XPS:FULL: MSLO-JS: Towards Multi-SLO-Guaranteed per-Job Scheduling in Datacenters

XPS:FULL：MSLO-JS：在数据中心实现多 SLO 保证的每作业调度

• 批准号: 1629625
• 负责人: Hao Che
• 金额: $ 80万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1629625&HistoricalAwards=false
• 关键词: XPS; FULL; MSLO; JS; Towards

Today's datacenters enable a wide range of applications with diverse service-level objectives (SLOs), e.g., user-facing applications such as web searches and disaster recoveries that require real-time or near-real-time responses, calling for stringent job latency and throughput guarantees. However, due to the lack of a proper abstraction, the existing SLO-aware job resource provisioning approaches are platform dependent and trial-and-error by design. The proposed solution presents a new abstraction that provides an effective separation of concerns and thus makes it possible to develop platform-independent, portable algorithms that translate diverse job SLOs into exact performance objectives (a.k.a. budgets) for constituent tasks, resulting in SLO-guaranteed job resource provisioning. The approach proposed can be proven by design and is expected to provide (a) important insights into computer systems and architecture designs with high performance guarantees and cost-effectiveness; and (b) significant improvements in performance guarantees and resource utilization, and the reduction of operating cost for the increasingly popular cloud computing environment. It is also expected to encourage academia-and-industry,interdisciplinary and cross-layer collaborations.The proposed research develops a sound theoretical foundation to enable SLO-guaranteed job resource provisioning. It explores fundamental design principles and is cross-layer by design, involving a two-layer design, from applications to runtime system and system architecture. At the upper, application layer, with any given job workflow represented in the form of Directed Acyclic Graphs (DAGs), the job SLOs are translated into precise latency budgets for individual task nodes in the DAG, independent of the underlying system to be used to run the job. At the lower, runtime system layer, the subsystems for individual task nodes are selected and the resources are allocated to meet all the task performance budgets and hence the job SLOs. This proposed research will enable us to develop job resource provisioning algorithms with SLO guarantee, while allowing for service consolidation and achieving high datacenter resource utilization.

当今的数据中心支持具有不同服务级别目标（SLO）的各种应用，例如，面向用户的应用程序，如Web搜索和灾难恢复，需要实时或接近实时的响应，要求严格的作业延迟和吞吐量保证。然而，由于缺乏适当的抽象，现有的SLO感知作业资源供应方法是平台依赖的，并且通过设计试错。所提出的解决方案提出了一种新的抽象，该抽象提供了有效的关注点分离，从而可以开发独立于平台的可移植算法，将不同的作业SLO转换为精确的性能目标（也称为性能目标）。预算），从而实现有SLO保证的作业资源调配。所提出的方法可以通过设计来证明，并且期望提供（a）对具有高性能保证和成本效益的计算机系统和架构设计的重要见解;以及（B）在性能保证和资源利用方面的显著改进，以及对于日益流行的云计算环境的运营成本的降低。本研究为实现SLO保证的就业资源提供奠定了坚实的理论基础。它探索了基本的设计原则，并且是跨层的设计，涉及两层设计，从应用程序到运行时系统和系统架构。在上层应用层，对于以有向非循环图（DAG）的形式表示的任何给定作业工作流，作业SLO被转换为DAG中的各个任务节点的精确延迟预算，而与用于运行作业的底层系统无关。在较低的运行时系统层，为各个任务节点选择子系统，并分配资源以满足所有任务性能预算，从而满足作业SLO。这项研究将使我们能够开发具有SLO保证的作业资源配置算法，同时允许服务整合并实现高数据中心资源利用率。

项目成果

ForkTail: a black-box fork-join tail latency prediction model for user-facing datacenter workloads

ForkTail：用于面向用户的数据中心工作负载的黑盒分叉连接尾部延迟预测模型

• DOI: 10.1145/3208040.3208058
• 发表时间: 2018
• 期刊: Proceedings of the 27th International Symposium on High-Performance Parallel and Distributed Computing
• 作者: Nguyen, Minh;Alesawi, Sami;Li, Ning;Che, Hao;Jiang, Hong
• 通讯作者: Jiang, Hong