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A Global Orchestration Matching Framework for Energy-Efficient Multi-Access Edge Computing
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AI
DOI：10.1109/CloudNet53349.2021.9657120.png)
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Multi-access edge computing (MEC) enables mobile units (MUs) to offload computation tasks to edge servers nearby. This translates in energy savings for the MUs, but creates a joint problem of offloading decision making and allocation of the shared communication and computation resources. In a MEC scenario with multiple MUs, multiple access points and multiple cloudlets the complexity of this joint problem grows rapidly with the number of entities in the network. The complexity increases even further when some MUs have a higher incentive to offload tasks due to a low battery level and are willing to pay in exchange for more resources. Our proposed energy-minimization approach with a flexible maximum offloading time constraint is based on matching theory. A global orchestrator (GO) collects all the system state information and coordinates the offloading preferences of the MUs. A MU can lower the maximum time constraint by a payment. The GO allocates the shared communication and computation resources accordingly to satisfy the time constraint. The computation load of the algorithm at each MU is reduced to a minimum as each MU only has to take a simple offloading decision based on its task properties and payment willingness. In numerical simulations, the proposed matching approach and flexible resource allocation scheme is tested for fast and reliable convergence, even in large networks with hundreds of MUs. Furthermore, the matching algorithm, tested with different resource allocation strategies, shows a significant improvement in terms of energy-efficiency over the considered reference schemes.
Keyword:
RESOURCE-ALLOCATION
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