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

Gharehpasha, Sasan; Màsdàrí, Mohàmmàd; Jafarian, Ahmad

doi:10.1007/s10586-020-03187-y

Cited by 38 publications

(16 citation statements)

References 57 publications

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“…The projected cooptimization procedure purposes to allocate task for the VM that has the minimum performance cost within the fixed limits and later deploy the chosen VM on maximum used PH within capability limit. Gharehpasha et al [ 14 ] projected a novel method with an integration of the SCA and SSA as separate multiobjective and disordered function for a best VMP. The initial purpose of the projected method is to decrease the control utilization in CDCs by reducing the number of lively PMs.…”

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

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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%

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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“…A combination of multiobjective Sine-Cosine Algorithm and Salp Swarm Algorithm is used by Gharehpasha et al [23] for efficient VM allocation and their objectives are to decrease power consumption, waste of resources, and SLA violations. e authors compared results with Ant Colony System, First Fit, and Modified Best Fit Decreasing algorithm which are existing ones.…”

Section: Related Workmentioning

confidence: 99%

Intelligent SLA-Aware VM Allocation and Energy Minimization Approach with EPO Algorithm for Cloud Computing Environment

Samriya

Patel

Khurana

et al. 2021

Mathematical Problems in Engineering

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Cloud computing is the most prominent established framework; it offers access to resources and services based on large-scale distributed processing. An intensive management system is required for the cloud environment, and it should gather information about all phases of task processing and ensuring fair resource provisioning through the levels of Quality of Service (QoS). Virtual machine allocation is a major issue in the cloud environment that contributes to energy consumption and asset utilization in distributed cloud computing. Subsequently, in this paper, a multiobjective Emperor Penguin Optimization (EPO) algorithm is proposed to allocate the virtual machines with power utilization in a heterogeneous cloud environment. The proposed method is analyzed to make it suitable for virtual machines in the data center through Binary Gravity Search Algorithm (BGSA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). To compare with other strategies, EPO is energy-efficient and there are significant differences. The results of the proposed system have been evaluated through the JAVA simulation platform. The exploratory outcome presents that the proposed EPO-based system is very effective in limiting energy consumption, SLA violation (SLAV), and enlarging QoS requirements for giving capable cloud service.

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“…In [34], Gharehpasha et al proposed a novel approach using a combination of the Sine-Cosine Algorithm and Swarm Algorithm for virtual machine placement. In the proposed method, reducing power consumption and resource wastage are considered by reducing the number of active physical machines.…”

Section: Multi-objective Based Vm Placement Policiesmentioning

confidence: 99%

“…In this paper, a multiobjective plan for VMP is implemented, whose objective is to minimize PM power consumption and ensure VM performance. In their work, 31 Gharehpasha et al have presented a new model of the ant colony scheme (ACS) so that energy usage in cloud data centers is lowered. To examine the efficiency of the proposed algorithm, various simulation-based experiments have been conducted on small, medium, and large scale data centers.…”

Section: Power and Energy Efficiency-based Vmp Policiesmentioning

confidence: 99%

“…After that, they provide a two-level runtime reconfiguration method to include local adjustment and VM parallel migration and satisfy the migration of VM and total migration time. In another work, 31 Gharehpasha et al concentrated on how to solve the VMP problem regarding the existing bandwidth being formulated as a variable-sized bin packing problem. Also, a novel bandwidth allocation approach is engendered and combined with an optimized whale optimization algorithm variant, which is called an improved levy-based whale optimization algorithm.…”

Section: Multi-objective-based Vmp Policiesmentioning

confidence: 99%

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TRACTOR: Traffic‐aware and power‐efficient virtual machine placement in edge‐cloud data centers using artificial bee colony optimization

Nabavi

Gill

et al. 2021

Int J Communication

Self Cite

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Technology providers heavily exploit the usage of Edge-Cloud Data Centers (ECDC) to meet user demand while the ECDC are large energy consumers. Concerning the decrease of the energy expenditure of ECDCs, task placement is one of the most prominent solutions for effective allocation and consolidation of such tasks onto physical machine (PM). Such allocation must also consider additional optimizations beyond power and must include other objectives, including network-traffic effectiveness. In this study, we present a multi-objective Virtual Machine (VM) placement scheme (considering VMs as fog tasks) for ECDCs called TRACTOR, which utilizes an Artificial Bee Colony optimization algorithm for power and network-aware assignment of VMs onto PMs. The proposed scheme aims to minimize the network traffic of the interacting VMs and the power dissipation of the data center's switches and physical machines. To evaluate the proposed VM placement solution, the VL2 (Virtual Layer 2) and three-tier network topologies are modeled and integrated into the CloudSim toolkit to justify the effectiveness of the proposed solution in mitigating the network traffic and power consumption of the ECDC. Results indicate that our proposed method is able to reduce power energy consumption by 3.5% whilst decreasing network traffic and power by 15% and 30%, respectively, without affecting other Quality of Service parameters.

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

Cited by 38 publications

References 57 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

Intelligent SLA-Aware VM Allocation and Energy Minimization Approach with EPO Algorithm for Cloud Computing Environment

TRACTOR: Traffic‐aware and power‐efficient virtual machine placement in edge‐cloud data centers using artificial bee colony optimization

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