Multi-Objective Resource Optimization for Hierarchical Mobile Edge Computing
By Umair Yaqub, Sameh Sorour. 2018
15 citations recorded by the source corpus.
Mobile edge computing (MEC) enables the computation of complex tasks efficiently on end-devices. It has been shown that MEC can potentially reduce the delay and offer energy-efficient operation. So far, the research has been focused on or limited to multi-server optimization for partial offloading or competing to offload to the best server among different sets of mobile device. In this paper, we propose a hierarchical structure where user devices that have available resources can act as servers without the need to offload to the cloud. Results show that this model improves the system utility. Furthermore, there is a tradeoff between the number of users in the network and the number of peers willing to accept tasks. As the number of users increase, the utility of the hierarchical system keeps increasing whereas the simple MEC system utility saturates.
Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition
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In Computer Networks and Communications
In Computer Vision and Pattern Recognition
Citations in the corpus
- Adaptive Task Allocation for Asynchronous Federated Mobile Edge Learning (2019, methodology): “This paradigm of edge processing has been supported by the latest works in literature about MEC and Hierarchical-MEC (H-MEC) [3]–[5].”
- Task Allocation for Mobile Federated and Offloaded Learning with Energy and Delay Constraints (2020, methodology): “The energy consumed by learner k in each iteration of the learning iteration on a sample size of dk is given by: ek = μXkfk ζ−1 = μdkCmfk ζ−1, k ∈ κ [17] (3) where μ is the on-board chip capacitance (typically 10−11 F) and ζ = 2.”
- Adaptive Task Allocation for Mobile Edge Learning (2018, methodology): “This booming need for edge processing is supported by the late advancements in mobile edge computing (MEC) [3]–[6] and hierarchical MEC (H-MEC) [7], [8].”