Mobility-Aware Joint Task Scheduling and Resource Allocation for Cooperative Mobile Edge Computing

Mobile edge computing (MEC) has emerged as a new paradigm to assist low latency services by enabling computation offloading at the network edge. Nevertheless, human mobility can significantly impact the offloading decision and performance in MEC networks. In this context, we propose device-to-device...

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Veröffentlicht in:IEEE transactions on wireless communications Jg. 20; H. 1; S. 360 - 374
Hauptverfasser: Saleem, Umber, Liu, Yu, Jangsher, Sobia, Li, Yong, Jiang, Tao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Computation offloading
Device-to-device communication
device-to-device cooperation
Edge computing
Electronic devices
Evolutionary algorithms
Genetic algorithms
Heuristic task scheduling
human mobility
latency minimization
Linear programming
Mixed integer
Mobile computing
Mobile edge computing
Mobility
Network latency
Nonlinear programming
Processor scheduling
Resource allocation
Resource management
Resource scheduling
Scheduling
Servers
Task analysis
task scheduling
Trajectory
ISSN:1536-1276, 1558-2248
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Zusammenfassung:Mobile edge computing (MEC) has emerged as a new paradigm to assist low latency services by enabling computation offloading at the network edge. Nevertheless, human mobility can significantly impact the offloading decision and performance in MEC networks. In this context, we propose device-to-device (D2D) cooperation based MEC to expedite the task execution of mobile user by leveraging proximity-aware task offloading. However, user mobility in such distributed architecture results in dynamic offloading decision that instigates mobility-aware task scheduling in our proposed framework. We jointly formulate task assignment and power allocation to minimize the total task execution latency by taking account of user mobility, distributed resources, tasks properties, and energy constraint of the user device. We first propose Genetic Algorithm (GA)-based evolutionary scheme to solve our formulated mixed-integer non-linear programming (MINLP) problem. Then we propose a heuristic named mobility-aware task scheduling (MATS) to obtain effective task assignment with low complexity. The extensive evaluation under realistic human mobility trajectories provides useful insights into the performance of our schemes and demonstrates that, both GA and MATS achieve better latency than other baseline schemes while satisfying the energy constraint of mobile device.
Bibliographie:ObjectType-Article-1
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2020.3024538