Analytic technique for optimal workload scheduling in data-center using phase detection

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.