Multi-search-routes-based methods for minimizing makespan of homogeneous and heterogeneous resources in Cloud computing

Cloud computing, as a large-scale distributed computing system dynamically providing elastic services, is designed to meet the requirement of delivering computing services to users as subscription-oriented services. In general, the problems of resource scheduling in Cloud computing like minimizing makespan are usually NP-Hard problems. Various common algorithms including heuristic, meta-heuristic and machine learning are applied in resource scheduling of Cloud computing to obtain the solutions, which however are still probable and imperative to be optimized. Through innovatively applying heuristic algorithms namely LPT (Longest Processing Time) and BFD (Best Fit Decreasing) as the basic search routes and integrating these with neighborhood search algorithm namely OneStep, this paper proposes multi-search-routes-based algorithms containing LPT-OneStep, BFD-OneStep and their combinations for the sake of enhancing theoretical performance and improving solutions of scheduling schemes especially for problems of minimizing makespan for homogeneous and heterogeneous resources. Theoretical derivations prove that the proposed algorithms possess better theoretical approximation ratios for P||Cmax. Extensive experiments on simulation environment demonstrate the proposed algorithms outperform than corresponding compared algorithms for minimizing makespan problems in both homogenous resources and heterogeneous resources, which validates the superiority of the proposed algorithms.