Large, Internet based companies service user requests from multiple data centers located across the globe. These data centers often house a heterogeneous computing infrastructure and draw electricity from the local electricity market. Reducing the electricity costs of operating these data centers is a challenging problem, and in this work, we propose a novel solution which exploits both the data center heterogeneity and global electricity market diversity to reduce data center operating cost. We evaluate our solution in our test-bed that simulates a heterogeneous data center, using real-world request workload and real-world electricity prices. We show that our strategies achieve cost and energy saving of atleast 21% over a naïve load balancing scheme that distributes requests evenly across data centers, and outperform existing solutions which either do not exploit the electricity market diversity or do not exploit data center hardware diversity.

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Typically, complex resource-interdependence and heterogeneous workload patterns can result in sub-optimal job allocation leading to performance loss or under-utilization of compute resources. A well behaved model can anticipate the demand patterns and proactively react to the dynamic stresses in a timely and well optimized manner. For a workload hosting environment, pool of available resources are optimally configured and utilized to sustain certain expectation of Quality-of-Service (QoS) in the presence of power, thermal and reliability constraints. The workload (or job) scheduling mechanism is expected to withstand dynamic variations in demand stresses while maximizing the resource utilization and minimizing the performance loss. Furthermore, workloads can be co-allocated to the clusters with least amount of resource contention. In this paper we introduce the methodology that facilitates the coordinated scheduling of the workloads to the systems with least contentious resources through phase-assisted dynamic characterization. We describe the method to perform optimal job scheduling by using phase model synthesized by learning and classifying the run-time behavior of workloads.

Large, Internet based companies service user requests from multiple data centers located across the globe. These data centers often house a heterogeneous computing infrastructure and draw electricity from the local electricity market.Reducing the electricity costs of operating these data centers is a challenging problem, and in this work, we propose a novel solution which exploits both the data center heterogeneity and global electricity market diversity to reduce data center operating cost. We evaluate our solution in our test-bed that simulates a heterogeneous data center, using real-world request workload and real-world electricity prices. We show that our strategies achieve cost and energy saving of at least 21% over a naive load balancing scheme that distributes requests evenly across data centers, and outperform existing solutions which either do not exploit the electricity market diversity or do not exploit data center hardware diversity.