CSR: Small: Bridging Efficiency and Low Latency in Warehouse-scale Computing

计算现在是人类努力的各个方面的基本工具和创新的催化剂，包括医疗保健、教育、科学、商业、政府和娱乐。越来越多的计算在私有云和公共云上执行，这主要是因为托管云的仓库级系统的最终用户和运营商的成本和可扩展性优势。我们期望这些系统能为数百万用户提供对PB级数据的即时、个性化和情景访问。该项目的目标是提高仓库规模系统的能力和效率。具体地说，我们的目标是调和假定的大规模低延迟处理与能源消耗和资源使用效率之间的不兼容性。我们的目标是将仓库级系统的能源和资源效率提高2倍-5倍，同时允许大规模低延迟处理。同样重要的是，我们的目标是提高我们对现代计算系统中可伸缩性、低延迟和能源或资源效率之间权衡的理解。该项目专注于在线数据密集型工作负载，如搜索、社交网络、实时分析和机器学习分析，这些工作负载占用仓库级系统中的数千台服务器，并构成巨大的扩展挑战。其严格的延迟限制、大的状态要求和高的通信扇出使得在应用程序之间应用已知的降低功率和共享资源的技术变得困难。因此，它们通常消耗相当大比例的峰值功率，并使用非共享服务器，即使在中等或低用户流量的频繁时段也是如此。在很大程度上，低延迟和高效率被认为与这些工作负载不兼容。为了弥补这一差距，该项目使用跨层方法来监控端到端工作负载性能和服务质量，以指导系统范围的电源管理和资源管理。第一步是开发一种电源管理系统，在不影响延迟保证的情况下，在低或中等负载期间提高仓库规模系统的能量比例。第二步是开发系统范围的资源管理系统，该系统允许在低或中等负载期间在延迟关键型工作负载和其他工作负载之间积极共享服务器，而不会影响延迟保证。第三步是设计操作系统策略，以便在服务器内共存的工作负载之间实现性能隔离。最后一步是使用前面步骤中的见解来评估现有和拟议的服务器架构在在线数据密集型工作负载的能源和资源效率方面的效率。

Computing is now an essential tool and a catalyst for innovation for all aspects of human endeavor, including healthcare, education, science, commerce, government, and entertainment. An increasing amount of computing is performed on private and public clouds, primarily due to the cost and scalability benefits for both the end-users and operators of the warehouse-scale systems that host clouds. We have come to expect that these systems provide millions of users with instantaneous, personalized, and contextual access to petabytes of data. The goal of this project is to improve the capabilities and efficiency of warehouse-scale systems. Specifically, we aim to reconcile the presumed incompatibility between low-latency processing at massive scales and efficiency in terms of energy consumption and resource usage. We aim to improve energy and resource efficiency in warehouse-scale systems by factors of 2x-5x while allowing for low-latency processing at massive scales. Equally important, we aim to improve our understanding of the tradeoffs between scalability, low latency, and energy or resource efficiency in modern computing systems.The project focuses on on-line, data-intensive workloads, such as search, social networking, real-time analytics, and machine learning analysis, that occupy thousands of servers in warehouse-scale systems and pose significant scaling challenges. Their strict latency constraints, large state requirements, and high communication fan-out makes it difficult to apply known techniques for power reduction and resource sharing across applications. Hence, they typically consume a significant percentage of peak power and use non-shared servers even during the frequent periods of medium or low user traffic. To a large extent, low latency and high efficiency are considered incompatible for these workloads. To bridge this gap, the project uses a cross-layer approach that monitors end-to-end workload performance and quality-of-service to guide system-wide power management and resource management. The first step is to develop a power management system that improves the energy proportionality of warehouse-scale systems during periods of low or medium load without compromising latency guarantees. The second step is to develop a system-wide resource management system that allows aggressive server sharing between latency-critical workloads and other workloads during periods of low or medium load without compromising latency guarantees. The third step is to design operating system policies for performance isolation between co-located workloads within a server. The final step is to use the insights from the previous steps to evaluate the efficacy of existing and proposed server architectures with respect to energy and resource efficiency for on-line, data-intensive workloads.