 Provider selection probability The facility of the user to choose the provider for establishing their own platform ensures the providers to enhance the services of the providers to face the competitive community cloud market. When the user requests for the resource, the utility of the users varies inversely with the cost he pays to the provider. Thus, the utility function is described as (13)where refers to the preference of the kth user to choose the ith provider, which is a random variable, stands for the cost to be paid by the kth user for the ith provider, and the providers maintain . The probability to choose the provider is given as (14)where is the probability of the kth user to select the ith provider. The cost of the kth user to pay for the ith provider is based on the resources requested by the user and price of the jth resource on the ith provider . The cost is given as (15)Similarly, the user preference is formulated based on the requested resources and the user benefit a (16)where is the number of resources requested by the jth VM by the kth user and refers to the user benefit of the jth resource on the ith provider. 4.6 Bandwidth of the provider The bandwidth of the provider is denoted as . 5 Optimal migration of VMs using proposed Taylor-SSA This section revolves around the ethics associated with VM migration that insists the users choose the provider in the community cloud market so that the users satisfy themselves utilising the resource from the providers and thereby, meeting the budgetary constraints of the user. The section discusses the fitness function and the optimisation algorithm used for VM migration. 5.1 Solution encoding The goal of solution encoding is to better understand the solution vector, which indicates the number of VMs allocated under a provider. The optimisation algorithm chooses the VMs optimally in such a way to offer the bundle of resources requested by the user meeting the user satisfaction and thereby, to assure the user platform for executing their own applications. Let us suppose that there are a total of nine resources and three providers in the community cloud market in such a way that these resources are shared among the three providers. In Fig. 2, it is clear that there are two resources in provider 1, four resources in provider 2, and three resources in provider 3. After performing the VM migration optimally, the solution vector represents five, two, and two resources running in providers 1, 2, and 3, respectively. Fig. 2Open in figure viewer Solution encoding 5.2 Fitness evaluation The fitness formulated aims at solving the maximisation problem that is based on the maximum elective factor , minimum cost , and maximum movement resource gain . The fitness of the solution is evaluated based on the cost and the movement of resource gain. (17)where refers to the elective factor that is based on the availability of resources, cost, revenue, and probability of the user to choose the provider. is the cost of the ith provider and refers to the movement resource gain of the ith provider, which is formulated as (18)where refers to the resource types present previously and indicates the types of resource in the ith provider. The denominator term refers to the maximum number of resources. The elective factor is computed based on (6). 5.3 Taylor series based Salp Swarm algorithm (Taylor-SSA) The Taylor-SSA is the integration of Taylor series [[22]] in the SSA [[23]] in order to assure the optimal migration of the VMs. Taylor series [[22]] is the approximation that is obtained using the sum of the derivatives of a point/solution in such a way that the approximations become effective when a number of terms are included in the series. The series permits the computation of the function values on numerals and in addition, the first term in the series makes it applicable to the restricted domain. Using the Taylor series one can easily calculate the most complex functions, which is the biggest advantage. Despite high performance of SSA, slow convergence speed and getting stuck in local optima are two disadvantages. Hence, the integration of the Taylor series in SSA [[23]] adds value to the standard SSA in the way to offer better approximation and calculation of the global optimal solution. The significance of inferring the SSA is that the other swarm intelligence algorithms based on the single and multiple objectives failed to solve all the optimisation problems as per the no-free-lunch theorem. Additionally, the SSA renders the simulation using the behaviour of the salps that possess the ability to develop as the biggest swarms on the planet, ocean, and navigate efficiently. The optimisation is based on the swarming behaviours of the salps, and the mathematical model of the salps is performed through dividing the salps as two teams, followers and leaders. The leaders lead the swarm, whereas the followers follow the leader such that the guidance to the individual is directed by the leader salp. Hence, the position of the leader and the follower is updated as in the following equations, based on the best position or the position of the food source. The leader in the yth dimension updates the position based on the lower and upper bounds in the yth dimension and on the constants, which assist in balancing between the exploration and the exploitation phases. The position of the leader is given as (19)where refers to the position of the leader salp in the yth dimension at the iteration . The position of the food in the yth dimension is denoted as . The upper bound and lower bound in the yth dimension is given as and , respectively. On the other hand, the notations , , and are the random constants that have specific functions. The random number in the y-dimensional space permits the effective balance between the exploration and the exploitation phases. Similarly, the random numbers, and , available in the y-dimensional space defines if the position of the salp is towards the infinity or negative infinity and decides the step size. Moreover, the values of and vary between 0 and 1. On the contrary, the followers update their position based on the law of motion coiled by Newton, which is given as (20)where specifies the position of the zth salp present in the yth dimension in current iteration . and specify the position of the zth and salp in the yth dimension at iteration l. The integration of the Taylor series in the standard equation of SSA to update the position of the follower facilitates the derivation of the optimal solutions. The update equation of the Taylor series is given as (21)where and are the solution at iteration l and they denote the first- and second-order derivatives corresponding to the current solution . The first- and second-order derivatives are given as follows: (22) (23)Substituting (22) and (23) in the Taylor series, the final equation is given as (24)Rearranging the above equation, we get (25) (26) (27)Now, substitute (27) in (20), we get (28) (29) (30) (31) (32) Equation (32) specifies the position update of the follower salp, which is based on the position of the zth and salp in the previous iterations such that the best solution is derived. and refer to the position of the salp in the previous iterations. t is the constant that refers to the delay and it is mostly equal to 1. The advantages of the Taylor-SSA are that it is capable of dealing with the real-world issues effectively in the presence of the unknown search spaces. Generally, the global optimal solution is not known in the real space, but SSA balances between the exploration and exploitation phases so as to determine the global optimal solution. The Taylor-SSA gains these advantages as it exhibits the adaptive nature due to the greater exploration of the optimisation algorithm. The rapid convergence to the local optimal solution is avoided assuring greater exploitation that is brought about using the constant . Algorithmic steps of Taylor-SSA: The steps of formulating the proposed algorithm are deliberated in this section. Initialisation: The total population of the salps present in the search space is denoted as, ;, where n corresponds to the total number of salps in the search space. Fitness evaluation: The fitness of the individual salps is evaluated in such a way that the salp with the maximum value of the fitness is chosen. The solution or position of the salp refers to the VMs that are to be migrated among the providers in order to satisfy the users. The fitness is evaluated based on (17) and the best solution determined is denoted as, . Based on the best solution, the position of the salps is updated and the best solution refers to the position of the food. Update the exploration parameter: The exploration parameter balances the effective switch between the exploitation and exploration phases. The exploration parameter is defined as (33)where l specifies the current iteration and defines the maximum number of iterations. Update the position of the follower and leader: Once the exploration parameter is updated, the position of the leading and follower salps is updated based on (19) and (32), respectively. The position of the salps is based on the upper and lower bounds of the variables, the position of the salps in the previous iterations, and the position of the food source. End: The iteration is repeated for the maximum number of iterations and the optimal solution to determine the optimal placement of the VMs is computed. Algorithm 1 (see Fig. 3) shows the pseudo code of the proposed Taylor-S