This work focuses on the load balancing and scheduling problem for batch jobs considering a cloud system comprised of geographically dispersed, heterogeneous datacenters. Each batch job is modeled using a directed acyclic graph of heterogeneous tasks. Load balancing and scheduling of batch jobs with loose deadlines results in operational cost reduction in the cloud system due to availability of renewable energy sources in datacenters' site and time of use dependent energy pricing in utility companies. A solution for load balancing and scheduling problem based on the force-directed scheduling approach is presented that considers the online application workload and limited resource and peak power capacity in each datacenter. The simulation results demonstrate significant operational cost decrease (up to 40%) using the proposed algorithm with respect to a greedy solution.
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