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C3PO: Computation Congestion Control (PrOactive)

Published: 26 September 2016 Publication History

Abstract

There is an obvious trend that more and more data and computation are migrating into networks nowadays. Combining mature virtualization technologies with service-centric networking, which is deemed as a natural evolution of information-centric networking in 5G context, we are entering into an era where countless (mobile) services reside in an ISP network to provide low-latency access. Such services are often computation intensive and are dynamically created and destroyed on demand everywhere in the network to perform various tasks. Consequently, these ephemeral in-network services introduce a new type of congestion in the network which we refer to as "computation congestion". The service load need to be effectively distributed on different nodes in order to maintain the functionality and responsiveness of the network, which calls for a new design rather than reusing the centralised scheduler designed for cloud-based services. In this paper, we study both passive and proactive control strategies, based on the proactive control we further propose a fully distributed solution which is low complexity, adaptive, and responsive to network dynamics.

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References

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Cited By

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  • (2023)SoK: Distributed Computing in ICNProceedings of the 10th ACM Conference on Information-Centric Networking10.1145/3623565.3623712(88-100)Online publication date: 9-Oct-2023
  • (2020)SPINNProceedings of the 26th Annual International Conference on Mobile Computing and Networking10.1145/3372224.3419194(1-15)Online publication date: 16-Apr-2020
  • (2019)LQCC: A Link Quality-based Congestion Control Scheme in Named Data Networks2019 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC.2019.8885909(1-6)Online publication date: 15-Apr-2019
  • Show More Cited By

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cover image ACM Conferences
ACM-ICN '16: Proceedings of the 3rd ACM Conference on Information-Centric Networking
September 2016
275 pages
ISBN:9781450344678
DOI:10.1145/2984356
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 26 September 2016

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Author Tags

  1. 5G
  2. algorithm design
  3. information-centric networking
  4. load balancing
  5. network protocols

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ACM-ICN '16 Paper Acceptance Rate 23 of 84 submissions, 27%;
Overall Acceptance Rate 133 of 482 submissions, 28%

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Cited By

View all
  • (2023)SoK: Distributed Computing in ICNProceedings of the 10th ACM Conference on Information-Centric Networking10.1145/3623565.3623712(88-100)Online publication date: 9-Oct-2023
  • (2020)SPINNProceedings of the 26th Annual International Conference on Mobile Computing and Networking10.1145/3372224.3419194(1-15)Online publication date: 16-Apr-2020
  • (2019)LQCC: A Link Quality-based Congestion Control Scheme in Named Data Networks2019 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC.2019.8885909(1-6)Online publication date: 15-Apr-2019
  • (2018)Privacy-Preserving Machine Learning Based Data Analytics on Edge DevicesProceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society10.1145/3278721.3278778(341-346)Online publication date: 27-Dec-2018
  • (2017)NFaaSProceedings of the 4th ACM Conference on Information-Centric Networking10.1145/3125719.3125727(134-144)Online publication date: 26-Sep-2017

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