An Analysis-Efficient Cloud-Based Scheduling Infrastructure
Sirisha Potluri, Shaga Anoosha, Koti Tejasvi · 2024
The requirement to increase data center infrastructure performance is becoming more critical as cloud services grow. Data centers can maintain the velocity and effectiveness required to provide premium cloud services by leveraging high-performance computing, developing sophisticated networking solutions, and optimizing resources. A crucial aspect of this optimization strategy entails the execution of containerized applications. This approach leads to numerous benefits, including heightened portability, bolstered security, optimized resource utilization, swifter deployment, and scalability, as well as augmented interoperability and integration in smart agriculture–related infrastructure. These advantages collectively empower businesses in more effectively managing and deploy their applications, enabling them to respond swiftly and effectively to evolving business needs. Kubernetes, as a container orchestration system, automates the deployment, scaling, and administration of containerized applications. A notable feature is its ability to implement a scheduling algorithm for distributing the deployment and execution of containers across a cluster of nodes. This method chooses where in the cluster&s;s available nodes the containers should be placed. We give a thorough analysis of several scheduling techniques in the context of Kubernetes in this article. In our analysis, we categorize these scheduling algorithms into four sub-categories: Artificial intelligence (AI)-focused scheduling, generic scheduling, auto-scaling-enabled scheduling, and multi-objective optimization-based scheduling. Additionally, we pinpoint gaps and challenges that necessitate further investigation to improve cloud-based infrastructure needs in driving the shift of artificial intelligence in farming.