Heterogeneity-Aware Workload Placement and Migration in Distributed Sustainable Datacenters

While major cloud service operators have taken various initiatives to operate their sustainable data enters with green energy, it is challenging to effectively utilize the green energy since its generation depends on dynamic natural conditions. Fortunately, the geographical distribution of data enters provides an opportunity for optimizing the system performance by distributing cloud workloads. In this paper, we propose a holistic heterogeneity-aware cloud workload placement and migration approach, sCloud, that aims to maximize the system good put in distributed self-sustainable data enters. sCloud adaptively places the transactional workload to distributed data enters, allocates the available resource to heterogeneous workloads in each data enter, and migrates batch jobs across data enters, while taking into account the green power availability and QoS requirements. We formulate the transactional workload placement as a constrained optimization problem that can be solved by nonlinear programming. Then, we propose a batch job migration algorithm to further improve the system good put when the green power supply varies widely at different locations. We have implemented sCloud in a university cloud test bed with real-world weather conditions and workload traces. Experimental results demonstrate sCloud can achieve near-to-optimal system performance while being resilient to dynamic power availability. It outperforms a heterogeneity-oblivious approach by 26% in improving system good put and 29% in reducing QoS violations.