Enhancing the resilience of error-prone computing environments using a hybrid multi-objective optimization algorithm for edge-centric cloud computing systems.

The integration of advanced technologies like cloud and edge computing has facilitated efficient resource coordination, resulting in improved overall management and widespread applicability. Simultaneously, addressing energy consumption, reliability, and server and communication link failure rates has become a pressing research concern. To address this challenge, an advanced combined-decomposition whale optimization algorithm has been developed. This algorithm utilizes a unique combined-decomposition operator to identify nearly optimal server coalitions, enhancing the quality of service performance. Through a training process at various utilization levels, servers determine their optimal utilization for achieving the maximum energy-to-reliability trade-off ratio. Then, a head server with the highest optimal utilization leads each of these clusters of servers. Unlike other population-based clustering methods, this algorithm incorporates the whale optimization algorithm, extending its exploration and exploitation capabilities beyond other leading scheduling algorithms. The integration of these techniques successfully achieves the dual objective of balancing energy and reliability, addressing existing challenges, and ensuring optimal energy-reliability trade-offs. Simulation experiments using various evaluation metrics demonstrate that the proposed approach enhances energy efficiency by approximately 17 to 35% and reliability by 25 to 55%, all while meeting quality service standards. [ABSTRACT FROM AUTHOR]