research-article

Cocoa: Dynamic Container-Based Group Buying Strategies for Cloud Computing

Authors:

John C. S. LuiAuthors Info & Claims

ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS), Volume 2, Issue 2

Article No.: 8, Pages 1 - 31

https://doi.org/10.1145/3022876

Published: 07 February 2017 Publication History

Abstract

Although the Infrastructure-as-a-Service (IaaS) cloud offers diverse instance types to users, a significant portion of cloud users, especially those with small and short demands, cannot find an instance type that exactly fits their needs or fully utilize purchased instance-hours. In the meantime, cloud service providers are also faced with the challenge to consolidate small, short jobs, which exhibit strong dynamics, to effectively improve resource utilization. To handle such inefficiencies and improve cloud resource utilization, we propose Cocoa (COmputing in COntAiners), a novel group buying mechanism that organizes jobs with complementary resource demands into groups and allocates them to group buying deals predefined by cloud providers. Each group buying deal offers a resource pool for all the jobs in the deal, which can be implemented as either a virtual machine or a physical server. By running each user job on a virtualized container, our mechanism allows flexible resource sharing among different users in the same group buying deal, while improving resource utilization for cloud providers. To organize jobs with varied resource demands and durations into groups, we model the initial static group organization as a variable-sized vector bin packing problem, and the subsequent dynamic group organization problem as an online multidimensional knapsack problem. Through extensive simulations driven by a large amount of real usage traces from a Google cluster, we evaluate the potential cost reduction achieved by Cocoa. We show that through the effective combination and interaction of the proposed static and dynamic group organization strategies, Cocoa greatly outperforms the existing cloud workload consolidation mechanism, substantiating the feasibility of group buying in cloud computing.

References

[1]

Amazon. 2014. EC2 Spot Instance Pricing. Retrieved from http://aws.amazon.com/ec2/spot/pricing/.

[2]

Cynthia Barnhart, Ellis L. Johnson, George L. Nemhauser, Martin W. P. Savelsbergh, and Pamela H. Vance. 1998. Branch-and-price: Column generation for solving huge integer programs. Operations Research 46, 3 (1998), 316--329.

Digital Library

[3]

Anton Beloglazov and Rajkumar Buyya. 2012. Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurrency and Computation: Practice and Experience 24, 13 (2012), 1397--1420.

Digital Library

[4]

Anton Beloglazov and Rajkumar Buyya. 2013. Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Transactions on Parallel and Distributed Systems 24, 7 (2013), 1366--1379.

Digital Library

[5]

Arka A. Bhattacharya, David Culler, Eric Friedman, Ali Ghodsi, Scott Shenker, and Ion Stoica. 2013. Hierarchical scheduling for diverse datacenter workloads. In Proceedings of the 4th Annual Symposium on Cloud Computing (SOCC’13). ACM, 4.

Digital Library

[6]

J. Wilkes, C. Reiss, and J. Hellerstein. 2011. Google Cluster-Usage Traces. Retrieved from http://code.google.com/p/googleclusterdata/.

[7]

Yanpei Chen, Sara Alspaugh, and Randy Katz. 2012. Interactive analytical processing in big data systems: A cross-industry study of mapreduce workloads. Proc. VLDB Endow. 5, 12 (Aug. 2012), 1802--1813.

Digital Library

[8]

CloudSigma. 2014. Homepage. Retrieved from http://www.cloudsigma.com/.

[9]

George B Dantzig and Philip Wolfe. 1960. Decomposition principle for linear programs. Operations Research 8, 1 (1960), 101--111.

Digital Library

[10]

Nikhil R. Devanur and Thomas P. Hayes. 2009. The adwords problem: Online keyword matching with budgeted bidders under random permutations. In Proceedings of the 10th ACM Conference on Electronic Commerce (EC’09). ACM, New York, NY, 71--78.

Digital Library

[11]

Docker. 2014. Docker install docs. Retrieved from https://docs.docker.com/installation/#installation.

[12]

Khaled Elmeleegy. 2013. Piranha: Optimizing short jobs in hadoop. Proc. VLDB Endow. 6, 11 (Aug. 2013), 985--996.

Digital Library

[13]

Ali Ghodsi, Matei Zaharia, Benjamin Hindman, Andy Konwinski, Scott Shenker, and Ion Stoica. 2011. Dominant resource fairness: Fair allocation of multiple resource types. In Proceedings of the 8th USENIX Conference on Networked Systems Design and Implementation (NSDI’11). USENIX Association, Berkeley, CA, 323--336.

Digital Library

[14]

Gagan Goel and Aranyak Mehta. 2008. Online budgeted matching in random input models with applications to adwords. In Proceedings of the 19th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA’08). Society for Industrial and Applied Mathematics, Philadelphia, PA, 982--991.

Digital Library

[15]

Chien-Ju Ho and Jennifer Wortman Vaughan. 2012. Online task assignment in crowdsourcing markets. In AAAI, Vol. 12. 45--51.

Digital Library

[16]

Gueyoung Jung, Matti A. Hiltunen, Kaustubh R. Joshi, Richard D. Schlichting, and Calton Pu. 2010. Mistral: Dynamically managing power, performance, and adaptation cost in cloud infrastructures. In Proceedings of 13th IEEE International Conference on Distributed Computing Systems (ICDCS’10). IEEE, 62--73.

Digital Library

[17]

Kien Le, Ricardo Bianchini, Jingru Zhang, Yogesh Jaluria, Jiandong Meng, and Thu D. Nguyen. 2011. Reducing electricity cost through virtual machine placement in high performance computing clouds. In Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC’11). ACM, New York, NY, Article 22, 12 pages.

Digital Library

[18]

Gunho Lee, Byung-Gon Chun, and H. Katz. 2011. Heterogeneity-aware resource allocation and scheduling in the cloud. In Proceedings of the 3rd USENIX Conference on Hot Topics in Cloud Computing (HotCloud’11). USENIX Association, Berkeley, CA, 4--4.

Digital Library

[19]

Peng Lin, Xiaojun Feng, Qian Zhang, and Mounir Hamdi. 2013. Groupon in the air: A three-stage auction framework for spectrum group-buying. In Proceedings of INFOCOM. IEEE, 2013--2021.

[20]

Mohammad Mahdian and Qiqi Yan. 2011. Online bipartite matching with random arrivals: An approach based on strongly factor-revealing lps. In Proc. of SODA. ACM, New York, NY, 597--606.

Digital Library

[21]

Xiaoqiao Meng, Vasileios Pappas, and Li Zhang. 2010. Improving the scalability of data center networks with traffic-aware virtual machine placement. In Proceedings of INFOCOM. IEEE, San Diego, CA, 1--9.

Digital Library

[22]

Mayank Mishra, Anwesha Das, Purushottam Kulkarni, and Anirudha Sahoo. 2012. Dynamic resource management using virtual machine migrations. IEEE Communications Magazine 50, 9 (2012), 34--40.

[23]

Timothy Prickett Morgan. 2014. Google Runs All Software in Containers. Retrieved from http://www.enterprisetech.com/2014/05/28/google-runs-software-containers/.

[24]

OpenVz. 2014. Homepage. Retrieved from http://openvz.org/Main_Page.

[25]

Peter Orlik and Hiroaki Terao. 1992. Arrangements of hyperplanes, volume 300 of Grundlehren der Mathematischen Wissenschaften [Fundamental Principles of Mathematical Sciences].

[26]

Boaz Patt-Shamir and Dror Rawitz. 2012. Vector bin packing with multiple-choice. Discrete Applied Mathematics 160, 10 (2012), 1591--1600.

Digital Library

[27]

Jerome Petazzoni. 2013. Lightweight Virtualization with Linux Containers and Docker. Retrieved from http://tech.yandex.com/events/yac/2013/talks/14/. (2013).

[28]

Serge A. Plotkin, David B. Shmoys, and Éva Tardos. 1995. Fast approximation algorithms for fractional packing and covering problems. Mathematics of Operations Research 20, 2 (1995), 257--301.

[29]

David M. Ryan and Brian A. Foster. 1981. An integer programming approach to scheduling. In Computer Scheduling of Public Transport Urban Passenger Vehicle and Crew Scheduling. 269--280.

[30]

Stephen Soltesz, Herbert Pötzl, Marc E. Fiuczynski, Andy Bavier, and Larry Peterson. 2007. Container-based operating system virtualization: A scalable, high-performance alternative to hypervisors. In Proceedings of EuroSys. ACM, New York, NY, 275--287.

Digital Library

[31]

Rade Stanojevic, Ignacio Castro, and Sergey Gorinsky. 2011. CIPT: Using tuangou to reduce IP transit costs. In Proceedings of CoNEXT. ACM, New York, NY, 17:1--17:12.

Digital Library

[32]

David Strauss. 2013. Containers-Not Virtual Machines-Are the Future Cloud. Retrieved from http://www.linuxjournal.com/content/containers%E2%80%94not-virtual-machines%E2%80%94are-future-cloud.

[33]

AadW. van der Vaart and JonA. Wellner. 1996. Weak convergence. In Weak Convergence and Empirical Processes. Springer New York, 16--28.

[34]

Akshat Verma, Gargi Dasgupta, Tapan Kumar Nayak, Pradipta De, and Ravi Kothari. 2009. Server workload analysis for power minimization using consolidation. In Proceedings of ATC. USENIX Association, Berkeley, CA, 1.

Digital Library

[35]

Vrtuozzo. 2014. Homepage. Retrieved from http://www.parallels.com/virtuozzo.

[36]

Wei Wang, Di Niu, Baochun Li, and Ben Liang. 2013. Dynamic cloud resource reservation via cloud brokerage. In Proceedings of 33rd IEEE International Conference on Distributed Computing Systems (ICDCS’13). IEEE, Philadelphia, PA, 400--409.

Digital Library

[37]

Zhen Xiao, Weijia Song, and Qi Chen. 2013. Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Transactions on Parallel and Distributed Systems 24, 6 (2013), 1107--1117.

Digital Library

[38]

Fei Xu, Fangming Liu, Hai Jin, and Athanasios V. Vasilakos. 2014a. Managing performance overhead of virtual machines in cloud computing: A survey, state of the art, and future directions. Proc. IEEE 102, 1 (2014), 11--31.

[39]

Fei Xu, Fangming Liu, Linghui Liu, Hai Jin, Bo Li, and Baochun Li. 2014b. Iaware: Making live migration of virtual machines interference-aware in the cloud. IEEE Trans. Comput. 63, 12 (2014), 3012--3025.

Digital Library

[40]

Xiaomeng Yi, Fangming Liu, Zongpeng Li, and Hai Jin. 2016. Flexible instance: Meeting deadlines of delay tolerant jobs in the cloud with dynamic pricing. In Proc. of ICDCS.

[41]

Xiaomeng Yi, Fangming Liu, Jiangchuan Liu, and Hai Jin. 2014. Building a network highway for big data: Architecture and challenges. IEEE Network 28, 4 (2014), 5--13.

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https://doi.org/10.1007/978-3-031-51572-9_2
Kosińska JZieliński K(2023)Experimental Evaluation of Rule-Based Autonomic Computing Management Framework for Cloud-Native ApplicationsIEEE Transactions on Services Computing10.1109/TSC.2022.315900116:2(1172-1183)Online publication date: 1-Mar-2023
https://doi.org/10.1109/TSC.2022.3159001
Chouliaras SSotiriadis S(2023)An adaptive auto-scaling framework for cloud resource provisioningFuture Generation Computer Systems10.1016/j.future.2023.05.017148:C(173-183)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.future.2023.05.017
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Index Terms

Cocoa: Dynamic Container-Based Group Buying Strategies for Cloud Computing
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
      1. Cloud computing
2. Theory of computation
  1. Design and analysis of algorithms
    1. Approximation algorithms analysis
      1. Packing and covering problems
    2. Online algorithms

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Published In

cover image ACM Transactions on Modeling and Performance Evaluation of Computing Systems

ACM Transactions on Modeling and Performance Evaluation of Computing Systems Volume 2, Issue 2

June 2017

171 pages

ISSN:2376-3639

EISSN:2376-3647

DOI:10.1145/3051083

Editors:
Sem Borst
Nokia Bell Labs / Eindhoven University of Technology, Netherlands
,
Carey Williamson
University of Calgary, Canada

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Copyright © 2017 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 February 2017

Accepted: 01 November 2016

Revised: 01 November 2016

Received: 01 July 2015

Published in TOMPECS Volume 2, Issue 2

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National 973 Basic Research Program
National Natural Science Foundation of China
National Key Research and Development Program

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Cited By

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Kosińska JZieliński K(2023)Experimental Evaluation of Rule-Based Autonomic Computing Management Framework for Cloud-Native ApplicationsIEEE Transactions on Services Computing10.1109/TSC.2022.315900116:2(1172-1183)Online publication date: 1-Mar-2023
https://doi.org/10.1109/TSC.2022.3159001
Chouliaras SSotiriadis S(2023)An adaptive auto-scaling framework for cloud resource provisioningFuture Generation Computer Systems10.1016/j.future.2023.05.017148:C(173-183)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.future.2023.05.017
Gul O(2022)Heuristic Resource Reservation Policies for Public Clouds in the IoT EraSensors10.3390/s2223903422:23(9034)Online publication date: 22-Nov-2022
https://doi.org/10.3390/s22239034
Saboor AHassan MAkbar RShah SHassan FMagsi SSiddiqui M(2022)Containerized Microservices Orchestration and Provisioning in Cloud Computing: A Conceptual Framework and Future PerspectivesApplied Sciences10.3390/app1212579312:12(5793)Online publication date: 7-Jun-2022
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Ray BSaha AKhatua SRoy S(2021)Quality and Profit Assured Trusted Cloud Federation Formation: Game Theory Based ApproachIEEE Transactions on Services Computing10.1109/TSC.2018.283385414:3(805-819)Online publication date: 1-May-2021
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Liu KPeng JYu BLiu WHuang ZPan J(2021)An Instance Reservation Framework for Cost Effective Services in Geo-Distributed Data CentersIEEE Transactions on Services Computing10.1109/TSC.2018.281812114:2(356-370)Online publication date: 1-Mar-2021
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Rad Jahanbani PAdabi SRezaee A(2020)A new multi-agent group-buying auction for automated VM-to-Customer mappingJournal of Organizational Computing and Electronic Commerce10.1080/10919392.2020.183884731:1(35-58)Online publication date: 2-Dec-2020
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Maenhaut PVolckaert BOngenae VDe Turck F(2019)Resource Management in a Containerized Cloud: Status and ChallengesJournal of Network and Systems Management10.1007/s10922-019-09504-0Online publication date: 22-Nov-2019
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Lu LYu JZhu YLi M(2018)A Double Auction Mechanism to Bridge Users’ Task Requirements and Providers’ Resources in Two-Sided Cloud MarketsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2017.278123629:4(720-733)Online publication date: 1-Apr-2018
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