A Framework for Next-Generation Scheduling and Task Management for Extreme-Scale Computing

支持极限计算的下一代调度和任务管理框架辛康和阿比吉特·博斯密歇根大学这一研究项目的目标是为支持极限计算的调度和资源管理开发高效的算法和健壮的软件。这样的计算可能涉及配置为高端计算系统的数万个处理器。需要比目前使用的更有效的调度和资源管理系统来解决这种大型系统的可伸缩性和容错问题。同样的论点也适用于许多新兴应用程序日益复杂的需求。当前一代的调度器主要管理为应用程序静态配置的CPU集群。应用程序被分配固定数量的处理器或节点(对于SMP系统)，并且不期望在执行期间修改其资源需求。这可能会导致较低的资源利用率。此外，当前的大多数调度器都不能为运行的工作负载提供透明的容错。一个或多个处理器/节点故障通常会终止这些处理器上当前正在执行的任务，从而导致浪费周期。容错调度对于未来HEC系统在极大数量的处理器上的可扩展性至关重要。高端系统越来越多地用于计算密集型和数据密集型应用，这要求未来的调度系统在将任务映射到适当的资源时必须解决联合调度CPU、I/O和网络资源的问题。在某些HEC架构中，由于底层的互连拓扑，进程的放置会对应用程序的整体性能产生影响。将开发一种用于极端规模计算系统的调度和资源管理的集成软件框架，其提供以下能力：(I)在线工作负载表征，(Ii)基于资源利用和排队工作负载的时间序列建模和预测的预测调度，(Iii)对被一个或多个故障中断的应用程序的透明容错调度，以及(Iv)高效的启发式和进化算法，将工作量和资源特性和预测作为调度决策的一部分。这项工作代表了调度理论和健壮软件开发的结合。这些算法和在密歇根大学高级计算中心(CAC)的生产HEC环境中的软件框架。CAC HEC工厂提供了一个由1400多个CPU组成的试验台，这些CPU代表多处理器家族(AMD Athlons、Opterons、Apple XServe/G5)和几个互连系统，如千兆位以太网和Myrinet。这项研究的智力价值将是促进HEC系统调度和资源管理的最先进水平。通过在CAC实施和部署建议的框架，我们将能够从各种终端用户和应用程序中收集实际的工作负载跟踪。这项研究将对此类系统的健壮容错调度算法及其实现软件的开发起到催化剂作用。我们专门解决了容错机制的可扩展性，例如检查点/重新启动和可以跨数千个处理器扩展的I/O。此外，我们建议的研究、外展和教育活动的整合将对其他HEC中心和调度研究社区产生更广泛的影响。作为该项目的一部分开发的框架和我们的研究成果将通过开放源码软件和高质量出版物向工业界和学术界传播。

A Framework for Next-generation Scheduling and Task Management for Extreme-scale ComputingKang G. Shin and Abhijit BoseThe University of MichiganThe objective of this research project is to develop efficient algorithms and robust software for scheduling and resource management in support of extreme-scale computing. Such computations may involve tens of thousands of processors configured as a high-end computing system. More efficient scheduling and resource management systems than those currently used are needed to address scalability and fault-tolerance for such large systems. The same argument also holds for the increasingly complex requirements of many emerging applications. The current-generation schedulers primarily manage a cluster of CPUs that are configured statically for an application. An application is allocated a fixed number of processors or nodes (for SMP systems), and is not expected to modify its resource requirement during the execution. This may result in lower resource utilization. Furthermore, most of the current schedulers do not provide transparent fault-tolerance to the running workload. One or more processor/node failures often terminate the currently-executing task on these processors, resulting in wasted cycles. Fault-tolerant scheduling will be critical to the scalability of future HEC systems to an extremely large number of processors. The increasing usage of high-end systems for both computation- and data-intensive applications requires that future scheduling systems must address the problem of co-scheduling CPUs, I/O and network resources when mapping tasks to appropriate resources. In some HEC architectures, the placement of processes has an effect on the overall performance of the application due to the underlying interconnection topology. Both co-scheduling and workload-aware scheduling will be important for future high-end computing systems.An integrated software framework will be developed for scheduling and resource management for extreme-scale computing systems that provides the following capabilities: (i) on-line workload characterization, (ii) predictive scheduling based on time-series modeling and forecasting of resource utilization and queued workload, (iii) transparent fault-tolerant scheduling of applications that are interrupted by one or more faults, and (iv) efficient heuristic and evolutionary algorithms that consider the workload and resource characteristics and forecasts as part of the scheduling decision.This work represents a mix of scheduling theory and robust software development. These algorithms and the software framework in a production HEC environment at the Center for Advanced Computing (CAC) at the University of Michigan. The CAC HEC facility provides a testbed consisting of over 1400 CPUs representingmultiple processor families (AMD Athlons, Opterons, Apple Xserve/G5) and several interconnection systems such as Gigabit Ethernet and Myrinet.The intellectual merit of this research will be to advance the state-of-the-art in scheduling and resource management for HEC systems. By implementing and deploying the proposed framework at CAC, we will be able to collect realistic workload traces from a diverse array of end-users and applications. This research will serve as a catalyst for the development of robust fault-tolerant scheduling algorithms and their implementation software for such systems. We specifically address the scalability of fault-tolerance mechanisms such as checkpointing/restartand I/O that can scale across thousands of processors. Furthermore, our proposed integration of research, outreach and education activities will make broader impacts to other HEC centers and the scheduling research community. The framework developed as part of this project and our research results will be disseminated to industry and academia through open-source software and high-quality publications.