Journal Article10.1007/S11771-017-3645-Z
Energy-efficient virtual machine consolidation algorithm in cloud data centers
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TL;DR: Experimental results show that, in contrast to other energy-saving algorithms, the algorithm proposed in this work significantly reduces the energy consumption and maintains low service level agreement (SLA) violations.
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Abstract: Cloud data centers consume a multitude of power leading to the problem of high energy consumption. In order to solve this problem, an energy-efficient virtual machine (VM) consolidation algorithm named PVDE (prediction-based VM deployment algorithm for energy efficiency) is presented. The proposed algorithm uses linear weighted method to predict the load of a host and classifies the hosts in the data center, based on the predicted host load, into four classes for the purpose of VMs migration. We also propose four types of VM selection algorithms for the purpose of determining potential VMs to be migrated. We performed extensive performance analysis of the proposed algorithms. Experimental results show that, in contrast to other energy-saving algorithms, the algorithm proposed in this work significantly reduces the energy consumption and maintains low service level agreement (SLA) violations.
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Citations
An improved genetic algorithm using greedy strategy toward task scheduling optimization in cloud environments
TL;DR: A novel algorithm named MGGS (modified genetic algorithm (GA) combined with greedy strategy) is proposed that can find an optimal solution using fewer number of iterations to optimize task scheduling process.
205
Reinforcement learning based methodology for energy-efficient resource allocation in cloud data centers
TL;DR: A framework which can show effective performance for achieving the high data center energy efficiency and preventing Service Level Agreement (SLA) violation respectively with the aim of green cloud resources deployment is proposed.
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Type-aware virtual machine management for energy efficient cloud data centers
TL;DR: A distributed approach to an energy-efficient dynamic virtual machine consolidation mechanism that determines, based on novel algorithms, which virtual machines to migrate, and when, and the results of the performance evaluation demonstrate that the proposed new algorithms are able to enhance the energy efficiency in cloud data centers.
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Virtualization and consolidation: a systematic review of the past 10 years of research on energy and performance
TL;DR: All previous surveys on the subject of virtual machine consolidation are summarized and updates them with the most recent papers in the field and proposes a categorization that classifies the most important research on performance and energy in consolidated systems.
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Virtual machine migration algorithm for energy efficiency optimization in cloud computing
TL;DR: Under considering CPU and memory factors, the key three steps for EEOM algorithm, including trigger time, VM selection, and host location, are optimized and show that the algorithm saves 7% energy consumption and reduces 13% SLA violations.
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References
CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms
TL;DR: The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns.
5.3K
Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing
TL;DR: An architectural framework and principles for energy-efficient Cloud computing are defined and the proposed energy-aware allocation heuristics provision data center resources to client applications in a way that improves energy efficiency of the data center, while delivering the negotiated Quality of Service (QoS).
2.8K
The Case for Energy-Proportional Computing
Luiz Andre Barroso,Urs Hölzle +1 more
TL;DR: Energy-proportional designs would enable large energy savings in servers, potentially doubling their efficiency in real-life use, particularly the memory and disk subsystems.
Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: Challenges and opportunities
Rajkumar Buyya,Rajiv Ranjan,Rodrigo N. Calheiros +2 more
- 21 Jun 2009
TL;DR: CloudSim as mentioned in this paper is an extensible simulation toolkit that enables modelling and simulation of cloud computing environments, and it supports the creation of one or more virtual machines (VMs) on a simulated node of a Data Center, jobs, and their mapping to suitable VMs.
1.1K
Power and performance management of virtualized computing environments via lookahead control
TL;DR: This work implements and validate a dynamic resource provisioning framework for virtualized server environments wherein the provisioning problem is posed as one of sequential optimization under uncertainty and solved using a lookahead control scheme.
964