A dynamic evolutionary multi-objective virtual machine placement heuristic for cloud data centers

Torre, Ennio; Durillo, Juan J.; Maio, Vincenzo De; Agrawal, Prateek; Benedict, Shajulin; Saurabh, Nishant; Prodan, Radu

doi:10.1016/j.infsof.2020.106390

Cited by 24 publications

(14 citation statements)

References 9 publications

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“…For solving this optimization problem, an enhanced ACO using adaptive variable set was proposed for balancing its strong searching ability and fast convergence. Torre et al [ 18 ] presented a multi-impartial technique for active VMP that examines the live relocation method for concurrently improving the overcommitment ratio, migration energy, and resource waste. This optimization method utilizes a new evolution meta-experiential approach founded on the key populace method for approximating the Pareto-optimum set of VMP using better diversity and accuracy.…”

Section: Prior Work On Vmp Techniquesmentioning

confidence: 99%

“…For solving this biobjective optimization problem, they presented a joint bin packing heuristic and GA that attains an accurate optimum solution at low time complexity. Reducing the power cost and maintaining the QoS assurance are the two major objectives of this research [ 2 , 18 ]. To effectively tackle this issue, the presented VM merging method reflects the present and upcoming consumption of possessions by the host Underload Detection (UP-PUD) and host Overload Detection (UP-POD) [ 19 , 21 ].…”

Section: Prior Work On Vmp Techniquesmentioning

confidence: 99%

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Chaotic Salp Swarm Optimization-Based Energy-Aware VMP Technique for Cloud Data Centers

Parthiban

Harshavardhan

Neelakandan

et al. 2022

Computational Intelligence and Neuroscience

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The amount of energy required by Cloud Data Centers (CDCs) has increased significantly in this digital age, and as a result, there is a pressing need to reduce CDC energy ingesting. Consolidation of virtual machines (VMs) and effective virtual machine placement (VMP) techniques are commonly employed in large data middles to reduce energy consumption. The VMP is an NP-hard subject with infeasible optimum explanations even for tiny data middles, and it is dealt with using the Metaheuristic Optimization Algorithm, which is an experiential approach to optimization. With this in mind, this study introduces a novel energy-aware VMP technique for CDCs that is founded on the Disordered Salp Swarm Optimization Algorithm (EAVMP-CSSA) and is enhanced for energy efficiency (EAVMP-CSSA). The EAVMP-CSSA technique attempts to reduce CDC energy ingesting by dropping the quantity of active servers supporting virtual machines. The recommended EAVMP-CSSA strategy also aims to balance the resource operation of active servers (i.e., CPU, RAM, and Bandwidth), hence reducing waste and increasing efficiency. Furthermore, by combining the ideas of chaotic maps with the standard Salp Swarm Optimization Algorithm (SSA), the CSSA is intended to improve overall performance and reduce computational costs (SSA). A comprehensive range of experimental analyses are performed to ensure that the EAVMP-CSSA technique performs better, and the findings are compared to current VMP techniques. The EAVMP-CSSA approach achieves an effective outcome with a maximum service rate of 98.12%, whereas the Random, FFD, ACO, and AP-ACO procedures achieve a minimum service rate of 74.40%, 78.80%, 90.70%, and 96.31%, respectively. The experimental results demonstrate that the EAVMP-CSSA approach outperforms other assessment metrics.

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Section: Prior Work On Vmp Techniquesmentioning

confidence: 99%

Section: Prior Work On Vmp Techniquesmentioning

confidence: 99%

Chaotic Salp Swarm Optimization-Based Energy-Aware VMP Technique for Cloud Data Centers

Parthiban

Harshavardhan

Neelakandan

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…They formulated the VM placement problem as a bin packing problem and considered CPU and memory resources in their work. In [16], the authors proposed a multi-objective VM placement algorithm called Island NSGA II. Their goal was trading off energy consumption, resource wastage, and quality of service.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Virtual Machine Placement in Cloud Data Centers: Combinations of Fuzzy Logic with Reinforcement Learning and Biogeography-Based Optimization (BBO) Algorithms

Ghasemi

Haghighat

Keshavarzi

2022

Preprint

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The process of mapping Virtual Machines (VMs) to Physical Ma- chines (PMs), which is deﬁned as VM placement, aﬀects Cloud Data Centers (DCs) performance. To enhance the performance, optimal placement of VMs regarding conﬂicting objectives has been proposed in some research, such as Multi-Objective VM reBalance (MOVMrB) and Reinforcement Learning VM reBalance (RLVMrB) in recent years. The MOVMrB algorithm is based on the BBO meta-heuristic algorithm and the RLVMrB algorithm inspired by reinforcement learning, which in both of them the non-dominance method is used to evaluate generated solutions. Although this approach reaches accept- able results, it fails to consider other solutions which are optimal regarding all objectives, when it meets the best solution based on one of these objectives. In this paper, we propose two enhanced multi-objective algorithms, Fuzzy- RLVMrB and Fuzzy-MOVMrB, that are able to consider all objectives when evaluating candidate solutions in solution space. All four algorithms aim to balance the load between VMs in terms of processor, bandwidth, and memory as well as horizontal and vertical load balance. We simulated all algorithms using the CloudSim simulator and compared them in terms of horizontal and vertical load balance and execution time. The simulation results show that Fuzzy-RLVMrB and Fuzzy-MOVMrB algorithms outperform RLVMrB and MOVMrB algorithms in terms of vertical load balancing and horizontal load balancing. Also, the RLVMrB and Fuzzy-RLVMrB algorithms are better in execution time than the MOVMrB and Fuzzy-MOVMrB algorithms.

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“…Torre et al 37 suggested a multi‐objective strategy for dynamic VMP that makes use of live migration procedures to maximize over‐commitment ratio, resource dissipation, and migration energy all at the same time. Their technique applied a unique evolutionary meta‐heuristic utilizing an island population model to accurately and diversely estimate the Pareto optimum set of VMPs.…”

Section: Vmp In Cloud Computingmentioning

confidence: 99%

“…Their technique applied a unique evolutionary meta‐heuristic utilizing an island population model to accurately and diversely estimate the Pareto optimum set of VMPs. Torre et al 37 showed that single‐solution VMP strategies while enticing to understand, formulate, and apply, were ineffective compared to their multi‐objective methodology. The method approximates the Pareto optimal set and demonstrates unexposed regions of resource losses, over commitment, and live migration tradeoffs utilizing real traces from a Google data center cluster.…”

Section: Vmp In Cloud Computingmentioning

confidence: 99%

Nature‐inspired virtual machine placement mechanisms: A systematic review

Kong

He²,

Abnoosian

2022

Concurrency and Computation

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Cloud data centers do not completely use their resources, resulting in resource underutilization. Cloud computing companies primarily leverage virtualization technologies to supply cost-effective service provision. In order to optimize cloud performance, virtual machines (VMs) must be placed among physical machines (PMs). When it comes to concentrating on the issues in the cloud computing environment, effective VM placement (VMP) is one of the primary difficulties that might cost suppliers money. VMP may be applied in a variety of ways in cloud computing. In terms of lowering related processing overhead, consolidating the cloud environment to become a highly on-demand method, balancing the load among PMs, power usage, and refining performance, VMP techniques still require improvement in the computing environment. This study aims to provide a comprehensive overview of VMP approaches. This article provides an up-to-date survey of the most related VMP literature to highlight study possibilities in cloud settings utilizing nature-inspired metaheuristic algorithms. The findings suggest that placing VMs in the most efficient place saves power usage substantially. The key problem is to minimize data center energy usage without compromising performance or breaking service level agreements. Finally, we will discuss and look at what further may be accomplished in this line of science.

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A dynamic evolutionary multi-objective virtual machine placement heuristic for cloud data centers

Cited by 24 publications

References 9 publications

Chaotic Salp Swarm Optimization-Based Energy-Aware VMP Technique for Cloud Data Centers

Chaotic Salp Swarm Optimization-Based Energy-Aware VMP Technique for Cloud Data Centers

Enhanced Virtual Machine Placement in Cloud Data Centers: Combinations of Fuzzy Logic with Reinforcement Learning and Biogeography-Based Optimization (BBO) Algorithms

Nature‐inspired virtual machine placement mechanisms: A systematic review

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