Abstract:
Cloud computing energy efficiency is being researched because data centers use a lot of energy. However, reducing energy consumption without increasing Service Level Agreement violations is difficult.
Most virtual machine (VM) consolidation approaches constrain system performance and Quality of Service (QoS) metrics, resulting in high scheduling overhead and the inability to improve energy efficiency without sacrificing system performance and cloud service quality.
First, we define peak power efficiency and optimal utilization for heterogeneous physical machines (PMs). Peak Efficiency Aware Scheduling (PEAS), a novel VM placement and reallocation strategy for server clusters, improves performance and energy conservation.
On-line VM allocation and consolidation by PEAS keeps PMs at peak power efficiency. PEAS outperforms several energy-aware consolidation algorithms in energy consumption, system performance, and QoS metrics in Cloudsim experiments.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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