Adaptive Management and Multi-Objective Optimization for Virtual Machine Placement in Cloud Computing

Li, Qiang; Hao, Qinfen; Xiao, Limin; Li, Zhoujun

doi:10.3724/sp.j.1016.2011.02253

Cited by 31 publications

(20 citation statements)

References 6 publications

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“…Luo et al 2014;Song et al 2013;Tian et al 2014) only focused on physical machines and cannot provide VM-level power estimating. Power models are frequently used in evaluating VM allocation/migration algorithms like Li et al (2011) as well as virtual resource scheduling (Beloglazov et al 2012;Zhao et al 2014;Lin et al 2014). To design a power estimate module, programmers prefer to use concise and feasible power models with fewer parameters.…”

Section: Introductionmentioning

confidence: 99%

An intelligent power consumption model for virtual machines under CPU-intensive workload in cloud environment

Lin

Peng

2016

Soft Comput

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Cloud computing has gained enormous popularity by providing high availability and scalability as well as on-demand services. However, with the continuous rise of energy consumption cost, the virtualized environment of cloud data centers poses a challenge to today's power monitoring system. Software-based power monitoring is gaining prevalence since power models can work precisely by exploiting soft computing methodologies like genetic programming and swarm intelligence for model optimization. However, traditional power models barely consider virtualization and have drawbacks like high error rate, low feasibility as well as insufficient scalability. In this paper, we first analyze the power signatures of virtual machines in different configurations through experiments. Then we propose a virtual machine (VM) power model, named CAM, which is able to adapt to the reconfiguration of VMs and provide accurate power estimating under CPU-intensive workload. We also propose two training methodologies corresponding to two typical situations for model training. CAM can estimate the power of a single VM as well as a physical server hosting several heterogeneous VMs. We exploited public Linux benchmarks to evaluate CAM .The experimental results show that CAM produced very small errors in power estimating for both VMs (4.26 % on average) and the host server (0.88 % on average).

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Section: Introductionmentioning

confidence: 99%

An intelligent power consumption model for virtual machines under CPU-intensive workload in cloud environment

Lin

Peng

2016

Soft Comput

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“…The results of performance are compared with those of other heuristic algorithms, which can objectively show how good or bad our proposed strategy is based on BPSO. In addition to these heuristic algorithms, we use the idea of GA algorithm in articles [10,11] and design the experiments to compare it with our proposed algorithm. In this paper, all experiments are conducted on the same computer, whose processor is AMD A6-3400-m APU with Radeon HD Graphics, memory is 6 G, and operating system is Windows 7 Professional SP1, and simulation experiments are conducted using the platform of eclipse, whose version is Mars.1 Release (4.5.1).…”

Section: Methodsmentioning

confidence: 99%

“…Xu and Fortes [10] have used genetic algorithm to solve the problem of VM placement, but this method does not consider the overhead of VM migration, so it does not have good practicability. Li et al [11] have proposed a strategy for VM placement that is based on multiobjective genetic algorithm; however the strategy cannot be applied to solve the problem of power consumption. In addition, it does not combine resource control and energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Energy-Aware VM Initial Placement Strategy Based on BPSO in Cloud Computing

Xiong

Zhao

Wang

et al. 2018

Scientific Programming

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In recent years, high energy consumption has gradually become a prominent problem in a data center. With the advent of cloud computing, computing and storage resources are bringing greater challenges to energy consumption. Virtual machine (VM) initial placement plays an important role in affecting the size of energy consumption. In this paper, we use binary particle swarm optimization (BPSO) algorithm to design a VM placement strategy for low energy consumption measured by proposed energy efficiency fitness, and this strategy needs multiple iterations and updates for VM placement. Finally, the strategy proposed in this paper is compared with other four strategies through simulation experiments. The results show that our strategy for VM placement has better performance in reducing energy consumption than the other four strategies, and it can use less active hosts than others.

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“…With the development and progress of grid computing, service-oriented and virtualization technology, cloud computing [1] received strong support and promote in the theory and technology, gradually becoming the focus of attention and the future development trend of computing models.…”

Section: Introductionmentioning

confidence: 99%