TPD: Temporal and Positional Computation Offloading with Dynamic and Dependent Tasks

With the rapid development of wireless communication technologies and the proliferation of the urban Internet of Things (IoT), the paradigm of mobile computing has been shifting from centralized clouds to edge networks. As an enabling paradigm for computation-intensive and latency-sensitive computat...

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Autores principales:	Mingzhi Wang, Tao Wu, Xiaochen Fan, Penghao Sun, Yuben Qu, Panlong Yang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology T Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/e41f5c98c54948f49e0fb3b0b4fb50cc
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spelling	oai:doaj.org-article:e41f5c98c54948f49e0fb3b0b4fb50cc2021-11-22T01:11:18ZTPD: Temporal and Positional Computation Offloading with Dynamic and Dependent Tasks1530-867710.1155/2021/3877285https://doaj.org/article/e41f5c98c54948f49e0fb3b0b4fb50cc2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3877285https://doaj.org/toc/1530-8677With the rapid development of wireless communication technologies and the proliferation of the urban Internet of Things (IoT), the paradigm of mobile computing has been shifting from centralized clouds to edge networks. As an enabling paradigm for computation-intensive and latency-sensitive computation tasks, mobile edge computing (MEC) can provide in-proximity computing services for resource-constrained IoT devices. Nevertheless, it remains challenging to optimize computation offloading from IoT devices to heterogeneous edge servers, considering complex intertask dependency, limited bandwidth, and dynamic networks. In this paper, we address the above challenges in MEC with TPD, that is, temporal and positional computation offloading with dynamic-dependent tasks. In particular, we investigate channel interference and intertask dependency by considering the position and moment of computation offloading simultaneously. We define a novel criterion for assessing the criticality of each task, and we identify the critical path based on a directed acyclic graph of all tasks. Furthermore, we propose an online algorithm for finding the optimal computation offloading strategy with intertask dependency and adjusting the strategy in real-time when facing dynamic tasks. Extensive simulation results show that our algorithm reduces significantly the time to complete all tasks by 30–60% in different scenarios and takes less time to adjust the offloading strategy in dynamic MEC systems.Mingzhi WangTao WuXiaochen FanPenghao SunYuben QuPanlong YangHindawi-WileyarticleTechnologyTTelecommunicationTK5101-6720ENWireless Communications and Mobile Computing, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Telecommunication TK5101-6720
spellingShingle	Technology T Telecommunication TK5101-6720 Mingzhi Wang Tao Wu Xiaochen Fan Penghao Sun Yuben Qu Panlong Yang TPD: Temporal and Positional Computation Offloading with Dynamic and Dependent Tasks
description	With the rapid development of wireless communication technologies and the proliferation of the urban Internet of Things (IoT), the paradigm of mobile computing has been shifting from centralized clouds to edge networks. As an enabling paradigm for computation-intensive and latency-sensitive computation tasks, mobile edge computing (MEC) can provide in-proximity computing services for resource-constrained IoT devices. Nevertheless, it remains challenging to optimize computation offloading from IoT devices to heterogeneous edge servers, considering complex intertask dependency, limited bandwidth, and dynamic networks. In this paper, we address the above challenges in MEC with TPD, that is, temporal and positional computation offloading with dynamic-dependent tasks. In particular, we investigate channel interference and intertask dependency by considering the position and moment of computation offloading simultaneously. We define a novel criterion for assessing the criticality of each task, and we identify the critical path based on a directed acyclic graph of all tasks. Furthermore, we propose an online algorithm for finding the optimal computation offloading strategy with intertask dependency and adjusting the strategy in real-time when facing dynamic tasks. Extensive simulation results show that our algorithm reduces significantly the time to complete all tasks by 30–60% in different scenarios and takes less time to adjust the offloading strategy in dynamic MEC systems.
format	article
author	Mingzhi Wang Tao Wu Xiaochen Fan Penghao Sun Yuben Qu Panlong Yang
author_facet	Mingzhi Wang Tao Wu Xiaochen Fan Penghao Sun Yuben Qu Panlong Yang
author_sort	Mingzhi Wang
title	TPD: Temporal and Positional Computation Offloading with Dynamic and Dependent Tasks
title_short	TPD: Temporal and Positional Computation Offloading with Dynamic and Dependent Tasks
title_full	TPD: Temporal and Positional Computation Offloading with Dynamic and Dependent Tasks
title_fullStr	TPD: Temporal and Positional Computation Offloading with Dynamic and Dependent Tasks
title_full_unstemmed	TPD: Temporal and Positional Computation Offloading with Dynamic and Dependent Tasks
title_sort	tpd: temporal and positional computation offloading with dynamic and dependent tasks
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/e41f5c98c54948f49e0fb3b0b4fb50cc
work_keys_str_mv	AT mingzhiwang tpdtemporalandpositionalcomputationoffloadingwithdynamicanddependenttasks AT taowu tpdtemporalandpositionalcomputationoffloadingwithdynamicanddependenttasks AT xiaochenfan tpdtemporalandpositionalcomputationoffloadingwithdynamicanddependenttasks AT penghaosun tpdtemporalandpositionalcomputationoffloadingwithdynamicanddependenttasks AT yubenqu tpdtemporalandpositionalcomputationoffloadingwithdynamicanddependenttasks AT panlongyang tpdtemporalandpositionalcomputationoffloadingwithdynamicanddependenttasks
_version_	1718418313779871744

TPD: Temporal and Positional Computation Offloading with Dynamic and Dependent Tasks

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