An Improving Energy Cost Efficient for Multiple Cloud Data Center Using Green Computing
Keywords:
cloud data centers, energy cost, renewable energy, resource allocation, workload shifting. Green computing.Abstract
The increasing reliance on cloud computing has escalated energy consumption and environmental concerns, necessitating innovative solutions for energy efficiency in data centers. This paper presents a novel framework, CFWS (Cloud Framework for Workload Scheduling), designed to optimize energy costs while promoting the use of renewable energy sources (RES) across multiple cloud data centers. By integrating Green computing GC) CFWS employs an adaptive threshold adjustment method, TCN-MAD, which evaluates the likelihood of physical machine (PM) overload. This proactive approach minimizes unnecessary virtual machine (VM) migrations and reduces the risk of service level agreement (SLA) violations stemming from workload imbalances. Additionally, CFWS innovatively represents VM migrations among geo-distributed data centers as flattened indices within its GC action space, significantly enhancing execution efficiency. Simulation results indicate that CFWS outperforms existing algorithms, achieving a 5.67% to 13.22% reduction in brown energy consumption while maximizing RES utilization. Furthermore, the framework reduces VM migrations by up to 86.53% and maintains the lowest SLA violations, demonstrating its effectiveness in optimizing energy efficiency in cloud computing environments. This research contributes valuable insights into green computing practices, promoting sustainable energy management in the cloud industry.
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