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IEEE 7th International Symposium on Cloud and Service Computing (IEEE SC2)

作者： Ngoko, Yanik Trystram, Denis Cerin, Christophe Qarnot Comp Paris France Univ Paris 13 Paris France Univ Grenoble Alpes Grenoble France

ISBN: (纸本)9781538658628

In this paper, we introduce a new parallel and distributed algorithm for the resolution of the satisfiability problem. The proposed algorithm is based on algorithm portfolio and is intended to be used for servicing requests in a distributed cloud. The core of our contribution is the modeling of the optimal resource sharing schedule in parallel executions and the proposition of heuristics for its approximation. For this purpose, we reformulate a computational problem introduced in prior work. The main assumption is that it is possible to learn the optimal resource sharing from traces collected on past executions on a representative set of instances. We show that the learning can be formalized as a set coverage problem. Then, we propose to solve it by approximation and dynamic programming algorithms. These algorithms are based on classical greedy algorithms for the maximum coverage problem. Finally, we conduct an experimental evaluation for comparing the performance of the various proposed algorithms. The results show that some algorithms become more competitive if we intend to determine the trade-off between their quality and the runtime required for their computation.

关键词： Large scale cloud applications Resource provisioning and scheduling parallel distributed sat algorithm portfolio maximum coverage problem

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SOLVING sat IN A distributed CLOUD: A PORTFOLIO APPROACH

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INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE 2019年第2期29卷 261-274页

作者： Ngoko, Yanik Cerin, Christophe Trystram, Denis Qarnot Comp Dept Res & Dev 40-42 Rue Barbes Montrouge France Univ Paris 13 Dept Comp Sci 99 Ave Jean Baptiste Clement F-93430 Villetaneuse France Univ Grenoble Alpes Dept Comp Sci 700 Ave Cent F-38401 St Martin Dheres France

We introduce a new parallel and distributed algorithm for the solution of the satisfiability problem. It is based on an algorithm portfolio and is intended to be used for servicing requests in a distributed cloud. The core of our contribution is the modeling of the optimal resource sharing schedule in parallel executions and the proposition of heuristics for its approximation. For this purpose, we reformulate a computational problem introduced in a prior work. The main assumption is that it is possible to learn optimal resource sharing from traces collected on past executions on a representative set of instances. We show that the learning can be formalized as a set coverage problem. Then we propose to solve it by approximation and dynamic programming algorithms based on classical greedy algorithms for the maximum coverage problem. Finally, we conduct an experimental evaluation for comparing the performance of the various algorithms proposed. The results show that some algorithms become more competitive if we intend to determine the trade-off between their quality and the runtime required for their computation.

关键词： resource provisioning and scheduling parallel distributed sat algorithm portfolio maximum coverage problem

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