Parallel random matrix particle swarm optimization scheduling algorithms with budget constraints on cloud computing systems

Nowadays, increasing number of Internet of Things and mobile Internet application services are migrated to cloud computing systems. One of the most important cloud challenges for this business is to optimize services cost. The efficient way to deal with this challenge is to improve the performance of resource management and task scheduling in cloud systems. However, this kind of service scheduling is a typical combinatorial optimization problem, and it is difficult to obtain the optimal solution. In this study, we first formalize this cloud services on virtual machines (VMs) with budget constraints scheduling problem. Then, we propose a random matrix particle swarm optimization scheduling algorithm (RMPSO), which uses the random integer matrix to represent its position and a feasible task scheduling scheme, to achieve the optimal total cost of cloud services. However, the drawback of this solution for large-scale systems is its high time complexity. Therefore, we propose two parallel RMPSO algorithms: CPU parallel algorithm (M-RMPSO) on multi-core system with shared memory and manycore GPU-accelerated strategy (G-RMPSO) to reduce its time complexity. Finally, the rigorous performance evaluation results clearly show that our proposed G-RMPSO outperforms M-RMPSO and existing FMPSO, HYBRID (MPSO+MCSO) in terms of cloud services total cost and algorithm execution time. Therefore, our proposed GPU-accelerated G-RMPSO algorithm is very suitable for cloud service scheduling. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

The study introduces a random matrix particle swarm optimization scheduling algorithm for cloud service scheduling, as well as two parallel algorithms to reduce its time complexity. Experimental results demonstrate that the GPU-accelerated algorithm performs better compared to the others.