research-article

Public Access

Asymptotically optimal algorithm for online reconfiguration of edge-clouds

Authors:

Kevin ChanAuthors Info & Claims

MobiHoc '16: Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing

Pages 291 - 300

https://doi.org/10.1145/2942358.2942363

Published: 05 July 2016 Publication History

Abstract

"Edge-clouds," which are small servers located close to mobile users, have the potential to greatly reduce delay and backhaul traffic of mobile applications by moving cloud services closer to users at the edge. Due to their limited storage capacity, proper configurations of edge-clouds have a significant impact on their performance. This paper proposes a tractable online algorithm that configures edge-clouds dynamically solely based on past system history without any assumptions on the arrival patterns of mobile applications. We evaluate the competitive ratio, which quantifies the worst-case performance in comparison to an optimal offline policy, of our policy. We prove that the competitive ratio of our policy is linear with the capacity of the edge-cloud. Moreover, we also prove that no deterministic online policy can achieve a competitive ratio that is asymptotically better than ours. The utility of our online policy is further evaluated by traces from real-world data centers. These trace-based simulations demonstrate that our policy has better, or similar, performance compared to many intelligent offline policies that have complete knowledge of all future arrivals.

References

[1]

Achlioptas, D., Chrobak, M., and Noga, J. Competitive analysis of randomized paging algorithms. In Algorithms-ESA'96. Springer, 1996, pp. 419--430.

Digital Library

[2]

Amble, M. M., Parag, P., Shakkottai, S., and Ying, L. Content-aware caching and traffic management in content distribution networks. In 2011 Proceedings IEEE INFOCOM.

[3]

Belady, L. A. A study of replacement algorithms for a virtual-storage computer. IBM Systems journal 5, 2 (1966), 78--101.

Digital Library

[4]

Bonomi, F., Milito, R., Zhu, J., and Addepalli, S. Fog computing and its role in the internet of things. In Proceedings of the first edition of the MCC workshop on Mobile cloud computing (2012), ACM, pp. 13--16.

Digital Library

[5]

Borst, S., Gupt, V., and Walid, A. Distributed caching algorithms for content distribution networks. In 2010 Proceedings IEEE INFOCOM (2010), IEEE, pp. 1--9.

Digital Library

[6]

Hellerstein, J. L. Google cluster data. Google research blog, Jan. 2010. Posted at http://googleresearch.blogspot.com/2010/01/google-cluster-data.html.

[7]

Lewis, G., Echeverría, S., Simanta, S., Bradshaw, B., and Root, J. Tactical cloudlets: Moving cloud computing to the edge. In 2014 IEEE Military Communications Conference (MILCOM) (2014), IEEE, pp. 1440--1446.

Digital Library

[8]

LIU, J., ZHAO, T., ZHOU, S., CHENG, Y., AND NIU, Z. CONCERT: A cloud-based architecture for next-generation cellular systems. IEEE Wireless Communications Magazine 21, 6 (Dec. 2014), 14--22.

[9]

Llorca, J., Tulino, A. M., Guan, K., Esteban, J., Varvello, M., Choi, N., and Kilper, D. Dynamic in-network caching for energy efficient content delivery. In 2013 Proceedings IEEE INFOCOM (2013), IEEE, pp. 245--249.

[10]

Moore, L. K. The first responder network and next-generation communications for public safety: Issues for congress. Congressional Research Service, Library of Congress.

[11]

Qiu, X., Li, H., Wu, C., Li, Z., and Lau, F. Cost-minimizing dynamic migration of content distribution services into hybrid clouds. In 2012 Proceedings IEEE INFOCOM (2012), IEEE, pp. 2571--2575.

[12]

Ravindran, R., Liu, X., Chakraborti, A., Zhang, X., and Wang, G. Towards software defined icn based edge-cloud services. In 2013 IEEE 2nd International Conference on Cloud Networking (CloudNet) (2013), IEEE, pp. 227--235.

[13]

Reineke, J., and Grund, D. Relative competitive analysis of cache replacement policies. In ACM Sigplan Notices (2008), vol. 43, ACM, pp. 51--60.

Digital Library

[14]

Satyanarayanan, M., Chen, Z., Ha, K., Hu, W., Richter, W., and Pillai, P. Cloudlets: at the leading edge of mobile-cloud convergence. In 2014 6th International Conference on Mobile Computing, Applications and Services (MobiCASE) (2014), IEEE, pp. 1--9.

[15]

Satyanarayanan, M., Lewis, G., Morris, E., Simanta, S., Boleng, J., and Ha, K. The role of cloudlets in hostile environments. IEEE Pervasive Computing 12, 4 (2013), 40--49.

Digital Library

[16]

Satyanarayanan, M., Simoens, P., Xiao, Y., Pillai, P., Chen, Z., Ha, K., Hu, W., and Amos, B. Edge analytics in the internet of things. IEEE Pervasive Computing, 2 (2015), 24--31.

[17]

Sleator, D. D., and Tarjan, R. E. Amortized efficiency of list update and paging rules. Communications of the ACM 28, 2 (1985), 202--208.

Digital Library

[18]

Tadrous, J., Eryilmaz, A., and El Gamal, H. Proactive content distribution for dynamic content. In 2013 IEEE International Symposium on Information Theory Proceedings (ISIT) (2013), IEEE, pp. 1232--1236.

[19]

Taleb, T., and Ksentini, A. Follow me cloud: interworking federated clouds and distributed mobile networks. IEEE Network 27, 5 (2013), 12--19.

[20]

Urgaonkar, R., Wang, S., He, T., Zafer, M., Chan, K., and Leung, K. K. Dynamic service migration and workload scheduling in edge-clouds. Performance Evaluation 91 (2015), 205--228.

Digital Library

[21]

Wang, S., Urgaonkar, R., Zafer, M., He, T., Chan, K., and Leung, K. K. Dynamic service migration in mobile edge-clouds. In Proceedings of IFIP Networking (2015).

Cited By

Yang YWang S(2024)EdgeOPT: A Competitive Algorithm for Online Parallel Task Scheduling With Latency Guarantee in Mobile Edge ComputingIEEE Transactions on Communications10.1109/TCOMM.2024.341274172:11(7077-7092)Online publication date: Nov-2024
https://doi.org/10.1109/TCOMM.2024.3412741
Fathoni HYang CHuang CChen C(2024)Empowered edge intelligent aquaculture with lightweight Kubernetes and GPU-embeddedWireless Networks10.1007/s11276-023-03592-230:9(7321-7333)Online publication date: 6-Jan-2024
https://doi.org/10.1007/s11276-023-03592-2
Ma XXu MLi QLi YZhou AWang SMa XXu MLi QLi YZhou AWang S(2024)Background5G Edge Computing10.1007/978-981-97-0213-8_1(1-16)Online publication date: 1-May-2024
https://doi.org/10.1007/978-981-97-0213-8_1
Show More Cited By

Index Terms

Asymptotically optimal algorithm for online reconfiguration of edge-clouds
1. Networks
  1. Network services
    1. Cloud computing
  2. Network types
    1. Data center networks
2. Theory of computation
  1. Design and analysis of algorithms
    1. Online algorithms
      1. Caching and paging algorithms

Recommendations

Decentralized Edge Clouds

Cloud computing services are traditionally deployed on centralized computing infrastructures confined to a few data centers, while cloud applications run in a single data center. However, the cloud's centralized nature can be limiting in terms of ...
Monitoring-based auto-scalability across hybrid clouds
SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing

Cloud computing is a relatively new type of Internet-based computing that becomes more and more popular. Using methods like virtualization, adopting architectures based on microservices, automation of building and deployment processes, Cloud could ...
Optimal scheduling across public and private clouds in complex hybrid cloud environment

The hybrid cloud extends the private cloud model by using both local and remote resources. The private cloud will rely on the resources leased from public cloud providers for the execution of private cloud applications. The paper presents optimal ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MobiHoc '16: Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing

July 2016

421 pages

ISBN:9781450341844

DOI:10.1145/2942358

General Chairs:
Falko Dressler
Paderborn University
,
Friedhelm Meyer auf der Heide
Paderborn University

Copyright © 2016 ACM.

© 2016 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 July 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article

Funding Sources

ARO
ARL

Conference

MobiHoc'16

Sponsor:

MobiHoc'16: The Seventeenth ACM International Symposium on Mobile Ad Hoc Networking and Computing

July 5 - 8, 2016

Paderborn, Germany

Acceptance Rates

Overall Acceptance Rate 296 of 1,843 submissions, 16%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

63
Total Citations
View Citations
731
Total Downloads

Downloads (Last 12 months)52
Downloads (Last 6 weeks)12

Reflects downloads up to 25 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Yang YWang S(2024)EdgeOPT: A Competitive Algorithm for Online Parallel Task Scheduling With Latency Guarantee in Mobile Edge ComputingIEEE Transactions on Communications10.1109/TCOMM.2024.341274172:11(7077-7092)Online publication date: Nov-2024
https://doi.org/10.1109/TCOMM.2024.3412741
Fathoni HYang CHuang CChen C(2024)Empowered edge intelligent aquaculture with lightweight Kubernetes and GPU-embeddedWireless Networks10.1007/s11276-023-03592-230:9(7321-7333)Online publication date: 6-Jan-2024
https://doi.org/10.1007/s11276-023-03592-2
Ma XXu MLi QLi YZhou AWang SMa XXu MLi QLi YZhou AWang S(2024)Background5G Edge Computing10.1007/978-981-97-0213-8_1(1-16)Online publication date: 1-May-2024
https://doi.org/10.1007/978-981-97-0213-8_1
Uryvsky LBudishevskyi OOsypchuk S(2024)Research and Design of Fog Network Architecture with Smart Control SystemDigital Ecosystems: Interconnecting Advanced Networks with AI Applications10.1007/978-3-031-61221-3_40(822-842)Online publication date: 30-Jul-2024
https://doi.org/10.1007/978-3-031-61221-3_40
Li GTan HLiu LZhou HJiang SHan ZLi XChen G(2023)DAG Scheduling in Mobile Edge ComputingACM Transactions on Sensor Networks10.1145/361637420:1(1-25)Online publication date: 20-Oct-2023
https://dl.acm.org/doi/10.1145/3616374
Zheng SRen ZCheng WZhang H(2023)Performance Analysis for Subgraph Isomorphism Based Embedding Service Function ChainsIEEE Transactions on Network Science and Engineering10.1109/TNSE.2023.3247625(1-15)Online publication date: 2023
https://doi.org/10.1109/TNSE.2023.3247625
Li XZhang XHuang T(2023)Joint Task Offloading and Service Placement for Mobile Edge Computing: An Online Two-Timescale ApproachIEEE Transactions on Cloud Computing10.1109/TCC.2023.331228311:4(3656-3671)Online publication date: Oct-2023
https://doi.org/10.1109/TCC.2023.3312283
Qu YLu DDai HTan HTang SWu FDong C(2023)Resilient Service Provisioning for Edge ComputingIEEE Internet of Things Journal10.1109/JIOT.2021.307862010:3(2255-2271)Online publication date: 1-Feb-2023
https://doi.org/10.1109/JIOT.2021.3078620
He TWang S(2023)Joint Service Placement and Request Scheduling at the EdgeArtificial Intelligence for Edge Computing10.1007/978-3-031-40787-1_10(315-332)Online publication date: 4-Aug-2023
https://doi.org/10.1007/978-3-031-40787-1_10
Chen MWang HMeng ZXu HXu YLiu JHuang H(2022)Joint Data Collection and Resource Allocation for Distributed Machine Learning at the EdgeIEEE Transactions on Mobile Computing10.1109/TMC.2020.304543621:8(2876-2894)Online publication date: 1-Aug-2022
https://doi.org/10.1109/TMC.2020.3045436
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents