Cloud Bursting Scheduler for Cost Efficiency
Young Choon Lee, Bing Lian · 2017
Clouds have been increasingly adopted due to primarily their elasticity and pay-as-you-go (PAYG) pricing. While many organizations outsource the entire ICT solution to public clouds like Amazon Web Services Elastic Compute Cloud (EC2), others consider occasional workload offloading (cloud bursting) due to various reasons including governance and security. In this paper, we present Cloud Bursting Scheduler (CBS), a new cloud bursting algorithm. CBS explicitly takes into account cost factors of private in-house system (or private cloud) and public cloud. In particular, CBS attempts to optimize the cost to performance ratio by offloading jobs to public cloud explicitly taking into account time-varying electricity rates with private clouds and the timeinvariant rental rate of many public clouds. Based on simulation results obtained using real workload traces, CBS saves costs of running workloads by 55% and 12% compared with costs of cloud sourcing and private cloud, respectively. It also improves resource utilization (to 89%) by judiciously (de)activating inhouse resources and dynamically provisioning cloud resources.