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Content Provider | IEEE Xplore Digital Library |
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Author | Eyraud-Dubois, L. Larcheveque, H. |
Copyright Year | 2013 |
Abstract | In this paper, we study a resource allocation problem in the context of Cloud Computing, in which a set of Virtual Machines (VM) has to be allocated on a set of Physical Machines (PM). Each VM has a given demand (e.g. CPU demand), and each PM has a capacity. However, VMs only use a fraction of their demand. The aim is to exploit the difference between the demand of the VM and its actual resource usage, to achieve a higher utilization on the PMs. However, the resource consumption of the VMs might change over time (while staying under its original demand), implying sometimes expensive “SLA violations” when the demand of some VMs is not satisfied because of overloaded PMs. Thus, while optimizing the global resource utilization of the PMs, it is necessary to ensure that at any moment a VM's need evolves, a few number of migrations (moving a VM from PM to PM) is sufficient to find a new configuration in which all the VMs' consumptions are satisfied. We model this problem using a fully dynamic bin packing approach and we present an algorithm ensuring a global utilization of the resources of 66%. Moreover, each time a PM is overloaded, at most one migration is sufficient to fall back in a configuration with no overloaded PM, and at most 3 different PMs are concerned by required migrations that may occur to keep the global resource utilization correct. This allows the platform to be highly resilient to a great number of changes. |
Sponsorship | IEEE Comput. Soc. |
Starting Page | 79 |
Ending Page | 87 |
File Size | 209716 |
Page Count | 9 |
File Format | |
ISBN | 9781467360661 |
ISSN | 15302075 |
DOI | 10.1109/IPDPS.2013.67 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2013-05-20 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Resource management Heuristic algorithms Context Approximation algorithms Approximation methods Cloud computing Robustness Consolidation Cloud Computing Bin Packing Online Algorithms Approximation Algorithms |
Content Type | Text |
Resource Type | Article |
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