Region-aware dynamic job scheduling and resource efficiency for load balancing based on adaptive chaotic sparrow search optimization and coalitional game in cloud computing environments

Abstract

Uneven distribution of workloads represents a significant challenge in cloud data centers, negatively impacting the efficient utilization of resources. To address this problem and optimize the performance-to-profit (PP) ratio, we introduced the Regional Awareness dynamic Scheduling Algorithm (RASA) through a three-phase approach. In the initial phase, tasks are categorized based on critical parameters like CPU usage, memory utilization, and I/O operations, using a task classification model. The second phase involves estimating the CPU, memory, and I/O capacities of each node. Subsequently, a unique merge-and-split-based coalitional game-theoretic process is employed to group nodes into clusters. In the final phase, we have improved task placement by introducing an enhanced Sparrow Search Algorithm (ISSA) to overcome the slow convergence and local optimization issues found in the traditional SSA. We have incorporated the Tent chaotic mapping approach to enhance both global and local search efficiency during this stage. Additionally, we have fine-tuned fair load distribution using the Cauchy mutation process. We performed simulations using the CloudSim toolkit, and the results underscore the RASA algorithm’s superiority in terms of how quickly it converges its precision in task placement and its ability to distribute workloads fairly. Our proposed algorithm achieves a 9% reduction in latency overhead, a 14% decrease in processing time, a 15% reduction in workload imbalance, a 19% decrease in energy consumption, and a 26% decrease in idle periods. Furthermore, it enhances resource availability and efficiency by 22% and 27% respectively, while simultaneously boosting service throughput by 32%.