Geographical Scheduling of Multi-application Tasks for Cost Minimization in Distributed Green Data Centers

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.