A Novel Levy Walk-based Framework for Scheduling Power-intensive Mobile Edge Computing Tasks

Mobile edge computing (MEC) enables computationally intensive tasks to be processed at the network edge to provide low-latency services. However, inefficient task scheduling can negatively impact performance metrics like completion time and energy consumption. This paper proposes CAPL-MEC, an adapti...

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Bibliographic Details
Published in:Journal of grid computing 2024-12, Vol.22 (4), p.69, Article 69
Main Authors: Younesi, Abolfazl, Fazli, Mohammad Amin, Ejlali, Alireza
Format: Article
Language:English
Subjects:
Adaptive algorithms
Adaptive systems
Completion time
Computer Science
Edge computing
Energy consumption
Management of Computing and Information Systems
Mobile computing
Performance measurement
Power management
Processor Architectures
Resource allocation
Resource scheduling
Scheduling
Task scheduling
User Interfaces and Human Computer Interaction
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Summary:Mobile edge computing (MEC) enables computationally intensive tasks to be processed at the network edge to provide low-latency services. However, inefficient task scheduling can negatively impact performance metrics like completion time and energy consumption. This paper proposes CAPL-MEC, an adaptive task scheduling framework that utilizes Levy walk modeling to address mobility patterns in MEC. The system model generates random edge nodes within defined bounds to simulate heterogeneous environments. A power consumption model is also presented to optimize dynamic and static power. Device mobility follows an adaptive Levy walk distribution where the power law exponent is time-varying. Latency and reliability (task replication) models are also defined. The CAPL-MEC algorithm utilizes an adaptive Levy walk approach to predict device locations and schedule tasks accordingly. A hybrid task allocation strategy combines proximity awareness, mobile-centric execution, and handovers between mobile and edge devices. Simulations evaluate CAPL-MEC across metrics like completion time, energy consumption, CPU and memory utilization, and wait times under various configurations. Results demonstrate that CAPL-MEC outperforms other algorithms by minimizing completion time through efficient resource allocation based on predicted mobility patterns. Energy consumption is also reduced through power-conscious scheduling. The framework presents a practical and adaptable solution for task scheduling in dynamic MEC environments.
ISSN:1570-7873
1572-9184
DOI:10.1007/s10723-024-09786-y