Sufficiency power consideration to run a workload on renewable energy operated datacenter

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-06-01 Epub Date: 2025-01-30 DOI:10.1016/j.future.2025.107710
Damien Landré , Laurent Philippe , Jean-Marc Pierson
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Abstract

Datacenters are an essential part of the internet, but their continuous development requires finding sustainable solutions to limit their impact on climate change. The Datazero2 project aims to design datacenters running solely on local renewable energy. In this paper, we tackle the problem of computing the minimum power demand to process a workload under quality of service constraint in a datacenter operated with renewable energy. To solve this problem, we propose a binary search algorithm that requires the computation of machine configurations with maximum computing power. When machines are heterogeneous, we face the problem of choosing the machines and their DVFS (Dynamic Voltage and Frequency Scaling) state. A MILP (Mixed-Integer Linear Programming), to find the optimal solution, and four heuristics that give satisfactory results in a reasonable time are proposed. simulations show that the best heuristics reach an average deviation from the optimal solution of 0.03% to 0.65%. The binary search algorithm is challenged against a real workload to assess the impact of flexibility on the quality of service.
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考虑在可再生能源操作的数据中心上运行工作负载的足够功率
数据中心是互联网的重要组成部分,但其持续发展需要找到可持续的解决方案,以限制其对气候变化的影响。Datazero2项目旨在设计完全使用当地可再生能源的数据中心。在本文中,我们解决了在可再生能源运行的数据中心中,在服务质量约束下处理工作负载的最小电力需求的计算问题。为了解决这个问题,我们提出了一种需要计算最大计算能力的机器配置的二分搜索算法。当机器是异构的时候,我们面临着选择机器和它们的DVFS(动态电压和频率缩放)状态的问题。提出了一种寻找最优解的混合整数线性规划方法,以及在合理时间内给出满意结果的四种启发式算法。仿真结果表明,最佳启发式算法与最优解的平均偏差为0.03% ~ 0.65%。针对实际工作负载,对二叉搜索算法进行了挑战,以评估灵活性对服务质量的影响。
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来源期刊
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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