CRED: Cloud Right-Sizing to Meet Execution Deadlines and Data Locality

As demands for cloud-based data processing continue to grow, cloud providers seek effective techniques that deliver value to the business without violating Service Level Agreements (SLAs). Cloud right-sizing has emerged as a very promising technique for making cloud services more cost-effective. In this paper, we present CRED, a novel framework for cloud right-sizing with execution deadlines and data locality constraints. CRED jointly optimizes data placement and task scheduling in data centers with the aim of minimizing the number of nodes needed while meeting users' SLA requirements. We formulate CRED as an integer optimization problem and present a heuristic algorithm with provable performance guarantees to solve the problem. Competitive ratios of the proposed algorithm are quantified in closed form for arbitrary task parameters and cloud configurations. Simulation results using Google trace show that our proposed algorithm significantly outperforms existing heuristics such as first-fit by reducing up to 47% of required active servers, and achieves nearly-optimal performance in terms of cloud-right sizing.