Virtual Machine Placement Using JAYA Optimization Algorithm

Reddy, M. Amarendhar; Ravindranath, K.

doi:10.1080/08839514.2019.1689714

Cited by 16 publications

(9 citation statements)

References 20 publications

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“…Paper 47 put each virtual machine on one server only. Also, each virtual machine was placed on a server so that for each server resource, not all virtual machine resource requests exceed the available resources.…”

Section: Physical Machine Selection (Vm Placement) Methodsmentioning

confidence: 99%

“…Also, each virtual machine was placed on a server so that for each server resource, not all virtual machine resource requests exceed the available resources. 47 Paper 1 used an improved genetic algorithm for reducing energy consumption and performing VMs scheduling. Hence, the servers did not become overloaded or under-loaded.…”

Section: Physical Machine Selection (Vm Placement) Methodsmentioning

confidence: 99%

See 1 more Smart Citation

An autonomous model for self‐optimizing virtual machine selection by learning automata in cloud environment

2021

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In recent years, cloud computing has become more popular because of advancements in virtualization technology. By increasing the number of servers in cloud computing environment, cloud data centers have expanded and consumed much energy. Virtual machine consolidation is a solution for energy management in cloud environment. On the other hand, by increasing resource utilization in virtual machine consolidation, service level agreement assurance is difficult to obtain. Two main challenges in virtual machine consolidation are timely detection of overloaded servers and proper immigrant virtual machine selection from detected servers. In this paper, a new model is proposed based on MAPE-k loop for autonomous virtual machine selection. The presented model uses a proposed ensemble prediction algorithm in the analysis phase. Also, in the planning phase, a new multi-heuristics algorithm with flexible weights using learning automata is proposed. The effectiveness of the proposed model is evaluated by CloudSim simulator under real workload as compared with well-known algorithms in this domain. The experimental results indicate that, the proposed approach has averagely improved the balance between service level agreement violations, energy and migration counts by 47.39% compared to other methods.

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Section: Physical Machine Selection (Vm Placement) Methodsmentioning

confidence: 99%

Section: Physical Machine Selection (Vm Placement) Methodsmentioning

confidence: 99%

An autonomous model for self‐optimizing virtual machine selection by learning automata in cloud environment

2021

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“…A performance-cost grey wolf optimization (PCGWO) algorithm has been proposed to reduce the processing time and cost of tasks [21]. A JAYA algorithm has been used to optimize VM placement and minimize the energy consumption [22]. A fair resource allocation method has been proposed to rapidly and fairly allocate resources and maximize the resource utilization via a flow control policy [23].…”

Section: Related Workmentioning

confidence: 99%

A proactive resource allocation method based on adaptive prediction of resource requests in cloud computing

Chen

Wang

Liu

2021

J Wireless Com Network

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With the development of big data and artificial intelligence, cloud resource requests present more complex features, such as being sudden, arriving in batches and being diverse, which cause the resource allocation to lag far behind the resource requests and an unbalanced resource utilization that wastes resources. To solve this issue, this paper proposes a proactive resource allocation method based on the adaptive prediction of the resource requests in cloud computing. Specifically, this method first proposes an adaptive prediction method based on the runs test that improves the prediction accuracy of resource requests, and then, it builds a multiobjective resource allocation optimization model, which alleviates the latency of the resource allocation and balances the utilizations of the different types of resources of a physical machine. Furthermore, a multiobjective evolutionary algorithm, the Nondominated Sorting Genetic Algorithm with the Elite Strategy (NSGA-II), is improved to further reduce the resource allocation time by accelerating the solution speed of the multiobjective optimization model. The experimental results show that this method realizes the balanced utilization between the CPU and memory resources and reduces the resource allocation time by at least 43% (10 threads) compared with the Improved Strength Pareto Evolutionary algorithm (SPEA2) and NSGA-II methods.

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“…It is adaptable, flexible, and sound-and-complete. Therefore, JAYA algorithm has been tremendously utilized for a plethora of optimization problems in different domains such as Optimal Power Flow [47,48], parameters extraction of solar cells [49], knapsack problems [50], virtual machine placement [51], job shop scheduling [52,53], permutation flow-shop scheduling problem [54], reliability-redundancy allocation problems [55], team formation problem [56], truss structures [57], facial emotion recognition [58], feature selection [59], Plate-fin heat exchanger [60], estimating Li-ion battery model parameters [61], etc.…”

Section: Introductionmentioning

confidence: 99%

An Intensive and Comprehensive Overview of JAYA Algorithm, its Versions and Applications

Zitar

Al-Betar

Awadallah

et al. 2021

Arch Computat Methods Eng

112

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In this review paper, JAYA algorithm, which is a recent population-based algorithm is intensively overviewed. The JAYA algorithm combines the survival of the fittest principle from evolutionary algorithms as well as the global optimal solution attractions of Swarm Intelligence methods. Initially, the optimization model and convergence characteristics of JAYA algorithm are carefully analyzed. Thereafter, the proposed versions of JAYA algorithm have been surveyed such as modified, binary, hybridized, parallel, chaotic, multi-objective and others. The various applications tackled using relevant versions of JAYA algorithm are also discussed and summarized based on several problem domains. Furthermore, the open sources code of JAYA algorithm are identified to provide enrich resources for JAYA research communities. The critical analysis of JAYA algorithm reveals its advantages and limitations in dealing with optimization problems. Finally, the paper ends up with conclusion and possible future enhancements suggested to improve the performance of JAYA algorithm. The reader of this overview will determine the best domains and applications used by JAYA algorithm and can justify their JAYA-related contributions.

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Virtual Machine Placement Using JAYA Optimization Algorithm

Cited by 16 publications

References 20 publications

An autonomous model for self‐optimizing virtual machine selection by learning automata in cloud environment

An autonomous model for self‐optimizing virtual machine selection by learning automata in cloud environment

A proactive resource allocation method based on adaptive prediction of resource requests in cloud computing

An Intensive and Comprehensive Overview of JAYA Algorithm, its Versions and Applications

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