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Robust resource provisioning in time-varying edge networks

Authors:

Yusheng JiAuthors Info & Claims

Mobihoc '20: Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing

Pages 21 - 30

https://doi.org/10.1145/3397166.3409146

Published: 11 October 2020 Publication History

Abstract

Edge computing is one of the revolutionary technologies that enable high-performance and low-latency modern applications, such as smart cities, connected vehicles, etc. Yet its adoption has been limited by factors including high cost of edge resources, heterogeneous and fluctuating demands, and lack of reliability. In this paper, we study resource provisioning in edge computing, taking into account these different factors. First, based on observations from real demand traces, we propose a time-varying stochastic model to capture the time-dependent and uncertain demand and network dynamics in an edge network. We then apply a novel robustness model that accounts for both expected and worst-case performance of a service. Based on these models, we formulate edge provisioning as a multi-stage stochastic optimization problem. The problem is NP-hard even in the deterministic case. Leveraging the multi-stage structure, we apply nested Benders decomposition to solve the problem. We also describe several efficiency enhancement techniques, including a novel technique for quickly solving the large number of decomposed subproblems. Finally, we present results from real dataset-based simulations, which demonstrate the advantages of the proposed models, algorithm and techniques.

References

[1]

Ravindra K. Ahuja, Thomas L. Magnanti, and James B. Orlin. 1993. Network Flows: Theory, Algorithms and Applictions.

Digital Library

[2]

MOSEK ApS. 2019. MOSEK Optimizer API for Python Release 9.0.98. https://docs.mosek.com/9.0/pythonfusion/index.html

[3]

Jacques F Benders. 1962. Partitioning Procedures for Solving Mixed-Variables Programming Problems. Numer. Math. 4, 1 (dec 1962), 238--252.

Digital Library

[4]

Deval Bhamare, Raj Jain, Mohammed Samaka, and Aiman Erbad. 2016. A Survey on Service Function Chaining. J. Netw. Comput. Appl. 75 (nov 2016), 138--155.

[5]

John R. Birge. 1985. Decomposition and Partitioning Methods for Multistage Stochastic Linear Programs. Oper. Res. 33, 5 (oct 1985), 989--1007.

Digital Library

[6]

John R. Birge and François Louveaux. 2011. Introduction to Stochastic Programming.

[7]

John R. Birge and François V. Louveaux. 1988. A Multicut Algorithm for Two-Stage Stochastic Linear Programs. Eur. J. Oper. Res. 34, 3 (mar 1988), 384--392.

[8]

Luiz F Bittencourt, Javier Diaz-Montes, Rajkumar Buyya, Omer F Rana, and Manish Parashar. 2017. Mobility-Aware Application Scheduling in Fog Computing. IEEE Cloud Comput. 4, 2 (mar 2017), 26--35.

[9]

Gabriele Castellano, Flavio Esposito, and Fulvio Risso. 2019. A Distributed Orchestration Algorithm for Edge Computing Resources with Guarantees. In Proc. IEEE INFOCOM. 2548--2556.

[10]

Ruilong Deng, Rongxing Lu, Chengzhe Lai, Tom Hao Luan, and Hao Liang. 2016. Optimal Workload Allocation in Fog-Cloud Computing Towards Balanced Delay and Power Consumption. IEEE Internet Things J. 3, 6 (2016), 1171--1181.

[11]

Andreas Fischer, Juan Felipe Botero, Michael Till Beck, Hermann de Meer, and Xavier Hesselbach. 2013. Virtual Network Embedding: A Survey. IEEE Commun. Surv. Tutorials 15, 4 (jan 2013), 1888--1906.

[12]

Bin Gao, Zhi Zhou, Fangming Liu, and Fei Xu. 2019. Winning at the Starting Line: Joint Network Selection and Service Placement for Mobile Edge Computing. In Proc. IEEE INFOCOM. 1459--1467.

[13]

Sladana Josilo and Gyorgy Dan. 2019. Wireless and Computing Resource Allocation for Selfish Computation Offloading in Edge Computing. In Proc. IEEE INFOCOM. 2467--2475.

[14]

Anton J. Kleywegt, Alexander Shapiro, and Tito Homem-de Mello. 2002. The Sample Average Approximation Method for Stochastic Discrete Optimization. SIAM J. Optim. 12, 2 (jan 2002), 479--502.

Digital Library

[15]

David Kotz, Tristan Henderson, Ilya Abyzov, and Jihwang Yeo. [n. d.]. CRAWDAD Dataset Dartmouth/Campus (V. 2009-09-09). https://crawdad.org/dartmouth/campus/20090909

[16]

Mathieu Leconte, Apostolos Destounis, and Georgios Paschos. 2018. Traffic Engineering with Precomputed Pathbooks. In Proc. IEEE INFOCOM. 234--242.

[17]

Defang Li, Peilin Hong, Kaiping Xue, and Jianing Pei. 2018. Virtual Network Function Placement Considering Resource Optimization and SFC Requests in Cloud Datacenter. IEEE Trans. Parallel Distrib. Syst. 29, 7 (jul 2018), 1664--1677.

[18]

Haijun Liao, Zhenyu Zhou, Shahid Mumtaz, and Jonathan Rodriguez. 2019. Robust Task Offloading for IoT Fog Computing Under Information Asymmetry and Information Uncertainty. In Proc. IEEE ICC. 1--6.

[19]

Qiang Liu and Tao Han. 2019. DIRECT: Distributed Cross-Domain Resource Orchestration in Cellular Edge Computing. In Proc. ACM MobiHoc.

Digital Library

[20]

Qiang Liu and Tao Han. 2019. VirtualEdge: Multi-Domain Resource Orchestration and Virtualization in Cellular Edge Computing. In Proc. IEEE ICDCS.

[21]

Jiaying Meng, Haisheng Tan, Chao Xu, Wanli Cao, Liuyan Liu, and Bojie Li. 2019. Dedas: Online Task Dispatching and Scheduling with Bandwidth Constraint in Edge Computing. In Proc. IEEE INFOCOM. 2287--2295.

[22]

Tao Ouyang, Rui Li, Xu Chen, Zhi Zhou, and Xin Tang. 2019. Adaptive User-managed Service Placement for Mobile Edge Computing: An Online Learning Approach. In Proc. IEEE INFOCOM. 1468--1476.

[23]

Konstantinos Poularakis, Jaime Llorca, Antonia M. Tulino, Ian Taylor, and Leandros Tassiulas. 2019. Joint Service Placement and Request Routing in Multi-cell Mobile Edge Computing Networks. In Proc. IEEE INFOCOM. 10--18.

[24]

R. Tyrrell Rockafellar and Stanislav Uryasev. 2000. Optimization of Conditional Value-at-Risk. J. Risk 2 (2000), 21--41.

[25]

Antonino Rullo, Edoardo Serra, Elisa Bertino, and Jorge Lobo. 2017. Shortfall-Based Optimal Placement of Security Resources for Mobile IoT Scenarios. In Proc. ESORICS. 419--436.

[26]

Haisheng Tan, Zhenhua Han, Xiang-Yang Li, and Francis C M Lau. 2017. Online Job Dispatching and Scheduling in Edge-Clouds. In Proc. IEEE INFOCOM. 1--9.

[27]

Taxi and Limousine Commission (TLC). 2019. 2018 Yellow Taxi Trip Data. https://data.cityofnewyork.us/Transportation/2018-Yellow-Taxi-Trip-Data/t29m-gskq

[28]

Liang Tong, Yong Li, and Wei Gao. 2016. A Hierarchical Edge Cloud Architecture for Mobile Computing. In Proc. IEEE INFOCOM. 1--9.

Digital Library

[29]

Lin Wang, Lei Jiao, Ting He, Jun Li, and Max Muhlhauser. 2018. Service Entity Placement for Social Virtual Reality Applications in Edge Computing. In Proc. IEEE INFOCOM. 468--476.

[30]

Duncan J. Watts and Steven H. Strogatz. 1998. Collective Dynamics of 'Small-World' Networks. Nature 393, 6684 (jun 1998), 440--442.

[31]

Christian Wolf. 2013. Advanced Acceleration Techniques for Nested Benders Decomposition in Stochastic Programming. Ph.D. Dissertation. University of Paderborn.

[32]

Yong Xiao and Marwan Krunz. 2017. QoE and Power Efficiency Tradeoff for Fog Computing Networks with Fog Node Cooperation. In Proc. IEEE INFOCOM. 1--9.

[33]

Di Xie, Ning Ding, Y Charlie Hu, and Ramana Kompella. 2012. The Only Constant Is Change: Incorporating Time-Varying Network Reservations in Data Centers. In Proc. ACM SIGCOMM. 199--210.

Digital Library

[34]

Jie Xu, Lixing Chen, and Pan Zhou. 2018. Joint Service Caching and Task Offloading for Mobile Edge Computing in Dense Networks. In Proc. IEEE INFOCOM. 207--215.

[35]

Yinyu Ye. 1991. An O(n3L) Potential Reduction Algorithm for Linear Programming. Math. Program. 50, 1-3 (mar 1991), 239--258.

Digital Library

[36]

Ruozhou Yu, Guoliang Xue, Vishnu Teja Kilari, and Xiang Zhang. 2018. Deploying Robust Security in Internet of Things. In Proc. IEEE CNS. 1--9.

[37]

Ruozhou Yu, Guoliang Xue, and Xiang Zhang. 2018. Application Provisioning in Fog Computing-enabled Internet-of-Things: A Network Perspective. In Proc. IEEE INFOCOM. 783--791.

[38]

Deze Zeng, Lin Gu, Song Guo, Zixue Cheng, and Shui Yu. 2016. Joint Optimization of Task Scheduling and Image Placement in Fog Computing Supported Software-Defined Embedded System. IEEE Trans. Comput. 65, 12 (dec 2016), 3702--3712.

Digital Library

Cited By

Wang GCheng PChen ZVucetic BLi Y(2024)Inverse Reinforcement Learning With Graph Neural Networks for Full-Dimensional Task Offloading in Edge ComputingIEEE Transactions on Mobile Computing10.1109/TMC.2023.332433223:6(6490-6507)Online publication date: Jun-2024
https://doi.org/10.1109/TMC.2023.3324332
Yu RGu HWang XZhou FXue GYang D(2023)EA-Market: Empowering Real-Time Big Data Applications with Short-Term Edge SLA Leases2023 32nd International Conference on Computer Communications and Networks (ICCCN)10.1109/ICCCN58024.2023.10230160(1-10)Online publication date: Jul-2023
https://doi.org/10.1109/ICCCN58024.2023.10230160
Yu RLo SZhou FXue G(2021)Data-Driven Edge Resource Provisioning for Inter-Dependent Microservices with Dynamic Load2021 IEEE Global Communications Conference (GLOBECOM)10.1109/GLOBECOM46510.2021.9685155(1-6)Online publication date: Dec-2021
https://doi.org/10.1109/GLOBECOM46510.2021.9685155

Index Terms

Robust resource provisioning in time-varying edge networks
1. Networks

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cover image ACM Conferences

Mobihoc '20: Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing

October 2020

384 pages

ISBN:9781450380157

DOI:10.1145/3397166

General Chairs:
Tommaso Melodia
Northeastern University
,
Eylem Ekici
The Ohio State University
,
Program Chairs:
Alhussein Abouzeid
Rensselaer Polytechnic Institute
,
Minghua Chen
City University of Hong Kong

Copyright © 2020 ACM.

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Published: 11 October 2020

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Mobihoc '20: The Twenty-first ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing

October 11 - 14, 2020

Virtual Event, USA

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Cited By

Wang GCheng PChen ZVucetic BLi Y(2024)Inverse Reinforcement Learning With Graph Neural Networks for Full-Dimensional Task Offloading in Edge ComputingIEEE Transactions on Mobile Computing10.1109/TMC.2023.332433223:6(6490-6507)Online publication date: Jun-2024
https://doi.org/10.1109/TMC.2023.3324332
Yu RGu HWang XZhou FXue GYang D(2023)EA-Market: Empowering Real-Time Big Data Applications with Short-Term Edge SLA Leases2023 32nd International Conference on Computer Communications and Networks (ICCCN)10.1109/ICCCN58024.2023.10230160(1-10)Online publication date: Jul-2023
https://doi.org/10.1109/ICCCN58024.2023.10230160
Yu RLo SZhou FXue G(2021)Data-Driven Edge Resource Provisioning for Inter-Dependent Microservices with Dynamic Load2021 IEEE Global Communications Conference (GLOBECOM)10.1109/GLOBECOM46510.2021.9685155(1-6)Online publication date: Dec-2021
https://doi.org/10.1109/GLOBECOM46510.2021.9685155

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