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 opensource. 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 1.8× to 2.36× 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.