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Probabilistic Consolidation of Virtual Machines in Self-Organizing Cloud Data Centers
July-December 2013 (vol. 1 no. 2)
pp. 215-228
Carlo Mastroianni, eco4cloud srl and ICAR-CNR, Rende
Michela Meo, Politecnico di Torino, Torino
Giuseppe Papuzzo, eco4cloud srl, Rende
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 $({\rm ecoCloud})$, 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.
Index Terms:
Servers,Random access memory,Mathematical model,Virtual machining,Resource management,Probabilistic logic,Cloud computing,energy saving,Cloud computing,VM consolidation,data center
Carlo Mastroianni, Michela Meo, Giuseppe Papuzzo, "Probabilistic Consolidation of Virtual Machines in Self-Organizing Cloud Data Centers," IEEE Transactions on Cloud Computing, vol. 1, no. 2, pp. 215-228, July-Dec. 2013, doi:10.1109/TCC.2013.17
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