Probabilistic Consolidation of Virtual Machines in Self-Organizing Cloud Data Centers
Power efficiency is one of the main issues that will drive the design of data centers, especially of those devoted to provide Cloud computing services. In virtualized data centers, consolidation of Virtual Machines (VMs) on the minimum number of physical servers has been recognized as a very efficie...
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Published in | IEEE transactions on cloud computing Vol. 1; no. 2; pp. 215 - 228 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Piscataway
IEEE Computer Society
01.07.2013
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | Power efficiency is one of the main issues that will drive the design of data centers, especially of those devoted to provide Cloud computing services. In virtualized data centers, consolidation of Virtual Machines (VMs) on the minimum number of physical servers has been recognized as a very efficient approach, as this allows unloaded servers to be switched off or used to accommodate more load, which is clearly a cheaper alternative to buy more resources. The consolidation problem must be solved on multiple dimensions, since in modern data centers CPU is not the only critical resource: depending on the characteristics of the workload other resources, for example, RAM and bandwidth, can become the bottleneck. The problem is so complex that centralized and deterministic solutions are practically useless in large data centers with hundreds or thousands of servers. This paper presents ecoCloud, a self-organizing and adaptive approach for the consolidation of VMs on two resources, namely CPU and RAM. Decisions on the assignment and migration of VMs are driven by probabilistic processes and are based exclusively on local information, which makes the approach very simple to implement. Both a fluid-like mathematical model and experiments on a real data center show that the approach rapidly consolidates the workload, and CPU-bound and RAM-bound VMs are balanced, so that both resources are exploited efficiently. |
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ISSN: | 2168-7161 2168-7161 2372-0018 |
DOI: | 10.1109/TCC.2013.17 |