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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 6/2015

01.08.2015 | Original Article

A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing

verfasst von: Keng-Mao Cho, Pang-Wei Tsai, Chun-Wei Tsai, Chu-Sing Yang

Erschienen in: Neural Computing and Applications | Ausgabe 6/2015

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Abstract

Virtual machine (VM) scheduling with load balancing in cloud computing aims to assign VMs to suitable servers and balance the resource usage among all of the servers. In an infrastructure-as-a-service framework, there will be dynamic input requests, where the system is in charge of creating VMs without considering what types of tasks run on them. Therefore, scheduling that focuses only on fixed task sets or that requires detailed task information is not suitable for this system. This paper combines ant colony optimization and particle swarm optimization to solve the VM scheduling problem, with the result being known as ant colony optimization with particle swarm (ACOPS). ACOPS uses historical information to predict the workload of new input requests to adapt to dynamic environments without additional task information. ACOPS also rejects requests that cannot be satisfied before scheduling to reduce the computing time of the scheduling procedure. Experimental results indicate that the proposed algorithm can keep the load balance in a dynamic environment and outperform other approaches.
Vorheriger Artikel Hybrid affective computing—keyboard, mouse and touch screen: from review to experiment
Nächster Artikel Integration of artificial neural network and geographical information system for intelligent assessment of land suitability for the cultivation of a selected crop

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Fußnoten
1
The source code of the proposed algorithm and all of the test datasets are available for download at http://​itlab.​ee.​ncku.​edu.​tw/​ACOPS_​Program_​and_​TestData.​rar and http://​goo.​gl/​6Upnw5.
 
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Metadaten
Titel
A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing
verfasst von
Keng-Mao Cho
Pang-Wei Tsai
Chun-Wei Tsai
Chu-Sing Yang
Publikationsdatum
01.08.2015
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 6/2015
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-014-1804-9

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