Performance- and Energy-Aware HEC Storage Stacks

高端计算（HEC）系统是为性能而设计的，而不是能效。 近年来，HEC用户发现，随着能源成本的增加，网络和磁盘已成为显著的瓶颈;更糟糕的是，科学工作负载差异很大，会占用HEC群集的不同部分，因此无法了解瓶颈在哪里以及能源浪费在哪里。本项目探讨存储堆栈配置对功耗和性能的影响，使用实际的群集配置和现实的科学工作负载。 该研究遵循三个重点：跟踪和分析，自适应集群重新配置和新的存储软件栈。(1)跟踪数据被收集和分析，这些数据联合收割机了大量科学工作负载的性能和能源数据：I/O密集型、网络密集型、内存密集型和CPU密集型。 三个流行的科学集群配置进行了研究，改变许多配置参数。 (2)正在开发工具，以动态调整集群的配置，以适应给定的工作负载，以便在运行长期科学实验或模拟之前优化功率和性能。 (3)新的操作系统软件是专门为优化科学工作负载的功率和性能而开发的：一个新的轻量级文件系统和磁盘I/O调度器。该项目的长期成果有助于社会节省计算能源，而不会过度损害性能。

High-End Computing (HEC) systems are designed for performance, not energy efficiency. In recent years, HEC users have found that as energy costs increase, network and disks have become significant bottlenecks; worse, scientific workloads vary wildly, exercising different parts of HEC clusters, making it impossible to understand where the bottlenecks are and where energy is being wasted.This project explores the impact of storage-stack configurations on power and performance, using actual cluster configurations and realistic scientific workloads. The research follows three thrusts: tracing and analysis, adaptive cluster reconfiguration, and new storage software stacks.(1) Traces are collected and analyzed which combine both performance and energy data on a large set of scientific workloads: I/O-, network-, memory-, and CPU-intensive. Three popular scientific cluster configurations are investigated, varying many configuration parameters. (2) Tools are being developed to dynamically adapt a cluster's configurations to a given workload, so as to optimize power and performance prior to running long-term scientific experiments or simulations. (3) New operating systems software is developed specifically to optimize power and performance for scientific workloads: a new lightweight file system and disk I/O scheduler.The long-term results of this project help society save energy in computing without unduly hurting performance.