Dynamic Multilevel User Allocation in MEC Using CESO for Resource Efficiency and QoE

Mobile Edge Computing (MEC) has become one of the key paradigms to enable next-generation networks in supporting applications that are latency sensitive and computation-intensive. Nevertheless, the resourceful placement of heterogeneous and dynamically incoming user tasks with distributed edge servers is a problematic issue to be achieved because of network fluctuation, non-uniform resource availability, and variance in Quality of Experience (QoE) demand. To overcome these constraints, this study suggests the Dynamic Multilevel User Allocation Algorithm (DMUAA) that incorporates a new Cognitive Evolutionary Synergy Optimization (CESO) framework in order to reach stable, adaptive, and resource-optimizing allocation in real-time. DMUAA means a hierarchical optimization pipeline that consists of heuristic initialization, stochastic refinement, and strategic game-theoretic equilibrium assisted by a coordination and feedback mechanism that guarantees the constant adaptation to variations in user mobility and load. The system model collaboratively optimizes the latency, energy, resource, and QoE under the multi-constraint edge-server conditions. Extensive simulations over a wide range of resource capabilities, user rates, and mobility patterns indicate that DMUAA can be greatly superior to five state-of-the-art baselines, which are the MGGO, GTA, EUA, HAILP, and LGP. Findings indicate that DMUAA decreases average end-to-end latency by 18-34%, increases Resource Utilization Efficiency (RUE) by 12–27%, and increases Service Continuity Rate (SCR) by 15–30% over the current practices. The solved approach also produces 20-35% greater QoE, better load balancing (with up to 25% reduced LBI), and up to 22 per cent greater energy-QoE efficiency (EQR). Moreover, CESO allows for more rapid and stable convergence, and DMUAA comes to optimal allocation states 40-55% quicker than competing algorithms.

@article{Arun2026,
  title     = {Dynamic Multilevel User Allocation in MEC Using CESO for Resource Efficiency and QoE},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {3},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {V Arun and M Azhagiri},
  doi       = {10.14569/IJACSA.2026.0170366},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170366}
}