数据中心的可持续性资源分配

Jingzhe Wang, Balaji Palanisamy, Jinlai Xu
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引用次数: 1

摘要

在大数据时代,云计算为提供计算服务,处理各种数据密集型工作负载提供了一种有效的使用模式。数据中心容量规划和资源分配策略在数据中心的长期生命周期管理中起着至关重要的作用。在确保良好性能的同时,有效地设计和管理数据中心基础设施对于最大限度地减少数据中心的碳足迹至关重要。传统的解决方案主要侧重于优化数据中心运营阶段的影响,包括在资源管理阶段降低能源成本。在本文中,我们提出了一个两阶段的可持续性资源分配和管理框架,用于数据中心生命周期管理,在不影响工作性能和服务质量的情况下,共同优化数据中心制造阶段和运营阶段的影响。所建议的方法的第一阶段通过一种新颖的制造成本意识服务器供应计划来最小化数据中心建设阶段的碳足迹。在阶段2中,该方法使用服务器生命周期感知的资源分配方案和制造成本感知的替换计划来最小化操作阶段的碳足迹。通过使用数据中心中生成的实际工作负载进行大量实验,对所提出的技术进行了评估。评估结果表明,所提出的框架在不影响工作负载中工作的性能的情况下显著降低了数据中心的碳足迹。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sustainability-aware Resource Provisioning in Data Centers
In the big data era, cloud computing provides an effective usage model for providing computing services to handle diverse data-intensive workloads. Data center capacity planning and resource provisioning policies play a vital role in longterm life cycle management of datacenters. Effective design and management of data center infrastructures while ensuring good performance is critical to minimizing the carbon footprint of the datacenter. Traditional solutions have primarily focused on optimizing data center operational phase impacts including reducing energy cost during the resource management phase. In this paper, we propose a two-phase sustainability-aware resource allocation and management framework for data center life-cycle management that jointly optimizes the data center manufacturing phase and operational phase impact without impacting the performance and service quality for the jobs. Phase 1 of the proposed approach minimizes data center building phase carbon footprint through a novel manufacturing cost-aware server provisioning plan. In phase 2, the approach minimizes the operational phase carbon footprint using a server lifetime-aware resource allocation scheme and a manufacturing cost-aware replacement plan. The proposed techniques are evaluated through extensive experiments using realistic workloads generated in a data center. The evaluation results show that the proposed framework significantly reduces the carbon footprint in the data center without impacting the performance of the jobs in the workload.
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