Multi-Cell Mobile Edge Computing: Joint Service Migration and Resource Allocation
Zezu Liang, Yuan Liu, Tat-Ming Lok, and Kaibin Huang

TL;DR
This paper proposes an optimized policy for service migration and resource allocation in multi-cell mobile edge computing, improving throughput and reducing migration costs amid user mobility and network interference.
Contribution
It introduces a novel decision-optimization framework with an efficient relaxation-and-rounding solution for joint service migration and resource management in MEC networks.
Findings
Close-to-optimal performance demonstrated in simulations.
Effective handling of user mobility and network interference.
Enhanced MEC throughput with minimized migration costs.
Abstract
Mobile-edge computing (MEC) enhances the capacities and features of mobile devices by offloading computation-intensive tasks over wireless networks to edge servers. One challenge faced by the deployment of MEC in cellular networks is to support user mobility. As a result, offloaded tasks can be seamlessly migrated between base stations (BSs) without compromising the resource-utilization efficiency and link reliability. In this paper, we tackle the challenge by optimizing the policy for migration/handover between BSs by jointly managing computation-and-radio resources. The objectives are twofold: maximizing the sum offloading rate, quantifying MEC throughput, and minimizing the migration cost. The policy design is formulated as a decision-optimization problem that accounts for virtualization, I/O interference between virtual machines (VMs), and wireless multi-access. To solve the complex…
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Taxonomy
TopicsIoT and Edge/Fog Computing · IoT Networks and Protocols · Age of Information Optimization
