## 2 Literature Review  
In traditional Mobile Edge Computing systems, research on task offloading and resource allocation has primarily focused on various optimization objectives, including minimizing latency [13; 14; 15; 16], reducing energy consumption [7; 8; 9; 10; 11; 12], lowering costs [17; 18; 19; 20], and maximizing utility [21]. Among these, minimizing latency and energy consumption has been a central focus. Several studies have explored latency optimization in MEC systems, yet inherent limitations persist. [PERSON] et al. [13] proposed a Joint Partial Offloading and Resource Allocation (JPORA) scheme for the problem of minimizing total task processing latency in Device-to-Device (D2D)-enabled MEC systems, but its reliance on centralized optimization may hinder scalability in large-scale networks. [PERSON] et al. [14] addressed the issue of minimizing expected execution latency for fine-grained tasks in low-power MEC systems and introduced a lightweight multi-user offloading strategy with a distributed consensus mechanism; however, their approach assumes ideal wireless conditions, neglecting dynamic interference fluctuations. [PERSON] et al. [15] explored the minimization of system latency in multi-user, multi-server MEC and decomposed the latency minimization problem into task offloading and energy allocation subproblems, solving them via matching theory and heuristics, yet their single-server-per-user constraint restricts flexibility in multi-server environments. [PERSON] et al. [16] studied latency minimization in Non-Orthogonal Multiple Access (NOMA)-based MEC systems and developed a heuristic algorithm for NOMA-based MEC, combining binary search and swap matching, but the computational overhead increases exponentially with user density. [PERSON] et al. [22] proposed a distributed application placement framework leveraging an actor-critic reinforcement learning paradigm,specifically employing the IMPortance Weighted Actor Learner Architecture (IMPALA) to optimize resource allocation in edge computing environments. In a complementary study, [PERSON] et al. [PERSON] et al. (2018) proposed two novel semi-greedy heuristic algorithms--Priority-Aware Semi-Greedy (PSG) and its enhanced variant incorporating a multi-start procedure--to address the challenge of deadline-constrained IoT task scheduling in fog networks, demonstrating significant improvements in mapping efficiency. Energy-efficient MEC strategies also exhibit critical tradeoffs. [PERSON] et al. [PERSON] et al. (2017) addressed the problem of minimizing total system energy consumption in asynchronous task MEC systems, proposing a DDQNL-IST-based polling feedback energy-saving offloading strategy, yet their approach lacks adaptability to bursty workloads. [PERSON] et al. [PERSON] et al. (2018) tackled the minimization of total energy consumption for all users in multi-user, multi-server MEC systems, employing game theory and Lagrangian functions to solve the offloading strategy and resource allocation subproblems, but their Nash equilibrium-based solution may not guarantee global optimality. [PERSON] et al. [PERSON] et al. (2018) investigated the minimization of total energy consumption for all mobile users (MUs) in multi-access MEC systems, introducing a two-layer iterative algorithm to jointly optimize task offloading rates, computational frequencies, and transmission precoding matrices for each MU. [PERSON] et al. [PERSON] et al. (2018) studied energy consumption minimization in Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS)-assisted MEC systems, proposing an alternating algorithm to optimize STAR-RIS transmission and reflection coefficients, but their algorithm assumes static RIS configurations, limiting responsiveness to mobility. [PERSON] et al. [PERSON] et al.