A Distributed Cloud Service for the Resolution of SAT

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.