Particle Swarm Optimization for Energy-Aware Virtual Machine Placement Optimization in Virtualized Data Centers

Wang, Shangguang; Liu, Zhipiao; Zheng, Zibin; Sun, Qibo; Yang, Fangchun

doi:10.1109/icpads.2013.26

Cited by 102 publications

(44 citation statements)

References 16 publications

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“…Only the first fit instances that satisfy all the constraints (C.2) to (C.4) are eligible for the objective function evaluation stage. A similar positioning approach has been utilized in [20] the during backfilling operation.…”

Section: B Combinations Of Pfm and Fsm Methodsmentioning

confidence: 99%

Resource allocation for cloud-based social TV applications using Particle Swarm Optimization

Kulupana

Talagala

Arachchi

et al. 2015

2015 IEEE International Conference on Communications (ICC)

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Abstract-Social interaction of groups of users, amongst themselves and with the media content itself, is increasingly becoming popular due to the advancements in the Internet access technologies. However, multimedia resource provisioning for dispersed user groups poses a challenge and demands innovative technologies. This paper proposes a novel approach based on Particle Swarm Optimization (PSO) to optimally allocate computational and networking resources to a group of interactive users, such that the group Quality-of-Service (QoS) is maximized. We evaluate the performance of the proposed improved PSO method with respect to the state-of-the-art greedy resource allocation mechanisms and related PSO approaches. The ability to find a feasible solution (i.e., the serving probability) and the accuracy of such solutions are compared for different network topologies. The proposed method demonstrates reduced computational complexity, an up to 40% increase in the serving probability compared to the greedy methods, and up to 60 times faster convergence compared to the basic PSO approach. Overall, the comparable QoS level to the optimal solution suggests that the proposed solution efficiently allocates the resources available in the network.Index Terms-Cloud computing, data center location, interactive television, particle swarm optimization, Quality-ofService, resource optimization, social multimedia applications, video distribution.

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Section: B Combinations Of Pfm and Fsm Methodsmentioning

confidence: 99%

Resource allocation for cloud-based social TV applications using Particle Swarm Optimization

Kulupana

Talagala

Arachchi

et al. 2015

2015 IEEE International Conference on Communications (ICC)

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“…VM Scheduling Strategy Based On SA_PSO Particle Swarm Optimization PSO is an intelligent optimization algorithm that is based on swarm intelligence and was first introduced by Eberhart and Kennedy in 1995 [10]. PSO is used for solving optimization problems that inspired from the characteristics of population behavior, each particle represents a potential solution and represented by velocity and current position.…”

Section: Model Formulationmentioning

confidence: 99%

“…But it also has some defects. Such as premature convergence, search accuracy and the late iterative efficiency are not high [10]. In order to improve the global convergence of the PSO, we have improved the original PSO as the following:…”

Section: Self-adaptive Particle Swarm Optimizationmentioning

confidence: 99%

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Virtual Machine Scheduling to Minimize Resource Wastage in Cloud Computing Environment

Zhu¹,

Chen²

2016

Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering

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Abstract. Virtual machine scheduling is the process of selecting the most suitable server in cloud data centers to deploy newly created VMs. The optimal placement is important for improving resource utilization and reducing resource wastage in a cloud computing environment. In this article, we propose an Self-Adaptive Particle Swarm Optimization for the virtual machine scheduling problem. Our algorithm focuses on efficient VM allocation to physical servers in order to minimize the total resource wastage and the number of servers used. Simulation experiments were designed to evaluate the proposed algorithm with performance and scalability. Its solution performance are compared with PSO and GPSO scheduling strategies. The results show that the proposed algorithm is more efficient and effective than the methods we compared to.

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“…Proposed approach significantly reduces energy by 13-23% as compared to other heuristic approaches [50] In [52], a predictive design which aims to combine the machine learning clustering and stochastic theory to estimate both the number of VM requests and the amount of cloud resources associated with each request is formulated. An amalgamated resource conditioning framework depending upon this method has been used by the authors to make suitable energy-aware resource supervised decisions which is further evaluated using real Google traces collected over a 29-day period from a Google cluster containing over 12,500 PMs.…”

Section: Java Based Simulatormentioning

confidence: 99%

Resource Scheduling in Cloud Environmet: A Survey

Mangla¹,

Singh²,

Rana³

2016

Adv. Sci. Technol. Res. J.

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Cloud Computing offers the avant-garde services at a stretch that are too attractive for any cloud user to ignore. With its growing application and popularization, IT companies are rapidly deploying distributed data centers globally, posing numerous challenges in terms of scheduling of resources under different administrative domains. This perspective brings out certain vital factors for efficient scheduling of resources providing a wide genre of characteristics, diversity in context of level of service agreements and that too with user-contingent elasticity. In this paper, a comprehensive survey of research related to various aspects of cloud resource scheduling is provided. A comparative analysis of various resource scheduling techniques focusing on key performance parameters like Energy efficiency, Virtual Machine allocation and migration, Cost-effectiveness and Service-Level Agreement is also presented.

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Particle Swarm Optimization for Energy-Aware Virtual Machine Placement Optimization in Virtualized Data Centers

Cited by 102 publications

References 16 publications

Resource allocation for cloud-based social TV applications using Particle Swarm Optimization

Resource allocation for cloud-based social TV applications using Particle Swarm Optimization

Virtual Machine Scheduling to Minimize Resource Wastage in Cloud Computing Environment

Resource Scheduling in Cloud Environmet: A Survey

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