Dynamic Resource Management in Clouds: A Probabilistic Approach

Paulo GONÇALVES  Shubhabrata ROY  Thomas BEGIN  Patrick LOISEAU  

IEICE TRANSACTIONS on Communications   Vol.E95-B   No.8   pp.2522-2529
Publication Date: 2012/08/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.E95.B.2522
Print ISSN: 0916-8516
Type of Manuscript: INVITED PAPER (Special Section on Networking Technologies for Cloud Services)
cloud networking,  resource management,  epidemic model,  workload generator,  large deviation principle,  service level agreements,  video on demand,  buzz/flash crowd,  

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Dynamic resource management has become an active area of research in the Cloud Computing paradigm. Cost of resources varies significantly depending on configuration for using them. Hence efficient management of resources is of prime interest to both Cloud Providers and Cloud Users. In this work we suggest a probabilistic resource provisioning approach that can be exploited as the input of a dynamic resource management scheme. Using a Video on Demand use case to justify our claims, we propose an analytical model inspired from standard models developed for epidemiology spreading, to represent sudden and intense workload variations. We show that the resulting model verifies a Large Deviation Principle that statistically characterizes extreme rare events, such as the ones produced by “buzz/flash crowd effects” that may cause workload overflow in the VoD context. This analysis provides valuable insight on expectable abnormal behaviors of systems. We exploit the information obtained using the Large Deviation Principle for the proposed Video on Demand use-case for defining policies (Service Level Agreements). We believe these policies for elastic resource provisioning and usage may be of some interest to all stakeholders in the emerging context of cloud networking.