A Framework for Allocating Server Time to Spot and On-Demand Services in Cloud Computing

Author:

Wu Xiaohu1,Pellegrini Francesco De2ORCID,Gao Guanyu3,Casale Giuliano4

Affiliation:

1. Nanyang Technological University, Nanyang Avenue, Singapore

2. University of Avignon, Meinajaries, Avignon, France

3. Nanjing University of Science and Technology, Nanjing, China

4. Imperial College London, United Kingdom, London, UK

Abstract

Cloud computing delivers value to users by facilitating their access to servers at any time period needed. An approach is to provide both on-demand and spot services on shared servers. The former allows users to access servers on demand at a fixed price and users occupy different time periods on servers. The latter allows users to bid for the remaining unoccupied time periods via dynamic pricing; however, without appropriate design, such time periods may be arbitrarily short since on-demand users arrive randomly. This is also the current service model adopted by Amazon Elastic Cloud Compute. In this article, we provide the first integral framework for sharing time on servers between on-demand and spot services while optimally pricing spot service. It guarantees that on-demand users can get served quickly while spot users can stably use servers for a properly long period once accepted, which is a key feature in making both on-demand and spot services accessible. Simulation results show that, by complementing the on-demand market with a spot market, a cloud provider can improve revenue by up to 461.5%. The framework is designed under assumptions that are met in real environments. It is a new tool that other cloud operators can use to quantify the advantage of a hybrid spot and on-demand service, making the case for eventually integrating this service model into their own infrastructures.

Funder

European Union's Horizon 2020 research and innovation program

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture,Safety, Risk, Reliability and Quality,Media Technology,Information Systems,Software,Computer Science (miscellaneous)

Reference48 articles.

1. Fixed and market pricing for cloud services

2. Deconstructing Amazon EC2 Spot Instance Pricing

3. Amazon.com Inc.2018. Amazon EC2 pricing. Retrieved November 28 2018 from https://aws.amazon.com/ec2/purchasing-options/. Amazon.com Inc.2018. Amazon EC2 pricing. Retrieved November 28 2018 from https://aws.amazon.com/ec2/purchasing-options/.

4. Truthful Online Scheduling with Commitments

5. Dynamic Pricing Without Knowing the Demand Function: Risk Bounds and Near-Optimal Algorithms

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Enhanced Red-tailed Hawk Algorithm: Elevating Cloud Task Scheduling Efficiency;2024-02-01

2. Delay and Price Differentiation in Cloud Computing: A Service Model, Supporting Architectures, and Performance;ACM Transactions on Modeling and Performance Evaluation of Computing Systems;2023-06-24

3. CoSpot;Proceedings of the 13th Symposium on Cloud Computing;2022-11-07

4. Selected Aspects of Interactive Feature Extraction;Lecture Notes in Computer Science;2022

5. Cost Optimization for Big Data Workloads Based on Dynamic Scheduling and Cluster-Size Tuning;Big Data Research;2021-07

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3