Adaptive Heterogeneity Index Cloudlet Scheduler for Variable Workload and Virtual Machine Configuration

Abstract

In any service-based computing environment, performance pertains to the effectiveness of a system or application in managing user tasks. The key performance assessment metrics include makespan, responsiveness, speed, throughput, resource utilization, etc. In any distributed landscape, like cloud computing, optimal performance relies on resource management techniques such as scheduling, load balancing, etc. Cloud environments often exhibit varying levels of heterogeneity arising from the diverse characteristics of cloudlets and virtual machines. This research paper focuses on the impact of this heterogeneity and proposes two scheduling algorithms to address it effectively: the Variance Managed Heuristic Scheduler (VMHS) and the Adaptive Heterogeneity Index Cloudlet Scheduler (AHICS). AHICS aims to minimize makespan, virtual machine underutilization, the degree of load imbalance, and the deviation of completion time among virtual machines. AHICS functions as the main scheduler, whereas VMHS and MaxMin act as sub-schedulers in this proposed work. AHICS is designed to be flexible and adjust its scheduling strategy based on the level of heterogeneity within the cloud environment. AHICS utilizes the VMHS scheduler in scenarios with a low heterogeneity index or the MaxMin scheduler when the cloudlets and the virtual machines characteristics are highly diverse or heterogeneous. This multi-objective AHICS scheduling algorithm harnesses the strengths of both schedulers as a hybrid algorithm. Implemented using the CloudSim 3.0.3 simulator, experimental results demonstrate that AHICS outperforms other heuristic scheduling algorithms, including MinMin, TASA, HAMM, PTFR, and RSSM, in terms of makespan, virtual machine utilization ratio, the degree of load imbalance, and the deviation of completion time among the virtual machines in both low and high heterogeneity levels