A Systematic Review of Optimization Techniques for Computation Offloading in Mobile Edge Computing: Lyapunov, Convex, Heuristic, Game-Theoretic, and Machine Learning Approaches - Rasool, Mohammad Ashique E; Kumar, Anoop; Islam, Asharul (Informatica, 2025)
Mobile Edge Computing (MEC) has become a key paradigm for reducing latency, energy consumption, and system overhead in computation-intensive applications by enabling task offloading to edge servers. In this paper, we conduct a Systematic Literature Review (SLR) of 70 peer-reviewed studies, with 30 papers coded in detail across five main optimization approaches: Lyapunov, convex, heuristic, game-theoretic, and machine learning methods. The review systematically compares these paradigms in terms of optimization objectives, task assumptions, and evaluation setups. Our analysis reveals that while Lyapunov and game-theoretic approaches rely entirely on simulation (100%), heuristic studies exhibit stronger practical grounding, with 33% including real-world validation. Convex methods demonstrated 10–18% energy savings in heterogeneous MEC scenarios, whereas machine learning approaches showed adaptability under uncertain conditions but lacked real-world testing. Beyond numerical comparison, the review highlights the methodological evolution of MEC optimization, from formal mathematical programming to learning-driven and hybrid approaches. The coding of 30 representative papers provides a quantitative foundation that exposes common assumptions—such as independent task models and simplified simulators—that limit real-world applicability. At the same time, the synthesis identifies promising trends, including reinforcement learning for adaptive decision-making and hybrid frameworks that combine convex optimization with predictive models. These insights underscore the need for future MEC strategies that balance computational efficiency, adaptability, and scalability. By synthesizing results, quantifying trends, and framing open challenges, this SLR provides researchers and practitioners with a comprehensive understanding of the strengths, limitations, and opportunities in MEC computation offloading.