Cost-Efficient and Resilient Job Life-Cycle Management on Hybrid Clouds

Abstract

Cloud infrastructure offers democratized access to on-demand computing resources for scaling applications beyond captive local servers. While on-demand, fixed-price Virtual Machines (VMs) are popular, the availability of cheaper, but less reliable, spot VMs from cloud providers presents an opportunity to reduce the cost of hosting cloud applications. Our work addresses the issue of effective and economic use of hybrid cloud resources for planning job executions with deadline constraints. We propose strategies to manage a job's life-cycle on spot and on on-demand VMs to minimize the total dollar cost while assuring completion. With the foundation of stochastic optimization, our reusable table-based algorithm (RTBA) decides when to instantiate VMs, at what bid prices, when to use local machines, and when to checkpoint and migrate the job between these resources, with the goal of completing the job on time and with the minimum cost. In addition, three simpler heuristics are proposed as comparison. Our evaluation using historical spot prices for the Amazon EC2 market shows that RTBA on an average reduces the cost by 72%, compared to running on only on-demand VMs. It is also robust to fluctuations in spot prices. The heuristic, H3, often approaches RTBA in performance and may prove adequate for ad hoc jobs due to its simplicity.