research-article

Workload management for dynamic mobile device clusters in edge femtoclouds

Authors:

Ellen W. Zegura,

Khaled A. HarrasAuthors Info & Claims

SEC '17: Proceedings of the Second ACM/IEEE Symposium on Edge Computing

Article No.: 6, Pages 1 - 14

https://doi.org/10.1145/3132211.3134455

Published: 12 October 2017 Publication History

Abstract

Edge computing offers an alternative to centralized, in-the-cloud compute services. Among the potential advantages of edge computing are lower latency that improves responsiveness, reduced wide-area network congestion, and possibly greater privacy by keeping data more local. In our previous work on Femtoclouds, we proposed taking advantage of clusters of devices that tend to be co-located in places such as public transit, classrooms or coffee shops. These clusters can perform computations for jobs generated from within or outside of the cluster. In this paper, we address the full requirements of workload management in Femtoclouds. These functions enable a Femtocloud to provide a service to job initiators that is similar to that provided by a centralized cloud service. We develop a system architecture that relies on the cloud to efficiently control and manage a Femtocloud. Within this architecture, we develop adaptive workload management mechanisms and algorithms to manage resources and effectively mask churn. We implement a prototype of our Femtocloud system on Android devices and utilize it to evaluate the overall system performance. We use simulation to isolate and study the impact of our workload management mechanisms and test the system at scale. Our prototype and simulation results demonstrate the efficiency of the Femtocloud workload management mechanisms especially in situations with potentially high churn. For instance, our mechanisms can reduce the average job completion time by up to 26% compared to similar mechanisms used in traditional cloud computing systems when used in situations that suggest high churn.

References

[1]

BOINC: Open-source software for volunteer computing. https://boinc.berkeley.edu/. (????). Online; accessed 23-April-2017.

[2]

Paul A Ashley, Anthony M Butler, Ghada M ELKeissi, and Leny Veliyathuparambil. 2017. Dynamic security sandboxing based on intruder intent. (2017). US Patent 9,535,731.

[3]

R. Balan, J. Flinn, M. Satyanarayanan, S. Sinnamohideen, and H. Yang. 2002. The case for cyber foraging. In ACM EW.

Digital Library

[4]

F. Bonomi, R. Milito, J. Zhu, and S. Addepalli. 2012. Fog computing and its role in the internet of things. In SIGCOMM MCC.

Digital Library

[5]

M. Chen, Y. Hao, Y. Li, C. Lai, and D. Wu. 2015. On the computation offloading at ad hoc cloudlet: architecture and service modes. IEEE Communications Magazine (2015).

[6]

B. Chun, S. Ihm, P. Maniatis, M. Naik, and A. Parti. 2011. CloneCloud: elastic execution between mobile device and cloud. In ACM EuroSys.

Digital Library

[7]

E. Cuervo, A. Balasubramanian, D. Cho, A. Wolman, S. Saroiu, R. Chandra, and P. Bahl. 2010. MAUI: making smartphones last longer with code offload. In ACM MobiSys.

Digital Library

[8]

C. Devi and R. Uthariaraj. 2016. Load Balancing in Cloud Computing Environment Using Improved Weighted Round Robin Algorithm for Nonpreemptive Dependent Tasks. The Scientific World Journal (2016).

[9]

H. T. Dinh, C. Lee, D. Niyato, and P. Wang. 2013. A survey of mobile cloud computing: architecture, applications, and approaches. Wireless communications and mobile computing (2013).

[10]

U. Drolia, R. Martins, J. Tan, A. Chheda, M. Sanghavi, R. Gandhi, and P. Narasimhan. 2013. The case for mobile edge-clouds. In IEEE UIC/ATC.

Digital Library

[11]

Zhangjie Fu, Kui Ren, Jiangang Shu, Xingming Sun, and Fengxiao Huang. 2016. Enabling personalized search over encrypted outsourced data with efficiency improvement. IEEE TPDS (2016).

[12]

Hend Gedawy, Sannan Tariq, Abderrahmen Mtibaa, and Khaled Harras. 2016. Cumulus: A distributed and flexible computing testbed for edge cloud computational offloading. In Cloudification of the Internet of Things (CIoT). IEEE.

[13]

Rosario Gennaro, Craig Gentry, and Bryan Parno. 2010. Non-interactive verifiable computing: Outsourcing computation to untrusted workers. In Annual Cryptology Conference. Springer.

[14]

Craig Gentry and others. 2009. Fully homomorphic encryption using ideal lattices. In STOC.

Digital Library

[15]

Martin Georgiev, Suman Jana, and Vitaly Shmatikov. 2015. Rethinking security of Web-based system applications. In ACM WWW.

Digital Library

[16]

M. S Gordon, D. Jamshidi, S. A Mahlke, M. Mao, and X. Chen. 2012. COMET: Code Offload by Migrating Execution Transparently. In OSDI.

[17]

K. Ha, P. Pillai, W. Richter, Y. Abe, and M. Satyanarayanan. 2013. Just-in-time provisioning for cyber foraging. In ACM MobiSys.

Digital Library

[18]

K. Habak, M. Ammar, K. A Harras, and E. Zegura. 2015. Femtoclouds: Leveraging mobile devices to provide cloud service at the edge. In IEEE CLOUD.

[19]

Karim Habak, Cong Shi, Ellen W Zegura, Khaled A Harras, and Mostafa Ammar. 2017. Elastic Mobile Device Clouds: Leveraging Mobile Devices to Provide Cloud Computing Services at the Edge. Fog for 5G and IoT (2017).

[20]

Ahmed E Kosba, Dimitrios Papadopoulos, Charalampos Papamanthou, Mahmoud F Sayed, Elaine Shi, and Nikos Triandopoulos. 2014. TRUESET: Faster Verifiable Set Computations. In USENIX Security.

[21]

K. Kumar and Y. Lu. 2010. Cloud computing for mobile users: Can offloading computation save energy? IEEE Computer (2010).

[22]

Y. Kwok and I. Ahmad. 1996. Dynamic critical-path scheduling: An effective technique for allocating task graphs to multiprocessors. IEEE TPDS (1996).

[23]

Abderrahmen Mtibaa, Khaled A. Harras, and Afnan Fahim. 2013. Towards Computational Offloading in Mobile Device Clouds. In IEEE CloudCom.

Digital Library

[24]

Michael Naehrig, Kristin Lauter, and Vinod Vaikuntanathan. 2011. Can homomorphic encryption be practical?. In Proceedings of the 3rd ACM workshop on Cloud computing security workshop. ACM.

Digital Library

[25]

T. Nishio, R. Shinkuma, T. Takahashi, and N. B. Mandayam. 2013. Service-oriented heterogeneous resource sharing for optimizing service latency in mobile cloud. In MobileCloud. ACM.

Digital Library

[26]

Maria Alejandra Rodriguez and Rajkumar Buyya. 2016. A taxonomy and survey on scheduling algorithms for scientific workflows in IaaS cloud computing environments. Concurrency and Computation: Practice and Experience (2016).

[27]

Ahmed Saeed, Mostafa Ammar, Khaled A Harras, and Ellen Zegura. 2015. Vision: The case for symbiosis in the internet of things. In Proceedings of the 6th International Workshop on Mobile Cloud Computing and Services. ACM.

Digital Library

[28]

M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies. 2009. The case for vm-based cloudlets in mobile computing. IEEE Pervasive Computing (2009).

[29]

John A Sharp. 1992. Data flow computing: theory and practice. Intellect Books.

[30]

William F Sharpe, Gordon J Alexander, and Jeffery V Bailey. 1999. Investments. Prentice-Hall Upper Saddle River, NJ.

[31]

C. Shi, K. Habak, P. Pandurangan, M. Ammar, M. Naik, and E. Zegura. 2014. Cosmos: computation offloading as a service for mobile devices. In ACM MobiHoc.

Digital Library

[32]

C. Shi, Va. Lakafosis, M. H. Ammar, and E. W. Zegura. 2012. Serendipity: enabling remote computing among intermittently connected mobile devices. In ACM MobiHoc.

Digital Library

[33]

I. Stojmenovic. 2014. Fog computing: A cloud to the ground support for smart things and machine-to-machine networks. In IEEE ATNAC.

[34]

I. Stojmenovic and S. Wen. 2014. The fog computing paradigm: Scenarios and security issues. In IEEE FedCSIS.

[35]

H. Topcuoglu, S. Hariri, and M. Wu. 2002. Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE TPDS (2002).

[36]

Stephen Tu, M Frans Kaashoek, Samuel Madden, and Nickolai Zeldovich. 2013. Processing analytical queries over encrypted data. In Proceedings of the VLDB Endowment.

Digital Library

[37]

Vinod Kumar Vavilapalli, Arun C Murthy, Chris Douglas, Sharad Agarwal, Mahadev Konar, Robert Evans, Thomas Graves, Jason Lowe, Hitesh Shah, Siddharth Seth, and others. 2013. Apache hadoop yarn: Yet another resource negotiator. In Proceedings of the 4th annual Symposium on Cloud Computing. ACM.

Digital Library

[38]

F. Wu, Q. Wu, and Y. Tan. 2015. Workflow scheduling in cloud: a survey. The Journal of Supercomputing (2015).

[39]

T. Zhang, A. Chowdhery, P. Bahl, K. Jamieson, and S. Banerjee. 2015. The design and implementation of a wireless video surveillance system. In MobiCom.

Digital Library

Cited By

Trigka MDritsas E(2025)Edge and Cloud Computing in Smart CitiesFuture Internet10.3390/fi1703011817:3(118)Online publication date: 6-Mar-2025
https://doi.org/10.3390/fi17030118
Gedawy HHarras KBui TTanveer T(2024)Toward Context-Aware Federated Learning Assessment: A Reality CheckIEEE Internet of Things Journal10.1109/JIOT.2023.333827511:7(12567-12578)Online publication date: 1-Apr-2024
https://doi.org/10.1109/JIOT.2023.3338275
Shahraki AOhlenforst TKreyß F(2023)When machine learning meets Network Management and Orchestration in Edge-based networking paradigmsJournal of Network and Computer Applications10.1016/j.jnca.2022.103558212:COnline publication date: 24-Mar-2023
https://dl.acm.org/doi/10.1016/j.jnca.2022.103558
Show More Cited By

Index Terms

Workload management for dynamic mobile device clusters in edge femtoclouds
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing theory, concepts and paradigms
      1. Mobile computing
      2. Ubiquitous computing

Recommendations

A Survey of Mobile Cloud Computing Applications: Perspectives and Challenges

As mobile computing has been developed for decades, a new model for mobile computing, namely, mobile cloud computing, emerges resulting from the marriage of powerful yet affordable mobile devices and cloud computing. In this paper we survey existing ...
Security and privacy challenges in mobile cloud computing

The rapid growth of mobile computing is seriously challenged by the resource constrained mobile devices. However, the growth of mobile computing can be enhanced by integrating mobile computing into cloud computing, and hence a new paradigm of computing ...
Towards the Decentralised Cloud: Survey on Approaches and Challenges for Mobile, Ad hoc, and Edge Computing

Cloud computing emerged as a centralised paradigm that made “infinite” computing resources available on demand. Nevertheless, the ever-increasing computing capacities present on smart connected things and devices calls for the decentralisation of Cloud ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SEC '17: Proceedings of the Second ACM/IEEE Symposium on Edge Computing

October 2017

365 pages

ISBN:9781450350877

DOI:10.1145/3132211

General Chair:
Junshan Zhang
Arizona State University
,
Program Chairs:
Mung Chiang
Purdue University and Princeton Edge Lab
,
Bruce Maggs
Akamai/Duke University

Copyright © 2017 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 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]

Sponsors

SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
IEEE-CS\DATC: IEEE Computer Society

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 October 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

US National Science Foundation
the Qatar National Research Fund, a member of Qatar Foundation

Conference

SEC '17

Sponsor:

SIGMOBILE
IEEE-CS\DATC

SEC '17: IEEE/ACM Symposium on Edge Computing

October 12 - 14, 2017

California, San Jose

Acceptance Rates

SEC '17 Paper Acceptance Rate 20 of 41 submissions, 49%;

Overall Acceptance Rate 40 of 100 submissions, 40%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

42
Total Citations
View Citations
778
Total Downloads

Downloads (Last 12 months)118
Downloads (Last 6 weeks)4

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Trigka MDritsas E(2025)Edge and Cloud Computing in Smart CitiesFuture Internet10.3390/fi1703011817:3(118)Online publication date: 6-Mar-2025
https://doi.org/10.3390/fi17030118
Gedawy HHarras KBui TTanveer T(2024)Toward Context-Aware Federated Learning Assessment: A Reality CheckIEEE Internet of Things Journal10.1109/JIOT.2023.333827511:7(12567-12578)Online publication date: 1-Apr-2024
https://doi.org/10.1109/JIOT.2023.3338275
Shahraki AOhlenforst TKreyß F(2023)When machine learning meets Network Management and Orchestration in Edge-based networking paradigmsJournal of Network and Computer Applications10.1016/j.jnca.2022.103558212:COnline publication date: 24-Mar-2023
https://dl.acm.org/doi/10.1016/j.jnca.2022.103558
Sankar Ramachandran GGarcia LKrishnamachari B(2023)Distributed Computing for Internet of Things Under Adversarial EnvironmentsIoT for Defense and National Security10.1002/9781119892199.ch15(285-306)Online publication date: 6-Jan-2023
https://doi.org/10.1002/9781119892199.ch15
Hoque MHarras K(2022)WebAssembly for Edge Computing: Potential and ChallengesIEEE Communications Standards Magazine10.1109/MCOMSTD.0001.20000686:4(68-73)Online publication date: Dec-2022
https://doi.org/10.1109/MCOMSTD.0001.2000068
Gedawy HHarras KErbad A(2022)On Leveraging FemtoClouds for Federated LearningIEEE Internet of Things Magazine10.1109/IOTM.001.21001365:3(68-75)Online publication date: Sep-2022
https://doi.org/10.1109/IOTM.001.2100136
Sharif ZJung LAyaz MYahya MPitafi S(2022)A Taxonomy for Resource Management in Edge Computing, Applications and Future Realms2022 International Conference on Digital Transformation and Intelligence (ICDI)10.1109/ICDI57181.2022.10007397(46-52)Online publication date: 1-Dec-2022
https://doi.org/10.1109/ICDI57181.2022.10007397
Gedawy HHarras KErbad A(2022)Leveraging Semi-Connected Devices To Enhance Federated Learning2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)10.1109/ICCSPA55860.2022.10019249(1-6)Online publication date: 27-Dec-2022
https://doi.org/10.1109/ICCSPA55860.2022.10019249
Torres GTourani RMtibaa AStelmakh DMisra SSrikanteswara SZhang YHoque S(2022)uDiscover: User-Driven Service Discovery in Pervasive Edge Computing using NDN2022 IEEE International Conference on Edge Computing and Communications (EDGE)10.1109/EDGE55608.2022.00022(77-82)Online publication date: Jul-2022
https://doi.org/10.1109/EDGE55608.2022.00022
Gedawy HElgazar AHarras K(2022)MAESTRO: Orchestrating Computational Offloading to Multiple FemtoClouds in Various Communication EnvironmentsIEEE Access10.1109/ACCESS.2022.315207510(27096-27112)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3152075
Show More Cited By

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