Secure and Cost-Efficient Microservice Placement in MEC Through Network-Aware Adaptation

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Abstract
Mobile Edge Computing (MEC) facilitates low-latency service delivery by bringing computation to the network edge, while microservice architectures enhance system flexibility and scalability through modular application decomposition. Their integration allows applications to dynamically scale and efficiently isolate faults in response to fluctuating demand. However, the heterogeneous and dynamic nature of edge environments poses significant challenges to the cost efficiency, security, and privacy of such deployments. Unlike prior work that handle security and privacy as constraints or overlook AN dynamics, we characterize both AN selection and service placement as mutually dependent decision layers within a unified control structure. Our proposal balances the computational overheads incurred by security and privacy verification against deployment costs, preventing aggressive cost-reduction efforts from compromising system protections. Considering the unpredictability of run-time and network conditions, we propose an online algorithm for progressive decision-making. The deployment problem is decomposed into one-slot optimization sub-problems, each NP-hard, for which we develop a greedy-based heuristic algorithm. Experiments across diverse loads, request patterns, and security/privacy thresholds show that the proposed algorithm consistently achieves the lowest average deployment cost, with cost reductions of at least 41.67% compared with the baselines, while maintaining comparable or higher request acceptance ratios and generally lower, well-balanced edge-server workloads.

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