Minimax Optimization for Adaptive Cache Orchestration in Edge-Fog-Cloud Streaming Microservices
DOI:
https://doi.org/10.47839/ijc.25.2.4650Keywords:
cache placement, microservices, Edge–Fog–Cloud, minimax optimization, modified genetic algorithm, NSGA-II, QoS, dynamic orchestrationAbstract
Streaming microservice applications in multi-level Edge-Fog-Cloud infrastructures require low content delivery latency despite limited cache resources at peripheral nodes. This paper proposes a minimax optimization model for adaptive balancing of local and remote caching in streaming applications. Unlike existing request-oriented approaches, the method integrates streaming traffic dynamics, topological user affinity, and volatile edge cache consumption into a unified optimization criterion. The solution co-optimizes microservice and mobile cache placement to minimize the worst-case value of a composite objective function. A modified genetic algorithm (MGA) is introduced with tournament selection, two-point crossover, stochastic encoding, adaptive penalty handling, and a fitness stagnation stopping criterion. Experimental evaluation included Python/NumPy simulations with Zipf-distributed requests and a simulation software complex under realistic Edge and Fog constraints. The proposed MGA reduced average edge cache utilization from 61.77% to 34.84%, decreased peak cache load by 51.84%, and lowered mean delivery latency from 247.3 ms to 158.4 ms. The share of requests satisfying the 200 ms QoS threshold increased from 41.1% to 78.9%.
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