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Abstract
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The cloud computing is an important factor for environmentally sustainable development. If, in the one hand, the increasing demand of users drive the creation of large datacenters, in the other hand, cloud computing’s “multitenancy” trait allows the reduction of physical hardware and, therefore, the saving of energy. Thus, it is imperative to optimize the energy consumption corresponding to the datacenter’s activities.
Three elements are crucial on energy consumption of a cloud platform: computation (processing), storage and network infrastructure. Therefore, the aim is to provide different techniques to reduce energy consumption regarding these three elements using the algorithmic game theory tool.
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Work program
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We intend to elaborate algorithms of load balancing, which is conditioned by the energy consumption of the entire system as well as users’ requirements.
This work will be divided in five tasks.
1) The problem corresponds to compute efficient solution for several objectives simultaneously. In our context, it is not pertinent to use these techniques (since energy and SLA’s constraints are conflicting objectives). In multiobjective optimization, a tool to capture the trade-off between conflicting objectives is the notion of Pareto set. The first work is to understand how this Pareto set, can be computed.
2) The second work will also focus on cooperation in order to reduce the energy cost. To exploit the recent advances of algorithmic game theory, we are interested to design mechanisms that are able to enforce cooperation among the private clouds. In terms of the game theory we can express two divergent goals realized at each storage/computing server.
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