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Future Trends and Significant Solutions for Intelligent Computing Resource Management
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Author(s): Diya Biswas (Brainware University, India), Anuska Dutta (Brainware University, India), Shivnath Ghosh (Brainware University, India)and Piyal Roy (Brainware University, India)
Copyright: 2024
Pages: 22
Source title:
Computational Intelligence for Green Cloud Computing and Digital Waste Management
Source Author(s)/Editor(s): K. Dinesh Kumar (Amrita Vishwa Vidyapeetham, India), Vijayakumar Varadarajan (The University of New South Wales, Australia), Nidal Nasser (College of Engineering, Alfaisal University, Saudi Arabia)and Ravi Kumar Poluru (Institute of Aeronautical Engineering, India)
DOI: 10.4018/979-8-3693-1552-1.ch010
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Abstract
Cloud providers place a high value on reducing energy consumption in cloud computing since it reduces operating costs and improves service sustainability. Cloud services are frequently replicated across providers to ensure high availability and dependability, which increases provider resource utilization and overhead. Finding the right balance between service replication and consolidation to lower energy usage and boost service uptime can be challenging for cloud resource management decision-makers. This chapter addresses this problem by presenting a ground-breaking technique known as “CRUZE,” which is based on cuckoo optimization and considers energy efficiency, dependability, and comprehensive resource management in cloud computing, encompassing cooling systems, servers, networks, and storage. Using cloud resources, effectively illuminating and executing a range of jobs, CRUZE has significantly reduced energy usage by 20.1% while improving dependability and CPU utilization by effectively illustrating and executing a variety of workloads on cloud resources that have been allocated.
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