Cloud computing is a concept introduced in the information technology era, with the main components being the grid, distributed, and valuable computing. The cloud is being developed continuously and, naturally, comes up with many challenges, one of which is scheduling. A schedule or timeline is a mechanism used to optimize the time for performing a duty or set of duties. A scheduling process is accountable for choosing the best resources for performing a duty. The main goal of a scheduling algorithm is to improve the efficiency and quality of the service while at the same time ensuring the acceptability and effectiveness of the targets. The task scheduling problem is one of the most important NP-hard issues in the cloud domain and, so far, many techniques have been proposed as solutions, including using genetic algorithms (GAs), particle swarm optimization, (PSO), and ant colony optimization (ACO). To address this problem, in this paper, one of the collective intelligence algorithms, called the Salp Swarm Algorithm (SSA), has been expanded, improved, and applied. The performance of the proposed algorithm has been compared with that of GAs, PSO, continuous ACO, and the basic SSA. The results show that our algorithm has generally higher performance than the other algorithms. For example, compared to the basic SSA, the proposed method has an average reduction of approximately 21% in makespan.

翻译：云计算是信息技术时代引入的概念，其主要组成部分包括网格计算、分布式计算和效用计算。云计算持续发展，自然面临着众多挑战，其中之一便是调度问题。调度或时间线是一种用于优化执行单个或多个任务时间的机制。调度过程负责选择执行任务的最佳资源。调度算法的主要目标是在确保目标的可接受性和有效性的同时，提升服务效率与质量。任务调度问题是云领域中最重要的NP难问题之一，迄今为止已提出许多解决技术，包括使用遗传算法（GA）、粒子群优化（PSO）和蚁群优化（ACO）。为解决该问题，本文对一种名为樽海鞘群算法（SSA）的集体智能算法进行了扩展、改进并应用。所提算法的性能与GA、PSO、连续ACO以及基础SSA进行了对比。结果表明，我们的算法整体性能优于其他算法。例如，与基础SSA相比，所提方法在完工时间上平均减少约21%。