An Efficient Algorithm for Resource Allocation in Mobile Edge Computing Based on Convex Optimization and Karush–Kuhn–Tucker Method

Mobile edge computing (MEC) is receiving more attention than centralized cloud computing due to the massive increase in transmission and compute requirements in 5G vehicle networks. It offers a significant amount of processing and storage resources to the edge of networks, offloading applications fr...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2023, Vol.2023, p.1-15
Main Authors: Wang, Kaijing, Akhtar, Shelily F., Al-Zahrani, Fahad Ahmed
Format: Article
Language:English
Subjects:
Algorithms
Cloud computing
Communication
Computation offloading
Continuity (mathematics)
Convex analysis
Convexity
Decision making
Edge computing
Employment
Energy consumption
Internet of Things
Kuhn-Tucker method
Linear programming
Mobile computing
Natural language processing
Optimization
Resource allocation
Spectrum allocation
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Summary:Mobile edge computing (MEC) is receiving more attention than centralized cloud computing due to the massive increase in transmission and compute requirements in 5G vehicle networks. It offers a significant amount of processing and storage resources to the edge of networks, offloading applications from vehicle terminals that are computation-intensive and delay-sensitive. For devices with limited resources, it uses edge resources to provide computationally heavy operations while conserving energy. This paper proposes a novel approach for computing offloading in MEC. To effectively optimize the MEC resources, this paper proposes a novel algorithm. First, the joint optimization and service cache decision subproblems were determined from continuous and discrete variables. Then, the near-optimal solution is determined from the subproblems through convex optimization and Karush–Kuhn–Tucker method. Simulation results show that the proposed algorithm has better computational offloading and resource allocation performance as compared to existing algorithms.
ISSN:1076-2787
1099-0526
DOI:10.1155/2023/9604454