Resource-and-Latency-Aware PSO — A Novel Algorithm for Intelligent and Contextual Cloud Native Resource Scheduling

Swarm Intelligence (SI) has emerged as a prominent approach in resource scheduling, offering adaptability and effectiveness in multi-objective optimisation. However, SI-based methods often introduce complexity and challenges such as premature convergence and excessive exploration, leading suboptimal performance. To address these challenges, this paper presents a novel Resource-and-Latency-Aware Particle Swarm Optimisation (RALA-PSO) algorithm for intelligent and contextual cloud native resource scheduling. RALA-PSO expands upon Particle Swarm Optimisation (PSO) by incorporating a greedy initialisation for faster convergence, a circular search space to avoid boundary stagnation, a multi-criteria fitness function tailored to Cloud-Native characteristics, a heuristic-based scoring model to reward suitable solutions, and the integration of adaptive weights and learning factors for exploration-exploitation balance. The RALA-PSO implementation is available as open-source in [14]. RALA-PSO prioritise solutions based on efficient resource allocation and less communication overhead via service co-location. Experimental evaluations using the Alibaba Cluster TraceV2018 dataset show that RALA-PSO reduces network transmission overhead cost by 1.8x to 2.36x compared to state-of-the-art algorithms MOPPSO-CMS and ACO-CMS, while maintaining competitive resource efficiency. Notably, RALA-PSO achieves runtimes up to 212 times faster than ACO-CMS and 1.5 times faster than MOPPSO-CMS. These results demonstrate that RALA-PSO is a scalable, efficient, and context-aware scheduling solution for cloud-native applications, particularly effective in environments with strict network and resource constraints.