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• W4313353721 abstract "Platforms for cloud computing make it possible for end users to get access to computing resources online. The emergence of cloud computing has led to an increase in the use of Apache Hadoop in cloud data centres. MapReduce and the open-source framework Hadoop have generated great interest among both researchers and corporates. Optimising Hadoop’s energy consumption has been identified as a critical issue, with numerous studies undertaken on the topic. FIFO, capacity, and fair scheduling algorithms are the most commonly used scheduling algorithms in Hadoop cloud data centres. This paper examines the nature and non-nature-oriented energy-efficient scheduling techniques that are frequently used in cloud data centres. The studies that have been explored are classified into node management, virtualisation and consolidation, task scheduling, data placement, and infrastructure change categories. A brief discussion of the rationale for each category is included, along with its strengths and weaknesses. Additionally, for computation and I/O-intensive workloads, the paper examines the CPU energy consumption of three commonly used cloud workload scheduling algorithms: FIFO, fair, and capacity. Our goal is to see if the energy usage among the three schedulers differs significantly between two types of workloads and different job sizes. Finally, the cloud computing research community has been provided with a futuristic perspective in the area, based on current trends in energy-efficient algorithms. The findings and directions presented in this study may be helpful to researchers interested in optimising cloud data centre energy consumption." @default.
• W4313353721 created "2023-01-06" @default.
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• W4313353721 date "2023-01-01" @default.
• W4313353721 modified "2023-09-23" @default.
• W4313353721 title "Energy Saving Techniques for Cloud Data Centres: An Empirical Research Analysis" @default.
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• W4313353721 doi "https://doi.org/10.1007/978-981-19-5868-7_57" @default.
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