Service Placement in Fog Computing: A Systematic Review of Issues, Techniques and Multi-Objective Optimization Approaches

Service placement in fog computing has emerged as a fundamental research problem for enabling latency-sensitive and resource-constrained Internet of Things (IoT) applications. Although numerous optimization strategies have been proposed, existing studies remain fragmented, often addressing isolated objectives without systematically consolidating methodological trends, evaluation practices, and research gaps within a unified multi-objective perspective. This study presents a comprehensive Systematic Literature Review (SLR) that critically analyzes recent peer-reviewed research on service placement in fog computing, with a specific focus on multi-objective optimization approaches. Unlike prior surveys that emphasize architectural overviews or single-metric optimization, this review provides (i) a structured taxonomy of exact, heuristic, metaheuristic, game-theoretic, AI-driven, and hybrid approaches; (ii) a comparative analysis of optimization objectives including latency, energy consumption, cost, and resource utilization; and (iii) a synthesis of datasets, simulation tools, and validation frameworks used in empirical evaluation. The findings reveal a clear methodological shift from deterministic mathematical models toward adaptive and intelligence-driven frameworks integrating deep reinforcement learning, federated learning, and evolutionary optimization. Despite this progress, significant research gaps remain, particularly in integrating mobility-awareness, security and trust mechanisms, real-world validation, and lightweight AI models suitable for resource-constrained fog environments. By consolidating fragmented research streams into a unified analytical framework, this review provides a methodological foundation and research roadmap for developing intelligent, scalable, and empirically validated multi-objective service placement strategies in next-generation fog-based IoT systems.