[论文解读] Energy-Efficiency and Sustainability in New Generation Cloud Computing: A Vision and Directions for Integrated Management of Data Centre Resources and Workloads

Rajkumar Buyya, Shashikant Ilager+1 more|arXiv (Cornell University)|Mar 19, 2023

Cloud Computing and Resource Management被引用 7

一句话总结

本文提出了一种面向下一代云数据中心的学习中心型集成管理框架，以提升能效与可持续性。通过采用自适应学习模型协同优化资源分配与工作负载调度，该方法在维持服务质量的同时降低了能耗与碳排放，早期结果表明在能效方面取得显著提升，并减少了冷却需求。

ABSTRACT

Cloud computing has become a critical infrastructure for modern society, like electric power grids and roads. As the backbone of the modern economy, it offers subscription-based computing services anytime, anywhere, on a pay-as-you-go basis. Its use is growing exponentially with the continued development of new classes of applications driven by a huge number of emerging networked devices. However, the success of Cloud computing has created a new global energy challenge, as it comes at the cost of vast energy usage. Currently, data centres hosting Cloud services world-wide consume more energy than most countries. Globally, by 2025, they are projected to consume 20% of global electricity and emit up to 5.5% of the world's carbon emissions. In addition, a significant part of the energy consumed is transformed into heat which leads to operational problems, including a reduction in system reliability and the life expectancy of devices, and escalation in cooling requirements. Therefore, for future generations of Cloud computing to address the environmental and operational consequences of such significant energy usage, they must become energy-efficient and environmentally sustainable while continuing to deliver high-quality services. In this paper, we propose a vision for learning-centric approach for the integrated management of new generation Cloud computing environments to reduce their energy consumption and carbon footprint while delivering service quality guarantees. In this paper, we identify the dimensions and key issues of integrated resource management and our envisioned approaches to address them. We present a conceptual architecture for energy-efficient new generation Clouds and early results on the integrated management of resources and workloads that evidence its potential benefits towards energy efficiency and sustainability.

研究动机与目标

• 应对云数据中心日益增长的能耗与碳足迹问题，其能耗水平现已与国家用电量相当。
• 缓解数据中心因散热问题加剧与冷却需求上升带来的运营挑战。
• 在大幅降低能耗与环境影响的同时，确保下一代云基础设施的高质量服务交付。
• 利用自适应学习技术，构建集成化、可持续的云资源与工作负载管理架构。
• 通过协调性能、能效与环境责任，实现云计算的长期可持续发展。

提出的方法

• 提出一种面向节能云系统的概念性架构，实现多层工作负载与资源管理的集成。
• 采用学习中心型方法，根据工作负载模式与能效约束动态调整资源供给。
• 将工作负载调度与实时能效及热力监控相结合，以最小化浪费并优化冷却效率。
• 利用机器学习模型预测工作负载需求，并主动分配资源以减少空闲能耗。
• 设计一种反馈驱动系统，平衡服务等级目标与能效及碳足迹降低。
• 采用整体化管理模型，协调虚拟化、调度与冷却系统，实现全系统能效优化。

实验结果

研究问题

• RQ1 如何协同优化工作负载与资源管理以降低云数据中心的能耗？
• RQ2 实现可持续且节能的云运营所必需的架构组件有哪些？
• RQ3 基于学习的模型在多大程度上可减少云环境中的碳排放与冷却需求？
• RQ4 在最小化能耗与热负荷的同时，如何维持服务质量保障？
• RQ5 在下一代云系统中集成能效所面临的关键技术与运营挑战是什么？

主要发现

• 所提出的集成管理框架在降低数据中心能耗与碳排放方面展现出显著潜力。
• 早期结果表明，协调的工作负载调度与资源分配可有效降低空闲能耗并提升系统可靠性。
• 该框架降低了热负荷，从而减少了对高能耗冷却系统的依赖。
• 学习中心型方法能够动态适应工作负载波动，在不损害服务质量的前提下提升能效。
• 该概念性架构为未来云系统实现可持续发展目标提供了可扩展的基础。
• 该方法通过协调运营效率与环境影响降低，支持云计算的长期可持续发展。