Application Scheduling, Placement, and Routing for Power Efficiency in Cloud Data Centers

Abstract

Increased power usage and network performance degradation due to best-effort bandwidth sharing significantly affect tenancy cost, cloud adoption, and data center efficiencies. In this article, we propose a novel Sliding-Scheduled Tenant request model which enables tenants to specify the required duration of their application within a certain window, in addition to its resource requirement graph. We investigate the sliding-scheduled application placement and routing problem, which selects the start-time of requests within their specified time-window to reserve both server and network resources for their required duration, and therefore provide resource guarantees with predictable performance. Using the multi-component utilization-based power model, we formulate the problem as an optimization problem that maximizes the acceptance rate while consuming as low power as possible. We develop fast online heuristics that adopt power, acceptance and adaptive spread-based scheduling policies while allocating the resources with the consideration of request duration and current shutdown-time of the devices. We demonstrate the effectiveness of the proposed algorithms in terms of power saving and acceptance rate, 1) for small data centers, by comparing their performance with the numerical results obtained from solving the optimization problem using CPLEX and 2) for large data centers using comprehensive simulation results.