Delay-based Categorisation and Adaptive MES-Activation (DCAMA) through the Inclusion of Remote Cloud Server in Multi-Access Edge Computing Networks

Multi-access edge computing (MEC) has emerged as a promising paradigm to reduce communication overhead and computational burden on mobile users (MUs) by offloading resource-intensive applications to nearby multi-access edge servers (MESs). This strategy significantly lowers energy consumption and execution latency. However, most existing studies on MEC assume an ideal scenario in which MESs are already optimally deployed, typically co-located with every base station (BS) in fifth-generation (5G) networks. While deploying an MES at each BS ensures low-latency services and high quality of service (QoS), it introduces several practical limitations. These include high capital and operational expenditures for service providers, underutilised MESs in sparsely populated regions, and elevated service costs for end users due to continuous MES activation regardless of demand. To address these challenges, this work proposes a novel Delay-based Categorisation and Adaptive MES-Activation (DCAMA) approach aimed at optimising MES activation in beyond-5G (B5G) wireless networks. Rather than assuming static and full MES deployment, the DCAMA strategy dynamically determines the minimum number of MESs required based on current network workload and task urgency. This energy-aware and cost-efficient method enables scalable MES activation without compromising service performance .