Source-Level Energy Consumption Estimation for Cloud Computing Tasks Article Swipe

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Hui Liu , Fusheng Yan , Shao-Kui Zhang , Tao Xiao , Jie Song ·

YOU? · · 2017 · Open Access · · DOI: https://doi.org/10.1109/access.2017.2778309

In the cloud computing environment, the source-level energy consumption (EC) estimation is employed to approximately measure the EC of a cloud computing task before it is executed. The EC estimation on tasks is critical to task scheduling and source-code improvement in the aspect of EC optimization. The existing studies treat a task as a program, and EC of the task as the simple summation of each statement's EC. However, EC of two tasks consisting of the same statements with different structures is unequal; therefore, the code structure should be highlighted in source-level EC estimation. In this paper, an abstract energy consumption (AEC) model, which is static and runtime-independent, is proposed. For the model, the two quantitative measurements, “cross-degree”and “reuse-degree,”are proposed as the code structure features, and the relationship between EC and the measurements is formulated. Although AEC is not a precise EC measurement, it can properly represent the EC of a task, compare with other tasks, and verify the optimization effect. Experimental results show that the ratios between the EC and AEC with 50 test cases are stable; the standard deviation is 0.0002; and the mean value is 0.005. The regularities of EC and code structures, represented as “cross-degree”and “reuse-degree,”are also validated. Though AEC, it is easier to schedule the cloud computing tasks properly and further reduce the consumed energy.

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Computer science Cloud computing Reuse Source code Energy consumption Task (project management) Scheduling (production processes) Schedule Code (set theory) Operating system Mathematical optimization Programming language Mathematics Biology Ecology Economics Set (abstract data type) Management

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Type: article
Language: en
Landing Page: https://doi.org/10.1109/access.2017.2778309
OA Status: gold
Cited By: 12
References: 27
Related Works: 10
OpenAlex ID: https://openalex.org/W2769515370

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DOI: https://doi.org/10.1109/access.2017.2778309

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Title: Source-Level Energy Consumption Estimation for Cloud Computing Tasks

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Language: en

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Publication year: 2017

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Publication date: 2017-11-29

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Authors: Hui Liu, Fusheng Yan, Shao-Kui Zhang, Tao Xiao, Jie Song

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Landing page: https://doi.org/10.1109/access.2017.2778309

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Concepts: Computer science, Cloud computing, Reuse, Source code, Energy consumption, Task (project management), Scheduling (production processes), Schedule, Code (set theory), Operating system, Mathematical optimization, Programming language, Mathematics, Biology, Ecology, Economics, Set (abstract data type), Management

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Cited by: 12

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