Stochastic measurement-based multi-cloud consolidation for efficient resource distribution

Abstract

In distributed cloud systems, due to high computational complexity, existing methods cannot infinitely reduce the measurement interval to obtain the mean demand. However, considering both the unpredictability of demand and the diversity of pricing adds significant complexity to the problem. In response, we introduce an efficient algorithm called Stochastic Demand-oriented Resource consolidation based on Meta-heuristics (SDRM). The algorithm introduces the stochastic demand model and formulates a cross-cloud demand consolidation and resource scheduling problem, and tries to solve it efficiently by combining differential evolution algorithm with dynamic programming optimization. The results are provided for experiments utilizing both simulated and real-world data. It demonstrates that compared with traditional algorithms, increasing gain by up to 53%, SDRM just incurs an affordable time expense (i.e., 1.10-2.51 times that of existing algorithms). Therefore, SDRM stands as a compelling solution for resource distribution in heterogeneous cloud environments.