research-article

Elastic Offloading of Multitasking Applications to Mobile Edge Computing

Authors:

Houssemeddine Mazouzi,

Khaled BoussettaAuthors Info & Claims

MSWIM '19: Proceedings of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

Pages 307 - 314

https://doi.org/10.1145/3345768.3355926

Published: 25 November 2019 Publication History

Abstract

This paper focuses on offloading multitasking applications to a Mobile Edge Computing (MEC) environment. We address this issue through a mobile user's perspective that is seeking to obtain the execution result of a resource-hungry multitasking application, possibly through offloading some tasks to a mobile multiple edge servers' environment. The completion time of the application is constrained by a predefined strict deadline and the offloading decision aims to minimize the terminal's energy consumption. Moreover, the multitasking application is modeled by a weighted Directed Acyclic Graph (DAG) which characterizes the dependencies between tasks. To tackle this issue, we first model the multitasking offloading problem in a mobile multiple edge servers' environment as a Zero-one Integer Programming problem. Then, we propose an efficient adaptive offloading algorithm, named eTOMEC (Elastic Tasks graph Offloading for MEC), which decides which task must be offloaded and accordingly selects the edge server that will perform the task execution. Compared to many state-of-the-art offloading approaches, our proposal allows parallel offloading of tasks to several edge servers. Assessment results show that our proposal achieves better performances in terms of completion time for the application and energy consumption of the user's terminal.

References

[1]

Nancy M Amato and Ping An. 2000. Task scheduling and parallel mesh-sweeps in transport computations. TR00-009, Department of Computer Science, Texas A&M University (2000).

[2]

Rajesh Krishna Balan, Mahadev Satyanarayanan, So Young Park, and Tadashi Okoshi. 2003. Tactics-based remote execution for mobile computing. In Proceedings of the 1st international conference on Mobile systems, applications and services. ACM, 273--286.

Digital Library

[3]

Aaron Carroll, Gernot Heiser, et almbox. 2010. An Analysis of Power Consumption in a Smartphone. In USENIX annual technical conference, Vol. 14. Boston, MA, 21--21.

[4]

Meng-Hsi Chen, Ben Liang, and Min Dong. 2016. Joint offloading decision and resource allocation for multi-user multi-task mobile cloud. In 2016 IEEE International Conference on Communications (ICC). IEEE, 1--6.

[5]

Weiwei Chen, Dong Wang, and Keqin Li. 2018. Multi-user multi-task computation offloading in green mobile edge cloud computing. IEEE Transactions on Services Computing (2018).

[6]

Thinh Quang Dinh, Jianhua Tang, Quang Duy La, and Tony QS Quek. 2017. Offloading in mobile edge computing: Task allocation and computational frequency scaling. IEEE Transactions on Communications, Vol. 65, 8 (2017), 3571--3584.

[7]

Michael R. Garey and David S. Johnson. 1990. Computers and Intractability; A Guide to the Theory of NP-Completeness .W. H. Freeman & Co., New York, NY, USA.

[8]

Andrea Goldsmith. 2005. Wireless Communications .Cambridge University Press. https://doi.org/10.1017/CBO9780511841224

[9]

Songtao Guo, Bin Xiao, Yuanyuan Yang, and Yang Yang. 2016. Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing. In IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications. IEEE, 1--9.

Digital Library

[10]

Dong Huang, Ping Wang, and Dusit Niyato. 2012. A dynamic offloading algorithm for mobile computing. IEEE Transactions on Wireless Communications, Vol. 11, 6 (2012), 1991--1995.

[11]

Vinay Kumar, CP Katti, and PC Saxena. 2014. A Novel Task Scheduling Algorithm for Heterogeneous Computing. International Journal of Computer Applications, Vol. 85, 18 (2014).

[12]

S. Eman Mahmoodi, R.N. Uma, and K.P. Subbalakshmi. 2016. Optimal Joint Scheduling and Cloud Offloading for Mobile Applications. IEEE Transactions on Cloud Computing, Vol. 7161, c (2016), 1--1.

[13]

Houssemeddine Mazouzi, Nadjib Achir, and Khaled Boussetta. 2018. Maximizing Mobiles Energy Saving Through Tasks Optimal Offloading Placement in two-tier Cloud. In Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems. ACM, 137--145.

Digital Library

[14]

Houssemeddine Mazouzi, Nadjib Achir, and Khaled Boussetta. 2019. Dm2-ecop: An efficient computation offloading policy for multi-user multi-cloudlet mobile edge computing environment. ACM Transactions on Internet Technology (TOIT), Vol. 19, 2 (2019), 24.

Digital Library

[15]

M Yusuf Özkaya, Anne Benoit, Bora Ucc ar, Julien Herrmann, and Umit Catalyurek. 2019. A scalable clustering-based task scheduler for homogeneous processors using DAG partitioning. In IPDPS 2019--33rd IEEE International Parallel & Distributed Processing Symposium. IEEE, 1--11.

[16]

Peng Sun, Heli Zhang, Hong Ji, and Xi Li. 2018. Task Allocation for Multi-APs with Mobile Edge Computing. In 2018 IEEE/CIC International Conference on Communications in China (ICCC Workshops). IEEE, 314--318.

[17]

Tuyen X Tran and Dario Pompili. 2019. Joint task offloading and resource allocation for multi-server mobile-edge computing networks. IEEE Transactions on Vehicular Technology, Vol. 68, 1 (2019), 856--868.

[18]

H. Wang and O. Sinnen. 2018. List-Scheduling versus Cluster-Scheduling. IEEE Transactions on Parallel and Distributed Systems, Vol. 29, 8 (Aug 2018), 1736--1749.

[19]

Lei Yang, Bo Liu, Jiannong Cao, Yuvraj Sahni, and Zhenyu Wang. 2019. Joint computation partitioning and resource allocation for latency sensitive applications in mobile edge clouds. IEEE Transactions on Services Computing (2019).

Cited By

Patanè RAchir NAraldo ABoukhatem L(2024)Can Vehicular Cloud Replace Edge Computing?2024 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC57260.2024.10570889(1-6)Online publication date: 21-Apr-2024
https://doi.org/10.1109/WCNC57260.2024.10570889
Xu CLv MZhang KCao KWang GWei MPeng B(2024)Energy Consumption and Time-Delay Optimization of Dependency-Aware Tasks Offloading for Industry 5.0 ApplicationsIEEE Transactions on Consumer Electronics10.1109/TCE.2023.333862070:1(1590-1600)Online publication date: Feb-2024
https://doi.org/10.1109/TCE.2023.3338620
Zhu QGuo J(2024)Research on Dependent Task Offloading Based on Deep Reinforcement Learning2024 6th International Conference on Natural Language Processing (ICNLP)10.1109/ICNLP60986.2024.10692374(705-709)Online publication date: 22-Mar-2024
https://doi.org/10.1109/ICNLP60986.2024.10692374
Show More Cited By

Index Terms

Elastic Offloading of Multitasking Applications to Mobile Edge Computing
1. Networks
  1. Network services
    1. Cloud computing

Recommendations

Modelling Task Offloading Mobile Edge Computing
ICCDE '22: Proceedings of the 2022 8th International Conference on Computing and Data Engineering

With the rapid growth of mobile devices (such as smart phones and IoT devices) and the upcoming 5G era, it has been considered that edge computing will play a significant role, which together with the Cloud server forms the Mobile Edge Computing (MEC) ...
An energy- and cost-aware computation offloading method for workflow applications in mobile edge computing
Abstract
Mobile edge computing is becoming a promising computing architecture to overcome the resource limitation of mobile devices and bandwidth bottleneck of the core networks in mobile cloud computing. Although offloading applications to the cloud can ...
Adaptively Offloading the Software for Mobile Edge Computing
WWW '22: Companion Proceedings of the Web Conference 2022

In mobile edge computing (MEC), computation offloading is a promising way to support those resource-constrained mobile devices, since it moves some time-consuming computation activities to nearby edge servers. Owing to the geographical distribution of ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MSWIM '19: Proceedings of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

November 2019

340 pages

ISBN:9781450369046

DOI:10.1145/3345768

General Chair:
Antonio A. F. Loureiro
Federal University of Minas Gerais, Brazil
,
Program Chairs:
Salil Kanhere
University of New South Wales, Australia
,
Paolo Bellavista
University of Bologna, Italy

Copyright © 2019 ACM.

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 the author(s) 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].

Sponsors

SIGSIM: ACM Special Interest Group on Simulation and Modeling

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 November 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

MSWiM '19

Sponsor:

SIGSIM

MSWiM '19: 22nd Int'l ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

November 25 - 29, 2019

FL, Miami Beach, USA

Acceptance Rates

Overall Acceptance Rate 398 of 1,577 submissions, 25%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
265
Total Downloads

Downloads (Last 12 months)12
Downloads (Last 6 weeks)2

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Patanè RAchir NAraldo ABoukhatem L(2024)Can Vehicular Cloud Replace Edge Computing?2024 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC57260.2024.10570889(1-6)Online publication date: 21-Apr-2024
https://doi.org/10.1109/WCNC57260.2024.10570889
Xu CLv MZhang KCao KWang GWei MPeng B(2024)Energy Consumption and Time-Delay Optimization of Dependency-Aware Tasks Offloading for Industry 5.0 ApplicationsIEEE Transactions on Consumer Electronics10.1109/TCE.2023.333862070:1(1590-1600)Online publication date: Feb-2024
https://doi.org/10.1109/TCE.2023.3338620
Zhu QGuo J(2024)Research on Dependent Task Offloading Based on Deep Reinforcement Learning2024 6th International Conference on Natural Language Processing (ICNLP)10.1109/ICNLP60986.2024.10692374(705-709)Online publication date: 22-Mar-2024
https://doi.org/10.1109/ICNLP60986.2024.10692374
de Queiroz Gde Rezende JBarbosa V(2024)A flexible algorithm to offload DAG applications for edge computingJournal of Network and Computer Applications10.1016/j.jnca.2023.103791222(103791)Online publication date: Feb-2024
https://doi.org/10.1016/j.jnca.2023.103791
Silva CMagaia NGrilo ABoukerche ADe Rango FSun Z(2023)Task Offloading Optimization in Mobile Edge Computing based on Deep Reinforcement LearningProceedings of the Int'l ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems10.1145/3616388.3617539(109-118)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1145/3616388.3617539
Vicenzi JKorol GJordan MMorais WAli HFreitas ERutzig MBeck A(2023)Dynamic Offloading for Improved Performance and Energy Efficiency in Heterogeneous IoT-Edge-Cloud Continuum2023 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)10.1109/ISVLSI59464.2023.10238564(1-6)Online publication date: 20-Jun-2023
https://doi.org/10.1109/ISVLSI59464.2023.10238564
Gao YLiu X(2022)Location Aware Workflow Migration Based on Deep Reinforcement Learning in Mobile Edge ComputingAlgorithms and Architectures for Parallel Processing10.1007/978-3-030-95384-3_32(509-528)Online publication date: 23-Feb-2022
https://doi.org/10.1007/978-3-030-95384-3_32
Qian WCoutinho RChatzimisios PCoutinho RNotare M(2021)On the Design of Edge-Assisted Mobile IoT Augmented and Mixed Reality ApplicationsProceedings of the 17th ACM Symposium on QoS and Security for Wireless and Mobile Networks10.1145/3479242.3487326(131-136)Online publication date: 22-Nov-2021
https://dl.acm.org/doi/10.1145/3479242.3487326
Li JZhou FZhang GFeng LLi WYu P(2021)Resource Allocation for Componentized Multimedia Service in Ubiquitous Computing Power Environment2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)10.1109/BMSB53066.2021.9547193(1-6)Online publication date: 4-Aug-2021
https://doi.org/10.1109/BMSB53066.2021.9547193
Boukerche AKantarci BKaptan C(2020)Towards ensuring the reliability and dependability of vehicular crowd-sensing data in GPS-less location trackingPervasive and Mobile Computing10.1016/j.pmcj.2020.10124868:COnline publication date: 1-Oct-2020
https://dl.acm.org/doi/10.1016/j.pmcj.2020.101248

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

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten