This study has carefully analyzed and categorized the objectives, varieties, working principles, benefits, limitations, and
simulation tools of existing energy-efficient scheduling algorithms. Our systematic and comprehensive study will enhance the
fundamental understanding of energy-efficient techniques for new scholars who wish to delve deeper into the energy sector. This
evaluation highlights how crucial it is to distribute virtual machines (VMs) in an energy-efficient way to reduce data centers'
environmental impact and running costs. It provides useful information for researchers, practitioners, and policymakers who want to
develop and implement long-term strategies for optimizing resource utilization in cloud and edge computing environments. To sum
up. Load balancing systems aim to distribute workload evenly across physical servers to prevent resource underutilization and
overload situations and reduce energy wastage. Consolidation solutions integrate virtual machines (VMs) onto fewer physical
servers in an effort to optimize resource allocation. As a result, less power is used and the server is used more. Migration solutions
lower energy consumption while preserving performance levels through the use of dynamic virtual machine reallocation. By
considering energy consumption indicators, task characteristics, and resource availability, scheduling algorithms aim to allocate
resources as efficiently as feasible.
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