Nature inspired meta-heuristic algorithms for solving the load-balancing problem in cloud environments

Milan, Sara Tabaghchi; Rajabion, Lila; Ranjbar, Hamideh; Navimipour, Nima Jafari

doi:10.1016/j.cor.2019.05.022

Cited by 87 publications

(43 citation statements)

References 94 publications

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“…Load balancing is a fundamental problem in distributed systems and cloud computing environments [1][2][3]. The aim of load balancing is to distribute workloads to a set of reliable servers in the system.…”

Section: Introductionmentioning

confidence: 99%

A Load Balancing Algorithm for Mobile Devices in Edge Cloud Computing Environments

Lim

Lee

2020

Electronics

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As current data centers and servers are growing in size by orders of magnitude when needed, load balancing is a great concern in scalable computing systems, including mobile edge cloud computing environments. In mobile edge cloud computing systems, a mobile user can offload its tasks to nearby edge servers to support real-time applications. However, when users are located in a hot spot, several edge servers can be overloaded due to suddenly offloaded tasks from mobile users. In this paper, we present a load balancing algorithm for mobile devices in edge cloud computing environments. The proposed load balancing technique features an efficient complexity by a graph coloring-based implementation based on a genetic algorithm. The aim of the proposed load balancing algorithm is to distribute offloaded tasks to nearby edge servers in an efficient way. Performance results show that the proposed load balancing algorithm outperforms previous techniques and increases the average CPU usage of virtual machines, which indicates a high utilization of edge servers.

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Section: Introductionmentioning

confidence: 99%

A Load Balancing Algorithm for Mobile Devices in Edge Cloud Computing Environments

Lim

Lee

2020

Electronics

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“…Recently, the significance of inexpensive access to dependable high‐performance hardware and software resources as well as evading upkeep costs have encouraged big stakeholders of information technology companies and institution managers to journey to cloud computing . Cloud computing is being altered to a model containing services that are delivered in a way similar to old‐style utilities such as electricity, water, gas, and telephone . Cloud computing represents both the applications offered as services over the Internet and the hardware and systems software in the data centers .…”

Section: Introductionmentioning

confidence: 99%

“…1,2 Cloud computing is being altered to a model containing services that are delivered in a way similar to old-style utilities such as electricity, water, gas, and telephone. 3,4 Cloud computing represents both the applications offered as services over the Internet and the hardware and systems software in the data centers. 5,6 There are three basic types of cloud services, which are Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).…”

Section: Introductionmentioning

confidence: 99%

A cloud service composition method using a trust‐based clustering algorithm and honeybee mating optimization algorithm

Zanbouri

Navimipour

2019

Int J Communication

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Summary One of the most critical issues in using service‐oriented technologies is the combination of services, which has become an important challenge in the present. There are some significant challenges in the service composition, most notable is the quality of service (QoS), which is more challenging due to changing circumstances in dynamic service environments. Also, trust value in the case of selection of more reliable services is another challenge in the service composition. Due to NP‐hard complexity of service composition, many metaheuristic algorithms have been used so far. Therefore, in this paper, the honeybee mating optimization algorithm as one of the powerful metaheuristic algorithms is used for achieving the desired goals. To improve the QoS, inspirations from the mating stages of the honeybee, the interactions between honeybees and queen bee mating and the selection of the new queen from the relevant optimization algorithm have been used. To address the trust challenge, a trust‐based clustering algorithm has also been used. The simulation results using C# language have shown that the proposed method in small scale problem acts better than particle swarm optimization algorithm, genetic algorithm, and discrete gbest‐guided artificial bee colony algorithm. With the clustering and reduction of the search space, the response time is improved; also, more trusted services are selected. The results of the simulation on a large‐scale problem have indicated that the proposed method is exhibited worse performance than the average results of previous works in computation time.

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“…[10][11][12] In the cloud data centers, scalability and complexity issues cause cost and energy become two key challenges that need attention and careful study. 1-4 It considers the ideas of distributed and parallel computing to offer on-demand computing resources for computers or other devices.…”

Section: Introductionmentioning

confidence: 99%

“…5-9 It aims to offer shared services with high reliability, dynamicity, and scalability; although, it is facing many issues because of its complex architecture. [10][11][12] In the cloud data centers, scalability and complexity issues cause cost and energy become two key challenges that need attention and careful study. 13,14 As the carbon footprint and consumed energy of the cloud infrastructures have been increased, investigators tried to find applicable methods for decreasing energy consumption.…”

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confidence: 99%

An energy‐aware method for data replication in the cloud environments using a Tabu search and particle swarm optimization algorithm

Ebadi

Navimipour

2018

Concurrency and Computation

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Cloud computing is a type of parallel, configurable, and flexible system, which refers to the provision of applications on virtual data centers. However, reducing the energy consumption and also maintaining high computation capacity have become timely and important challenges. The concept of replication is used to face these challenges. By increasing the number of data replicas, the energy consumption, the performance, and also the cost of creating and maintaining new replicas also are increased. Deciding on the number of required replicas and their location on the cloud system is an NP-hard problem. In this paper, the problem is formulated as an optimization problem and a hybrid metaheuristic algorithm is offered to solve it. The algorithm uses the global search capability of the Particle Swarm Optimization (PSO) algorithm and the local search capability of the Tabu Search (TS) to get high-quality solutions. The efficiency of the method is shown by comparing it with simple PSO, TS, and Ant Colony Optimization (ACO) algorithm on different test cases. The obtained results indicate that the method outperforms all of them in terms of consumed energy and cost. KEYWORDS cloud computing, data replication, particle swarm optimization, replica INTRODUCTIONCloud computing is a deep revolution in providing the information technology-based services based on virtualization technology. 1-4 It considers the ideas of distributed and parallel computing to offer on-demand computing resources for computers or other devices. 5-9 It aims to offer shared services with high reliability, dynamicity, and scalability; although, it is facing many issues because of its complex architecture. [10][11][12] In the cloud data centers, scalability and complexity issues cause cost and energy become two key challenges that need attention and careful study. 13,14 As the carbon footprint and consumed energy of the cloud infrastructures have been increased, investigators tried to find applicable methods for decreasing energy consumption. 15 In the last few years, the main focus of investigators and engineers was on improving the performance in terms of energy, space, and cost. 1

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Nature inspired meta-heuristic algorithms for solving the load-balancing problem in cloud environments

Cited by 87 publications

References 94 publications

A Load Balancing Algorithm for Mobile Devices in Edge Cloud Computing Environments

A Load Balancing Algorithm for Mobile Devices in Edge Cloud Computing Environments

A cloud service composition method using a trust‐based clustering algorithm and honeybee mating optimization algorithm

An energy‐aware method for data replication in the cloud environments using a Tabu search and particle swarm optimization algorithm

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