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Reliability Augmentation of Requests with Service Function Chain Requirements in Mobile Edge-Cloud Networks

Published: 17 August 2020 Publication History

Abstract

Provisioning reliable network services for mobile users in a mobile edge computing environment is the top priority for most network service providers, as unreliable or severely failed services will result in tremendous loss on their revenues and consumers. In this paper, we study a novel service reliability augmentation problem in a Mobile Edge-Cloud (MEC) network, where mobile users request various network services through issuing requests with service function chain (SFC) requirements and reliability expectations, and an admitted request may not meet its reliability expectation initially. To enhance its service reliability to reach its expectation, it is a common practice to make use of redundant backups, that is to place redundant VNF instances of each Virtual Network Function (VNF) in its SFC in case its primary VNF instance fails. In this paper, we aim to augment the reliability of each admitted request as much as possible with the ultimate objective to reach its reliability expectation, subject to computing capacity on each cloudlet in the network. To this end, we first formulate a novel service reliability augmentation problem. We then deal with the problem for the admitted request under the assumption that all the secondary VNF instances of each primary VNF instance in its SFC must be placed into the cloudlets no more than l hops from the cloudlet of the primary VNF instance, where 1 ≤ l ≤ n − 1 and n is the number of cloudlets in the network, for which we propose an integer linear program (ILP) solution, and develop a randomized algorithm with a provable approximation ratio while a moderate resource constraint violation. We also devise an efficient heuristic algorithm for the problem without any resource constraint violation. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising, and their empirical results are superior to their analytical counterparts.

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cover image ACM Other conferences
ICPP '20: Proceedings of the 49th International Conference on Parallel Processing
August 2020
844 pages
ISBN:9781450388160
DOI:10.1145/3404397
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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Association for Computing Machinery

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Publication History

Published: 17 August 2020

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Author Tags

  1. Mobile Edge Computing
  2. Reliability augmentation for service function chain provisioning
  3. approximation algorithms
  4. primary and secondary VNF instance placement.
  5. virtualized network function placement

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  • Australian Research Council

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ICPP '20

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Overall Acceptance Rate 91 of 313 submissions, 29%

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Cited By

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  • (2024)Digital Twin-Assisted, SFC-Enabled Service Provisioning in Mobile Edge ComputingIEEE Transactions on Mobile Computing10.1109/TMC.2022.322724823:1(393-408)Online publication date: Jan-2024
  • (2024)Deep Reinforcement Learning Approach for Enhancing Profitability in Mobile Edge Computing2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD61410.2024.10580627(2876-2881)Online publication date: 8-May-2024
  • (2024)Dependability of Network Services in the Context of NFV: A Taxonomy and State of the Art ClassificationJournal of Network and Systems Management10.1007/s10922-024-09810-232:2Online publication date: 26-Mar-2024
  • (2024)Dynamic and efficient resource allocation for 5G end‐to‐end network slicing: A multi‐agent deep reinforcement learning approachInternational Journal of Communication Systems10.1002/dac.591637:17Online publication date: 30-Jul-2024
  • (2023)Availability-aware Provision of Service Function Chains in Mobile Edge ComputingACM Transactions on Sensor Networks10.1145/356548319:3(1-28)Online publication date: 1-Mar-2023
  • (2023)Service Home Identification of Multiple-Source IoT Applications in Edge ComputingIEEE Transactions on Services Computing10.1109/TSC.2022.317657616:2(1417-1430)Online publication date: 1-Mar-2023
  • (2023)Multi-SP Network Slicing Parallel Relieving Edge Network ConflictIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.331001334:11(2860-2875)Online publication date: Nov-2023
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  • (2022)Formulating Interference-aware Data Delivery Strategies in Edge Storage SystemsProceedings of the 51st International Conference on Parallel Processing10.1145/3545008.3545078(1-11)Online publication date: 29-Aug-2022
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