Abstract
The infrastructure resources in distributed green data centers (DGDCs) are shared by multiple heterogeneous applications to provide flexible services to global users in a high-performance and low-cost way. It is highly challenging to minimize the total cost of a DGDC provider in a market where bandwidth price of Internet service providers (ISPs), electricity price and the availability of renewable green energy all vary with geographic locations. Unlike existing studies, a Geographical Scheduling method of Multi-Application Tasks (GSMAT) that exploits spatial diversity in DGDCs is proposed to minimize the total cost of their provider by cost-effectively scheduling all arriving tasks of heterogeneous applications to meet tasks' delay bound constraints. In each time slot, the cost minimization problem for DGDCs is formulated as a constrained optimization one and solved by the proposed Simulated-annealing-based Bat Algorithm (SBA). Trace-driven experiments demonstrate that GSMAT achieves lower cost and higher throughput than two typical scheduling methods.