Artificial intelligence enhanced edge server placement for workload balancing and energy efficiency in B5G networks

The Internet of Things (IoT) and allied applications have made real-time responsiveness for massive devices over the Internet essential. Cloud-edge/fog ensembles handle such applications' computations. For Beyond 5th Generation (B5G) communication paradigms, Edge Servers (ESs) must be placed within Information Communication Technology infrastructures to meet Quality of Service requirements like response time and resource utilisation. Due to the large number of Base Stations (BSs) and ESs and the possibility of significant variations in placing the ESs within the IoTs geographical expanse for optimising multiple objectives, the Edge Server Placement Problem (ESPP) is NP-hard. Thus, stochastic evolutionary metaheuristics are natural. This work addresses the ESPP using a Particle Swarm Optimization that initialises particles as BS positions within the geography to maintain the workload while scanning through all feasible sets of BSs as an encoded sequence. The Workload-Threshold Aware Sequence Encoding (WTASE) Scheme for ESPP provides the number of ESs to be deployed, similar to existing methodologies and exact locations for their placements without the overhead of maintaining a prohibitively large distance matrix. Simulation tests using open-source datasets show that the suggested technique improves ESs utilisation rate, workload balance, and average energy consumption by 36%, 17%, and 32%, respectively, compared to prior works.