Power Consumption-Aware Virtual Machine Placement in Cloud Data Center

Portaluri, Giuseppe; Adami, Davide; Gabbrielli, Andrea; Giordano, Stefano; Pagano, Michele

doi:10.1109/tgcn.2017.2725488

Cited by 19 publications

(6 citation statements)

References 32 publications

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“…Portaluri et al 23 exhibited lot of power-aware dynamic allocators for VMs in cloud data centers (DCs) exploiting the software defined networking criterion. They presented 10 diverse allocation techniques and contrast them and a gauge that comprises utilizing the most first available server (first fit).…”

Section: Related Workmentioning

confidence: 99%

Secure virtual machine allocation against attacks using support value based game policy

Naik

Singh

Samaddar

2019

Int J Communication

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Summary Cloud computing, as a supreme computing stage, presents new security challenges. At the point when virtual machines (VMs) are sent in a cloud domain, VM placement strategies can essentially influence the general security dangers of the whole cloud. As of late, the attacks are explicitly intended to co‐locate with target VMs in the cloud. The VM placement without considering the security risks may put the clients, or even the whole cloud, in threat. Therefore, here we prompt an exhaustive approach to appraise the cloud environment security risk from network, host, and VM. In like manner, secure VM allocation against co‐resident attacks using support value based game policy is proposed that lessens the general security dangers of a cloud, while decides the multi‐objective measures such as efficiency, coverage, and the maximum link utilization. Then, the support value is calculated for the given multi‐objective values. At last, the game theory is used to allocate the best VM. The experimental results prove that the proposed method outperforms than the existing methods in regards of efficiency, coverage maximum link utilization, execution time, throughput, latency, workload balance, power consumption, and standard deviation.

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Section: Related Workmentioning

confidence: 99%

Secure virtual machine allocation against attacks using support value based game policy

Naik

Singh

Samaddar

2019

Int J Communication

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“…However, to be more effective, fog tasks should be placed on appropriate Physical Machines (PMs) using a process called VM placement [12,13] shown in Figure 1. Proper placement of VMs is very important in the cloud environment to effectively improve the power effectiveness and resource utilization in the edge-cloud infrastructure [14,15]. However, inefficient VM placement can increase the number of active PMs, which leads to more energy consumption and network traffic [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…onlooker =N employed * P i +1(14) In this equation, each onlooker bee represents a solution where indicates the placement of each VM on a PM. Equation 15 provides a more elaborate version of equation 6, in which F 1 , F 2, and F 3 parameters are replaced by equations 7, 11, and 13, respectively.…”

mentioning

confidence: 99%

“…Proper placement of VMs is very important in the cloud environment to effectively improve the power effectiveness and resource utilization in the edge-cloud infrastructure. 14,15 However, inefficient VMP can increase the number of active PMs, which leads to more energy consumption and network traffic. 16,17 To prevent these problems, numerous researches have been conducted to find optimal VMP on data centers' PMs.…”

mentioning

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

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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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“…Use a heuristic algorithm to optimize network performance and reduce the energy consumption of PMs and network elements[55] -Use multiple resources best fit and worst fit policies taking into account VMs' CPU, RAM, disk and bandwidth[138] -Heuristic algorithms based on PMs fault-aware scheduling[154] …”

mentioning

confidence: 99%

Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

2018

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Data center power consumption is among the largest commodity expenditures for many organizations. Reduction of power used in cloud data centres with heterogeneous physical resources can be achieved through Virtual-Machine (VM) consolidation which reduces the number of Physical Machines (PMs) used, subject to Quality of Service (QoS) constraints. This paper provides an in-depth survey of the most recent techniques and algorithms used in proactive dynamic VM consolidation focused on energy consumption. We present a general framework that can be used on multiple phases of a complete consolidation process.

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Power Consumption-Aware Virtual Machine Placement in Cloud Data Center

Cited by 19 publications

References 32 publications

Secure virtual machine allocation against attacks using support value based game policy

Secure virtual machine allocation against attacks using support value based game policy

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

Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

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