Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing

By Chen-Feng Liu, Medhi Bennis, Merouane Debbah, H. Vincent Poor. IEEE Transactions on Communications, 2018

368 citations recorded by the source corpus.

To overcome devices’ limitations in performing computation-intense applications, mobile edge computing (MEC) enables users to offload tasks to proximal MEC servers for faster task computation. However, the current MEC system design is based on average-based metrics, which fails to account for the ultra-reliable low-latency requirements in mission-critical applications. To tackle this, this paper proposes a new system design, where probabilistic and statistical constraints are imposed on task queue lengths, by applying extreme value theory. The aim is to minimize users’ power consumption while trading off the allocated resources for local computation and task offloading. Due to wireless channel dynamics, users are reassociated to MEC servers in order to offload tasks using higher rates or accessing proximal servers. In this regard, a user–server association policy is proposed, taking into account the channel quality as well as the servers’ computation capabilities and workloads. By marrying tools from Lyapunov optimization and matching theory, a two-timescale mechanism is proposed, where a user–server association is solved in the long timescale, while a dynamic task offloading and resource allocation policy are executed in the short timescale. The simulation results corroborate the effectiveness of the proposed approach by guaranteeing highly reliable task computation and lower delay performance, compared to several baselines.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/c9e16aaddd5d25e5f1f1f015f4b777fe1d4c6bfa

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Citations in the corpus

Edge Computing with Artificial Intelligence: A Machine Learning Perspective (2022, methodology): “In solving the problem of resource allocation and computing offloading in EC, some researchers adopt Lyapunov optimization algorithm [34] to find the optimal decision [35, 36].”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, methodology): “Considering the system dynamics, including task arrival dynamics in the network layer and the wireless channel dynamics in the physical layer, cross-layer optimization is needed to minimize the end-to-end delay for edge deviceserver co-inference [84], [85].”