Cloud computing is a new computation technology that provides services to consumers and businesses. The main idea of Cloud computing is to present software and hardware services through the Internet to the users and organizations at all levels. In Cloud computing, the users pay for the services, which means a usage-based payment system is used in this technology. Using virtualization technology in computation resources enables the appropriate utilization of resources in cloud computing. One of the most significant challenging issues in virtualization technology is the placement of optimal virtual machines on physical machines in cloud data centers. The placement of virtual machines comprises a process wherein virtual machines are mapped onto physical machines in cloud data centers. Optimal deployment leads to the reduction in power consumption, optimal use of resources, traffic reduction in data centers, costs reduction, and efficiency enhancement of the data center in the cloud. The present article proposed a new approach using a combination of the Sine–Cosine Algorithm and Salp Swarm Algorithm as discrete multi-objective and chaotic functions for optimal virtual machine placement. The first goal of the proposed algorithm was to reduce the power consumption in cloud data centers by condensing the number of active physical machines. The second goal was to reduce the waste of resources and manage it by optimally virtual machine placement on physical machines in cloud data centers. The third objective was to minimize and reduce Service Level Agreement among the active physical machines in cloud data centers. The proposed method prevent the increase in the migration of virtual machines onto physical machines. Ultimately, the results obtained from the proposed algorithm were compared with those of previous akin algorithms in the literature, including First Fit, Virtual Machine Placement Ant Colony System, and Modified Best Fit Decreasing. The proposed scheme is tested using Amazon EC2 Instances and the result indicated that the proposed algorithm performs better than the existing algorithms for various performance metrics.

云计算是一种新的计算技术，可为消费者和企业提供服务。云计算的主要思想是通过Internet向各个级别的用户和组织提供软件和硬件服务。在云计算中，用户为服务付费，这意味着该技术中使用了基于使用量的支付系统。在计算资源中使用虚拟化技术可以适当地利用云计算中的资源。虚拟化技术中最重大的挑战性问题之一是将最佳虚拟机放置在云数据中心的物理机上。虚拟机的放置包括将虚拟机映射到云数据中心中的物理机上的过程。最佳部署可降低功耗，资源的最佳利用，数据中心流量的减少，成本的降低以及云中数据中心效率的提高。本文提出了一种新方法，将正弦余弦算法和Salp Swarm算法结合使用，作为离散的多目标和混沌函数，以实现最佳虚拟机放置。提出的算法的第一个目标是通过压缩活动物理机的数量来减少云数据中心的功耗。第二个目标是通过在云数据中心的物理机上最佳地放置虚拟机来减少资源浪费并进行管理。第三个目标是最大程度地减少和减少云数据中心中活动物理机之间的服务水平协议。所提出的方法防止了虚拟机向物理机的迁移的增加。最终，将从提出的算法获得的结果与文献中先前的类似算法（包括“首次拟合”，虚拟机放置蚁群系统和“改进的最佳拟合递减”）进行比较。使用Amazon EC2实例对提出的方案进行了测试，结果表明，对于各种性能指标，提出的算法的性能要优于现有算法。