Enhancing Cloud Services: Streamlining Performance Metrics and Cloudlet Scheduling

Enhancing cloud service is the primary goal of cloud stakeholders. It is a comprehensive goal that can be achieved through optimizing performance metrics, effective resource utilization, and prioritizing user satisfaction. These performance metrics, also called Quality of Service (QoS) metrics, are mentioned in the Service Level Agreement (SLA), a contractual document. Optimizing metrics, resource utilization, and user experience requires continuous monitoring systems and cloud technologies such as virtualization, scheduling, migration, consolidation, load balancing, etc. Scheduling cloudlets, virtual machines, and load balancing are crucial for achieving the SLA enumerated QoS and other key demands. In order to monitor the performance and evaluate the effectiveness of any cloud service knowledge on key performance metrics, scheduling and load balancing become imperative. This paper is orchestrated to identify essential performance metrics and explore how scheduling and load balancing algorithms enhance cloud performance. Additionally, it seeks to conduct a comparative evaluation of FCFS, SJF, Min-Min, Max-Min, RASA, Suffrage, and TASA cloudlet scheduling algorithms using CloudSim. This evaluation focuses on key scheduling metrics, including average waiting time, makespan, virtual machine utilization ratio, and load balancing.