Abstract
Offloading of all or part of any cloud service computation, when running processing-intensive Mobile Cloud Computing Services (MCCS), to servers in the cloud introduces time delay and communication overhead. Edge computing has emerged to resolve these issues, by shifting part of the service computation from the cloud to edge servers near the end-devices. An innovative Smart Cooperative Computation Offloading Framework (SCCOF), to leverage computation offloading to the cloud has been previously published by us [1]. This paper proposes SOSE; a solution to offload sub-tasks to nearby devices, on-the-go, that will form an “edge computing resource, we call SOSE_EDGE” so to enable the execution of the MCCS on any end-device. This is achieved by using short-range wireless connectivity to network between available cooperative end-devices. SOSE can partition the MCCS workload to execute among a pool of Offloadees (nearby end-devises; such as Smartphones, tablets, and PC’s), so to achieve minimum latency and improve performance while reducing battery power consumption of the Offloader (end-device that is running the MCCS). SOSE established the edge computing resource by: (1) profiling and partitioning the service workload to sub-tasks, based on a complexity relationship we developed. (2) Establishing peer2peer remote connection, with the available cooperative nearby Offloadees, based on SOSE assessment criteria. (3) Migrating the sub-tasks to the target edge devices in parallel and retrieve results. Scenarios and experiments to evaluate SOSE show that a significant improvement, in terms of processing time (>40%) and battery power consumption (>28%), has been achieved when compared with cloud offloading solutions.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Al-ameri, A., Lami, I.A.: SCCOF: smart cooperative computation offloading framework for mobile cloud computing services. In: the 8th Annual International Conference: Big Data, Cloud and Security (2017)
Saad, S.M., Nandedkar, S.C.: Energy efficient mobile cloud computing (2014)
Elmannai, W., Elleithy, K.: Sensor-based assistive devices for visually-impaired people: current status, challenges, and future directions. Sensors 17(3), 565 (2017)
Dwivedi, A., et al.: Internet of Things’ (IoT’s) impact on decision oriented applications of big data sentiment analysis. In: 2018 3rd International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU). IEEE (2018)
Wei, X., et al.: MVR: an architecture for computation offloading in mobile edge computing. In: the IEEE International Conference on Edge Computing (2017)
Calo, S.B., et al.: Edge computing architecture for applying AI to IoT. In: 2017 IEEE International Conference on Big Data (Big Data). IEEE (2017)
Amazon Rekognition: Developer Guide. http://docs.aws.amazon.com/rekognition/latest/dg/rekognition. Accessed January 2019
Chen, X., et al.: Thriftyedge: resource-efficient edge computing for intelligent IoT applications. IEEE Netw. 32(1), 61–65 (2018)
Li, H., Ota, K., Dong, M.: Learning IoT in edge: deep learning for the internet of things with edge computing. IEEE Netw. 32(1), 96–101 (2018)
Ko, K., et al.: DisCO: a distributed and concurrent offloading framework for mobile edge cloud computing. In: 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN). IEEE (2017)
Nearby Connections API. https://developers.google.com/nearby/connections/android/exchange-data. Accessed July 2018
Wang, X., Chen, X., Wu, W., An, N., Wang, L.: Cooperative application execution in mobile cloud computing: a stackelberg game approach. IEEE Commun. Lett. 20, 946–949 (2016)
Sirivianos, M., et al.: Dandelion: cooperative content distribution with robust incentives. In: USENIX Annual Technical Conference, vol. 7 (2007)
Thu, M.S.Z., Htoon, E.C.: Cost solving model in computation offloading decision algorithm. In: 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). IEEE (2018)
Kumar, K., Lu, Y.-H.: Cloud computing for mobile users: can offloading computation save energy? Computer 43, 51–56 (2010)
BroadbandChecker. http://www.broadbandspeedchecker.co.uk. Accessed November 2017
Kosta, S., Aucinas, A., Hui, P., Mortier, R., Zhang, X.: ThinkAir: dynamic resource allocation and parallel execution in the cloud for mobile code offloading. In: 2012 Proceedings of the IEEE INFOCOM (2012)
Luzuriaga, J., et al.: Evaluating computation offloading trade-offs in mobile cloud computing: a sample application. In: Proceedings of the 4th International Conference on Cloud Computing, GRIDs, Virtualization (2013)
Acknowledgment
Gratitude to the University of Basra, and MOHESR (Ministry of Higher Education and Scientific Research) for sponsoring this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Al-ameri, A., Lami, I.A. (2019). SOSE: Smart Offloading Scheme Using Computing Resources of Nearby Wireless Devices for Edge Computing Services. In: Miraz, M., Excell, P., Ware, A., Soomro, S., Ali, M. (eds) Emerging Technologies in Computing. iCETiC 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 285. Springer, Cham. https://doi.org/10.1007/978-3-030-23943-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-23943-5_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23942-8
Online ISBN: 978-3-030-23943-5
eBook Packages: Computer ScienceComputer Science (R0)