Kaur Amandeep, Kumar Saurabh, Gupta Deepali, Hamid Yasir, Hamdi Monia, Ksibi Amel, Elmannai Hela, Saini Shilpa
Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140401, India.
Information Security and Engineering Technology, Abu Dhabi Polytechnic, Abu Dhabi 111499, United Arab Emirates.
Sensors (Basel). 2023 Jul 3;23(13):6117. doi: 10.3390/s23136117.
Cloud computing plays an important role in every IT sector. Many tech giants such as Google, Microsoft, and Facebook as deploying their data centres around the world to provide computation and storage services. The customers either submit their job directly or they take the help of the brokers for the submission of the jobs to the cloud centres. The preliminary aim is to reduce the overall power consumption which was ignored in the early days of cloud development. This was due to the performance expectations from cloud servers as they were supposed to provide all the services through their services layers IaaS, PaaS, and SaaS. As time passed and researchers came up with new terminologies and algorithmic architecture for the reduction of power consumption and sustainability, other algorithmic anarchies were also introduced, such as statistical oriented learning and bioinspired algorithms. In this paper, an indepth focus has been done on multiple approaches for migration among virtual machines and find out various issues among existing approaches. The proposed work utilizes elastic scheduling inspired by the smart elastic scheduling algorithm (SESA) to develop a more energy-efficient VM allocation and migration algorithm. The proposed work uses cosine similarity and bandwidth utilization as additional utilities to improve the current performance in terms of QoS. The proposed work is evaluated for overall power consumption and service level agreement violation (SLA-V) and is compared with related state of art techniques. A proposed algorithm is also presented in order to solve problems found during the survey.
云计算在每个信息技术领域都发挥着重要作用。许多科技巨头，如谷歌、微软和脸书，都在全球各地部署数据中心，以提供计算和存储服务。客户要么直接提交他们的任务，要么借助经纪人将任务提交到云中心。最初的目标是降低整体功耗，这在云计算发展的早期被忽视了。这是由于对云服务器的性能期望，因为它们应该通过其服务层基础设施即服务（IaaS）、平台即服务（PaaS）和软件即服务（SaaS）提供所有服务。随着时间的推移，研究人员提出了新的术语和算法架构来降低功耗和实现可持续性，还引入了其他算法体系，如统计导向学习和受生物启发的算法。在本文中，深入关注了虚拟机之间迁移的多种方法，并找出了现有方法中的各种问题。所提出的工作利用受智能弹性调度算法（SESA）启发的弹性调度来开发一种更节能的虚拟机分配和迁移算法。所提出的工作使用余弦相似度和带宽利用率作为额外的效用指标，以在服务质量（QoS）方面提高当前性能。对所提出的工作进行了整体功耗和违反服务水平协议（SLA-V）方面的评估，并与相关的现有技术进行了比较。还提出了一种算法，以解决在调查过程中发现的问题。
