In recent years, novel mobile applications such as augmented reality, virtual reality, and three‐dimensional gaming, running on handy mobile devices have been pervasively popular. With rapid developments of such mobile applications, decentralized mobile edge computing (MEC) as an emerging distributed computing paradigm is developed for serving them near the smart devices, usually in one hop, to meet their computation, and delay requirements. In the literature, offloading mechanisms are designed to execute such mobile applications in the MEC environments through transferring resource‐intensive tasks to the MEC servers. On the other hand, due to the resource limitations, resource heterogeneity, dynamic nature, and unpredictable behavior of MEC environments, it is necessary to consider the computation offloading issues as the challenging problem in the MEC environment. However, to the best of our knowledge, despite its importance, there is not any systematic, comprehensive, and detailed survey in game theory (GT)‐based computation offloading mechanisms in the MEC environment. In this article, we provide a systematic literature review on the GT‐based computation offloading approaches in the MEC environment in the form of a classical taxonomy to recognize the state‐of‐the‐art mechanisms on this important topic and to provide open issues as well. The proposed taxonomy is classified into four main fields: classical game mechanisms, auction theory, evolutionary game mechanisms, and hybrid‐base game mechanisms. Next, these classes are compared with each other according to the important factors such as performance metrics, case studies, utilized techniques, and evaluation tools, and their advantages and disadvantages are discussed, as well. Finally, open issues and future uncovered or weakly covered research challenges are discussed and the survey is concluded.

A review on the computation offloading approaches in mobile edge computing: A game-theoretic perspective

Sine Cosine Algorithm (SCA) is a recent meta-heuristic algorithm inspired by the proprieties of trigonometric sine and cosine functions. Since its introduction by Mirjalili in 2016, SCA has attracted great attention from researchers and has been widely used to solve different optimization problems in several fields. This attention is due to its reasonable execution time, good convergence acceleration rate, and high efficiency compared to several well-regarded optimization algorithms available in the literature. This paper presents a brief overview of the basic SCA and its variants divided into modified, multi-objective, and hybridized versions. Furthermore, the applications of SCA in several domains such as classification, image processing, robot path planning, scheduling, radial distribution networks, and other engineering problems are described. Finally, the paper recommended some potential future research directions for SCA.

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A comprehensive survey of sine cosine algorithm: variants and applications

Cloud providers offer computing services based on user demands using the Infrastructure as a Service (IaaS) service model. In a cloud data center, it is possible that multiple Virtual Machines (VMs) run on a Physical Machine (PM) using virtualization technology. Virtual Machine Placement (VMP) problem is the mapping of virtual machines across multiple physical ones. This process plays a vital role in defining energy consumption and resource usage efficiency in the cloud data center infrastructure. However, providing an efficient solution is not trivial due to difficulties such as machine heterogeneity, multi-dimensional resources, and large scale cloud data centers. In this paper, we propose an efficient heuristic algorithm that focuses on power consumption and resource wastage optimization to solve the aforementioned problem. The proposed algorithm, called MinPR, minimizes the total power consumption by reducing the number of active physical machines and prioritizing the power-efficient ones. Also, it reduces resource wastage by maximizing and balancing resource utilization among physical machines. To achieve these goals, we propose a new Resource Usage Factor model that manages virtual machine placement on physical machines using reward and penalty mechanisms. Simulations based on cloud user-customized VMs and Amazon EC2 Instances workloads illustrate that the proposed algorithm outperforms existing approaches. In particular, the proposed algorithm reduces total energy consumption by up to 15% for cloud user-customized VMs and by up to 10% for Amazon EC2 Instances.

An energy-efficient algorithm for virtual machine placement optimization in cloud data centers

Efficiency in cloud servers’ power consumption is of paramount importance. Power efficiency makes the reduction in greenhouse gases establishing the concept of green computing. One of the beneficial ways is to apply power-aware methods to decide where to allocate virtual machines (VMs) in data center physical resources. Virtualization is utilized as a promising technology for power-aware VM allocation methods. Since the VM allocation is an NP-complete problem, we use of evolutionary algorithms to solve it. This paper presents an effective micro-genetic algorithm in order to choose suitable destinations between physical hosts for VMs. Our evaluations in simulation environment show that micro-genetic approach provides invaluable improvements in terms of power consumption compared with other methods.

An efficient power-aware VM allocation mechanism in cloud data centers: a micro genetic-based approach

Cloud computing is a new computation technology that provides services to consumers and businesses. The main idea of Cloud computing is to present software and hardware services through the Internet to the users and organizations at all levels. In Cloud computing, the users pay for the services, which means a usage-based payment system is used in this technology. Using virtualization technology in computation resources enables the appropriate utilization of resources in cloud computing. One of the most significant challenging issues in virtualization technology is the placement of optimal virtual machines on physical machines in cloud data centers. The placement of virtual machines comprises a process wherein virtual machines are mapped onto physical machines in cloud data centers. Optimal deployment leads to the reduction in power consumption, optimal use of resources, traffic reduction in data centers, costs reduction, and efficiency enhancement of the data center in the cloud. The present article proposed a new approach using a combination of the Sine–Cosine Algorithm and Salp Swarm Algorithm as discrete multi-objective and chaotic functions for optimal virtual machine placement. The first goal of the proposed algorithm was to reduce the power consumption in cloud data centers by condensing the number of active physical machines. The second goal was to reduce the waste of resources and manage it by optimally virtual machine placement on physical machines in cloud data centers. The third objective was to minimize and reduce Service Level Agreement among the active physical machines in cloud data centers. The proposed method prevent the increase in the migration of virtual machines onto physical machines. Ultimately, the results obtained from the proposed algorithm were compared with those of previous akin algorithms in the literature, including First Fit, Virtual Machine Placement Ant Colony System, and Modified Best Fit Decreasing. The proposed scheme is tested using Amazon EC2 Instances and the result indicated that the proposed algorithm performs better than the existing algorithms for various performance metrics.

Power efficient virtual machine placement in cloud data centers with a discrete and chaotic hybrid optimization algorithm

Energy efficiency is one of the important issues in green cloud data centers (DCs). In this context, a virtual machine (VM) placement is one of the important techniques which can be used to achieve energy efficiency in such environments. Bio-inspired optimization algorithms are widely used in the literature to solve the VM placement (VMP) problem and different types of them are benefited to achieve energy efficiency while meeting Quality of Service (QoS) and user-specified constraints such as deadlines and cost. This paper presents a comprehensive survey and taxonomy of the bio-inspired VMP schemes. For this purpose, we first provide the essential concepts regarding the VMP and describe various objectives and factors which can be considered in this process. Then, we provide a taxonomy of VMP schemes regarding their applied optimization algorithms and compare their employed factors in the VMP process as well as simulator environments and the metrics which have been utilized in the verification of the investigated VMP frameworks. Finally, the concluding remarks and future researches directions are provided.

Bio-inspired virtual machine placement schemes in cloud computing environment: taxonomy, review, and future research directions

Cloud computing is a computing paradigm, where a large pool of systems is connected in private or public networks to provide dynamically scalable infrastructure for application, data, and file storage. With the advent of this technology, the cost of power computation, application hosting, content storage, resource wastage, and delivery is reduced significantly. Cloud computing provides the possibility of merely concentrating on business goals instead of expanding hardware resources for users. Challenging work in virtualization technology is the placement of virtual machines under optimal conditions on physical machines in cloud data centers. Optimal placement of virtual machines over physical ones in cloud data centers can lead to the management of resources and prevention of the resources waste. Hereby, a new approach is proposed based on the combination of the hybrid discrete multi-object whale optimization algorithm, multi-verse optimizer with chaotic functions for optimal placement in the cloud data center. The first object of the proposed algorithm is to decrease power consumption, which is consumed in cloud data centers by reducing active physical machines. The second goal is to cut the resource wastage and managing resources using the optimal placement of virtual machines over physical machines in cloud data centers. With this method, the increasing rate of virtual migration to physical machines is prevented. Finally, the results obtained from the proposed algorithm were compared to some algorithms such as first fit, VMPACS, MBFD.

Virtual machine placement in cloud data centers using a hybrid multi-verse optimization algorithm

Cloud computing, with its immense potentials in low cost and on-demand services, is a promising computing platform for both commercial and non-commercial computation applications. It focuses on the sharing of information and computation in a large network that are quite likely to be owned by geographically disbursed different venders. Power efficiency in cloud data centers (DCs) has become an important topic in recent years as more and larger DCs have been established and the electricity cost has become a major expense for operating them. Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of DCs. Virtual machine (VM) assignment is the key in server consolidation. In the past few years, many methods to VM assignment have been proposed, but existing VM assignment approaches to the VM assignment problem consider the energy consumption by physical machines (PM). In current paper a new approach is proposed that using a combination of the sine cosine algorithm (SCA) and ant lion optimizer (ALO) as discrete multi-objective and chaotic functions for optimal VM assignment. First objective of our proposed model is minimizing the power consumption in cloud DCs by balancing the number of active PMs. Second objective is reducing the resources wastage by using optimal VM assignment on PMs in cloud DCs. Reducing SLA levels was another purpose of this research. By using the method, the number of increase of migration of VMs to PMs is prevented. In this paper, several performance metrics such as resource wastage, power consumption, overall memory utilization, overall CPU utilization, overall storage space, and overall bandwidth, a number of active PMs, a number of shutdowns, a number of migrations, and SLA are used. Ultimately, the results obtained from the proposed algorithm were compared with those of the algorithms used in this regard, including First Fit (FF), VMPACS and MGGA.

A discrete chaotic multi-objective SCA-ALO optimization algorithm for an optimal virtual machine placement in cloud data center

Nowadays cloud computing is progressing very fast and has resulted in advances in other technologies too. Cloud computing provides quite a convenient platform for millions of users to use computing resources through the internet. Cloud computing provides the possibility of only concentrating on business goals instead of expanding hardware resources for users. Using virtualization technology in computing resources results in the efficient use of resources. A challenging work in virtualization technology is the placement of virtual machines under optimal conditions on physical machines in cloud data centers. Optimal placement of virtual machines on physical machines in cloud data centers can lead to the management of resources and prevention of the resources waste. In this paper, a new method is proffered based on the combination of hybrid discrete multi-object sine cosine algorithm and multi-verse optimizer for optimal placement. The first goal of the proposed approach is to decrease the power consumption which is consumed in cloud data centers by reducing active physical machines. The second goal is to cut in resource wastage and managing resources using the optimal placement of virtual machines on physical machines in cloud data centers. With this approach, the increasing rate of virtual migration to physical machines is prevented. Finally, the results gained from our proposed algorithm are compared to some algorithms like the first fit (FF), virtual machine placement ant colony system (VMPACS), modified best fit decreasing (MBFD).

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Sasan Gharehpasha

Papers

Bio-inspired virtual machine placement schemes in cloud computing environment: taxonomy, review, and future research directions

Power efficient virtual machine placement in cloud data centers with a discrete and chaotic hybrid optimization algorithm

Virtual machine placement in cloud data centers using a hybrid multi-verse optimization algorithm

A discrete chaotic multi-objective SCA-ALO optimization algorithm for an optimal virtual machine placement in cloud data center

The Placement of Virtual Machines Under Optimal Conditions in Cloud Datacenter