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Online Learning for Edge Node Program Placement in Mobile Edge Computing Networks
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DOI：10.1109/TON.2026.3658535.png)
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Mobile edge computing (MEC) is a key technology to support computationally intensive mobile applications with stringent latency requirements. With MEC servers deployed at network edge (e.g., base stations), the computational tasks generated by various applications can be offloaded to nearby edge nodes (ENs) and timely processed there. Meanwhile, future mobile applications will be more diverse and complex, which will need to be supported by a large number of complicated programs. As the storage space of ENs is limited, it is infeasible for each EN to store the program codes of all applications. Thus, it is necessary to optimize program placement at ENs to fully harvest the potential of MEC. In this paper, we investigate the problem of program placement and user association in storage-limited MEC networks. Such a problem is formulated as a sequential decision-making problem. We first consider the single EN scenario and propose an online learning-based solution. We then propose a solution framework for the multi-EN scenario, where we decompose the original problem into three subproblems and iteratively solve them with low-complexity approaches. Simulation results show that the average latency achieved by our proposed schemes is 30% to 70% lower than two benchmark schemes and is on average less than 10% higher than a lower bound.
Keyword:
Mobile edge computing
low-latency applications
storage-limited systems
program placement optimization
multi-armed bandit
Thompson sampling
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