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Abstract

Cloud environments often exhibit varying levels of heterogeneity arising from the diverse characteristics of cloudlets and virtual machines. This research paper focuses on addressing this heterogeneity and proposes two scheduling algorithms: the Variance Managed Heuristic Scheduler (VMHS) and theAdaptive 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 serves as the main scheduler, while VMHS and MaxMin function as sub-schedulers in this proposed work. This multi-objective AHICS scheduling algorithm harnesses the strengths of both schedulers. AHICS adaptively selects either VMHS or MaxMin based on the heterogeneity level of the cloudletsand virtual machines, employing VMHS in low heterogeneity scenarios and MaxMin in high heterogeneity scenarios. Implemented using the CloudSim 3.0.3 simulator, the AHICS scheduler outperforms other heuristic scheduling algorithms, including MinMin, TASA, HAMM, PTFR, and RSSM. Experimental results demonstrate improvements of 3-5% in makespan, 4-6% in virtual machine utilization, 25–84% in load imbalance, and a reduction of 25–91% in completion time deviation for both low and high heterogeneity scenarios. These performance gains can translate into substantial cost savings, increased efficiency, and an improved user experience

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