research-article

Online Learning and Optimization for Computation Offloading in D2D Edge Computing and Networks

Authors:

Yan ZhangAuthors Info & Claims

Mobile Networks and Applications, Volume 27, Issue 3

Pages 1111 - 1122

https://doi.org/10.1007/s11036-018-1176-y

Published: 01 June 2022 Publication History

Abstract

This paper introduces a framework of device-to-device edge computing and networks (D2D-ECN), a new paradigm for computation offloading and data processing with a group of resource-rich devices towards collaborative optimization between communication and computation. However, the computation process of task intensive applications would be interrupted when capacity-limited battery energy run out. In order to tackle this issue, the D2D-ECN with energy harvesting technology is applied to provide a green computation network and guarantee service continuity. Specifically, we design a reinforcement learning framework in a point-to-point offloading system to overcome challenges of the dynamic nature and uncertainty of renewable energy, channel state and task generation rates. Furthermore, to cope with high-dimensionality and continuous-valued action of the offloading system with multiple cooperating devices, we propose an online approach based on Lyapunov optimization for computation offloading and resource management without priori energy and network information. Numerical results demonstrate that our proposed scheme can reduce system operation cost with low task execution time in D2D-ECN.

References

[1]

Gubbi J, Buyya R, Marusic S, and Palaniswami M Internet of things (IoT): a vision, architectural elements, and future directions Future Generat Comput Syst 2013 29 7 1645-1660

[2]

ETSI ISG Mobile edge computing a key technology towards 5G White Paper 2015 11 1-16

[3]

Pu L, Chen C, Xu J, and Fu X D2D fogging: an energy-efficient and incentive-aware task offloading framework via network-assisted D2D collaboration IEEE J Sel Areas Commun 2016 34 12 3887-3901

[4]

Liu F et al. Gearing resource-poor mobile devices with powerful clouds: architectures, challenges, and applications IEEE Wireless Commun 2013 20 3 14-22

[5]

Zhang K et al. Energy-ffficient offloading for mobile edge computing in 5G heterogeneous networks IEEE Access 2016 4 5896-5907

[6]

Shih Y-Y, Chung W-H, Pang A-C, Chiu T-C, and Wei H-Y Enabling low-latency applications in fog-radio access networks IEEE Netw 2017 31 1 52-58

[7]

Zhang K, Mao Y, Leng S, He Y, and Zhang Y Mobile-edge computing for vehicular networks: a promising network paradigm with predictive off-loading IEEE Vehi Tech Maga 2017 12 2 36-44

[8]

Wang X, Leng S, and Yang K Social-aware edge caching in fog radio access networks IEEE Access 2017 5 8492-8501

[9]

Chen X, Pu L, Gao L, Wu W, and Wu D Exploiting massive D2D collaboration for energy-efficient mobile edge computing IEEE Commun Mag 2017 24 4 64-71

[10]

Ti N, Le L (2017) Computation offloading leveraging computing resources from edge cloud and mobile peers. In: Proceedings of the IEEE Int. Commun. Confe (ICC)

[11]

Meng X, Wang W, and Zhang Z Delay-constrained hybrid computation offloading with cloud and fog computing IEEE Access 2017 5 21355-21367

[12]

Garlatova M, Wallwater A, Zussman G (2011) Networking low-power energy harvesting devices: measurements and algorithms. In: Proceedings of the IEEE Conf. Comput. Commun. (INFOCOM), pp 1602–1610

[13]

Dhillon H, Li Y, Nuggehalli P, Pi Z, and Andrews J Fundamentals of heterogeneous cellular networks with energy harvesting IEEE Trans Wireless Commun 2014 13 5 2782-2797

[14]

Mao Y, Zhang J, and Letaief KB Dynamic computation offloading for Mobile-Edge computing with energy harvesting devices IEEE J Sel Areas Commun 2016 34 12 3590-3605

[15]

Fan B, Leng S, and Yang K A dynamic bandwidth allocation algorithm in mobile networks with big data of users and networks IEEE Netw Maga 2016 30 1 6-10

[16]

Mastronarde N and Schaar M Fast reinforcement learning for energy-efficient wireless communication IEEE Trans Sig Process 2011 50 12 6262-6266

[17]

Wei Y, Yu FR, Song M, and Han Z User scheduling and resource allocation in HetNets with hybrid energy supply: an actor-critic reinforcement learning approach IEEE Trans Wire Commun 2017 17 1 680-692

[18]

Xu J, Chen L, and Ren S Online learning for offloading and autoscaling in energy harvesting mobile edge computing IEEE Trans Cogn Commun Netw 2017 3 3 361-373

[19]

Mao Y, You C, Zhang J, Huang K, and Letaief KB A servey on mobile edge computing: the communication perspective IEEE Commun Survey Tuts 2017 19 4 2322-2358

[20]

Miettinen AP, Nurminen JK (2010) Energy efficiency of mobile clients in cloud computing. In: Proceedings of the USENIX Conf. Hot Topics Cloud Comput. (HotCloud), Boston, MA, USA, pp 1–7

[21]

Wang Q, Leng S, Fu H, and Zhang Y An IEEE 802.11p-based multichannle MAC scheme with channel coordination for vehicular Ad Hoc networks IEEE Trans Intell Trans Sys 2012 13 2 449-458

[22]

Rajan D, Sabharwal A, and Aazhang B Delay-bounded packet scheduling of bursty traffic over wireless channels IEEE Trans Inform Theory 2004 50 1 125-144

[23]

Burd TD and Brodersen RW Processor design for portable systems Kluwer J VLSI Signal Process Syst 1996 13 2 203-221

[24]

Altman E (1999) Constrained Markov Decision Process Chapman and Hall/CRC

[25]

Sutton RS and Barto AG Reinforcement learning: an introduction 1998 Cambridge MIT Press

[26]

Salodkar N, Bhorkar A, Karandikar A, and Borkar VS An on-line learning algorithm for energy efficient delay constrained scheduling over a fading channel IEEE J Sel Areas Commun 2008 26 4 732-742

[27]

Neely MJ (2010) Stochastic network optimization with application to communication and queueing systems, San rafael, CA USA: Morgan claypool

[28]

Gibilisco P, Hiai F, and Petz D Quantum covariance, quantum fisher information, and the uncertainty relations IEEE Trans Inform Theory 2009 55 1 439-443

[29]

Gorlatova M, Wallwater A, and Zussma G Networking low-power energy harvesting devices: Measurements and algorithms IEEE Trans Mobile Comput 2012 12 9 1853-1865

[30]

Mitchell TM (1997) Machine learning McGraw-Hill

Cited By

Taheri-abed SEftekhari Moghadam ARezvani M(2023)Machine learning-based computation offloading in edge and fog: a systematic reviewCluster Computing10.1007/s10586-023-04100-z26:5(3113-3144)Online publication date: 21-Jul-2023
https://dl.acm.org/doi/10.1007/s10586-023-04100-z
Chandrika PMekala MSrivastava G(2022)Edge resource slicing approaches for latency optimization in AI-edge orchestrationCluster Computing10.1007/s10586-022-03817-726:2(1659-1683)Online publication date: 30-Nov-2022
https://dl.acm.org/doi/10.1007/s10586-022-03817-7

Recommendations

Energy harvesting computation offloading game towards minimizing delay for mobile edge computing
Abstract
Mobile edge computing (MEC) has emerged for meeting the ever-increasing computation demands from mobile applications. Mobile users endowing with computation and energy harvesting (EH) capabilities, called EH devices, are desired in MEC ...
Highlights
- An energy harvesting computation offloading game (EHCOG) for delay minimization in MEC is studied.
A Heuristic Algorithm for Multi-site Computation Offloading in Mobile Cloud Computing

Due to limitation of mobile device in terms of battery life and processing power, Mobile Cloud Computing (MCC) has become an attractive choice to leverage this shortcoming as the mobile computation could be offloaded to the cloud, which is so-called ...
A Deep Reinforcement Learning Approach Towards Computation Offloading for Mobile Edge Computing
Human Centered Computing
Abstract
In order to improve the quality of service for users and reduce the energy consumption of the cloud computing environment, Mobile Edge Computing (MEC) is a promising paradigm by providing computing resources which is close to the end device in ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Mobile Networks and Applications

Mobile Networks and Applications Volume 27, Issue 3

Jun 2022

488 pages

ISSN:1383-469X

Issue’s Table of Contents

© Springer Science+Business Media, LLC, part of Springer Nature 2019.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 June 2022

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 29 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Taheri-abed SEftekhari Moghadam ARezvani M(2023)Machine learning-based computation offloading in edge and fog: a systematic reviewCluster Computing10.1007/s10586-023-04100-z26:5(3113-3144)Online publication date: 21-Jul-2023
https://dl.acm.org/doi/10.1007/s10586-023-04100-z
Chandrika PMekala MSrivastava G(2022)Edge resource slicing approaches for latency optimization in AI-edge orchestrationCluster Computing10.1007/s10586-022-03817-726:2(1659-1683)Online publication date: 30-Nov-2022
https://dl.acm.org/doi/10.1007/s10586-022-03817-7

View Options

View options

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 Issue’s Table of Contents