Dynamic scheduling strategy with efficient node availability prediction for handling divisible loads in multi-cloud systems

With large resource capacity, clouds have become a primary infrastructure for users to store big amount of data and perform large-scale computations. With the increasing demands and diversity of applications' requirements, users are facing a fundamental problem of management of resources reserved from clouds. Particularly, the problem of load scheduling is the most important since it directly affects the performance of the system. Designing an efficient scheduling strategy for minimizing the total processing time of loads is challenging since it has to consider many intrinsic characteristics of the system such as the availability and heterogeneity of computing nodes, network topology and capacity. In this paper, we propose a novel architecture of a multi-cloud system that can satisfy complex requirements of users' applications. Based on this architecture, we propose a dynamic scheduling strategy (DSS) that integrates the Divisible Load Theory and node availability prediction techniques to achieve high performance. We conduct intensive simulations to evaluate the performance of the proposed scheduling strategy. The results show that the proposed scheduling strategy outperforms the baseline schemes by reducing the total processing time of loads up to 44.60%. The results also provide useful insights on the applicability of the proposed approach in realistic scenarios.