A Switch in Time Saves the Dime: A Model to Reduce Rental Cost in Cloud Computing

• 成果类型: Article
• 署名作者: Hosseini, Leila; Tang, Shaojie; Mookerjee, Vijay; Sriskandarajah, Chelliah
• 署名单位: Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; University of Texas System; University of Texas Dallas; Texas A&M University System; Texas A&M University College Station
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2019.0912
• 发表日期: 2020
• 页码: 753-775
• 关键词: task-allocation; performance; algorithms; services; policies

摘要

The goal to continually reduce operating costs while meeting computational needs is common to all modern organizations that use cloud computing. We study the problem of selecting computing resources with the goal of minimizing the total rental cost of completing a computing task in the presence of a time constraint. The problem is formulated as a scheduling problem that assigns computing resources to time periods of the planning horizon (time available to complete a single computing task). This (NP-hard) preemptive-resume type scheduling problem-new to the scheduling literature-has not been carefully addressed in practice to provide an implementable solution. Typically, the approach taken in practice is to use a single resource (a single virtual machine instance, or a cluster of identical virtual machine instances) to complete a computing task. The main insight of this study is that rather than completing a computing task using a single computing resource, rental costs can be significantly lowered by using a few resources (sometimes even just two) to complete the task. Thus, the computing task is switched from one resource to another to exploit the cloud provider's price-performance schedule. Cloud computing has been recognized as an economically attractive computing environment whose adoption has been growing over time. However, providers (such as Amazon Web Services) offer a confusing and diverse set of computing resources with different configurations and unit rental costs. Our near-optimal solution is based on switching the computing task from one resource to another in way that leverages the relationship between the price and performance of the available computing resources. The performance of a given resource can vary randomly as well as be correlated with the performance of another (stronger or weaker) resource. We present a worst-case performance guarantee of the proposed solution. In addition, we study the performance using a detailed computational study and a real-world example of an actual company that can benefit from our proposed solution. In the computational study as well as the real-world example, the cost of our solution is usually about 15%-25% lower than the benchmark solution of using the best single computing resource to process the computing task. Practicing information technology managers can use our approach to migrate in-house solutions to the cloud in a cost-effective manner.