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Fair Computation Efficiency for OFDMA-Based Multiaccess Edge Computing Systems
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DOI：10.1109/LCOMM.2022.3174083.png)
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Abstract
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This letter investigates the joint optimization of task offloading and resource allocation for OFDMA-based multi-access edge computing systems. This joint optimization aims at maximizing the minimum computation efficiency, which is a ratio between the task data size to energy consumption, of all users to ensure fairness via adjusting transmit power, computation capacity and sub-channel allocations under the concrete delay, data size and energy consumption requirements. The formulated minimum computation efficiency maximization problem is a non-convex mixed-integer problem. To solve this problem, an iterative algorithm is proposed by alternatively solving the continuous sub-problem and the discrete sub-problem. For the continuous sub-problem about joint transmit power and computation capacity, a low-complexity bisection method is proposed to obtain the optimal solution by solving a series of feasibility problems. Then, the dual method is adopted to solve the discrete sub-problem about sub-channel association. Moreover, we acquire the closed-form optimal solution at each iteration. Simulation results verify that the proposed algorithm is superior over baseline schemes in terms of minimum computation efficiency.
Keywords:
Task analysis
Energy consumption
Computational efficiency
Resource management
Servers
Optimization
Delays
Multi-access edge computing
computation efficiency
task offloading
resource allocation
OFDMA
Journal
IF:
4.4
Papers:
1.3W
Citations:
2.2W
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Cited Papers
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art.png)
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