CSR: Small: Scalable Coordination for Wide-Area Distributed Systems

CSR：小型：广域分布式系统的可扩展协调

• 批准号: 1527629
• 负责人: Murat Demirbas
• 金额: $ 49.96万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527629&HistoricalAwards=false
• 关键词: CSR; Small; Scalable; Coordination; Wide

Scalable coordination of concurrently executing tasks is a challenging open problem for large-scale distributed systems. For loosely dependent tasks that require little or no communication, simple abstractions such as barrier synchronization suffice for coordination. However, for large-scale cloud-computing and web-services applications that require tighter synchronization (such as online transaction processing systems, distributed file systems, and graph processing applications), a fine-grained complex coordination service is needed. Especially with increasing demand for large-scale web-services for e-commerce, social networking, and Internet of Things, the coordination of tasks over wide-area (i.e., across clusters, across datacenters, and across Internet) has recently gained greater importance.Traditional distributed coordination techniques fail to scale for wide-area networks to support these new generation applications. Centralized coordination fails to scale with respect to the increased distances in the wide-area, whereas distributed coordination fails to scale with respect to the number of nodes involved. This research project claims that it is possible to achieve scalable coordination of distributed tasks over wide-area using a novel hybrid design, called Maestro. The Maestro framework will address the following research questions: (1) What are the limits of fully-centralized and fully-decentralized solutions to coordination, and what are the scalability benefits of a hybrid hierarchical approach? (2) How can locality-awareness be utilized to achieve high-performance across wide-area? (3) How can partition-awareness be utilized to achieve consistency across wide-area? The Maestro framework will employ a hierarchical lock broker architecture with a novel lock-leasing mechanism and smart/adaptive lock migration. This combination allows flexibility of control and provides the best of both centralized and decentralized approaches. As the authority of their respective domains, the brokers learn and adapt to the access patterns of tasks at runtime to improve lock-locality and hence scalability, while they also have autonomy to allow independent tasks to be initiated and executed in a decentralized manner. Maestro will provide optimizations such as proactive leasing of locks to servers even before they are requested, lock migration (changing primary site assignment of locks), and shared/fractional lock-leasing to selectively allow decentralized coordination and relaxed consistency when appropriate.The proposed Maestro framework will be evaluated on two popular distributed application domains: wide-area ZooKeeper for distributed coordination, and wide-area distributed metadata management. Maestro will fill an important gap in the wide-area scalable coordination of tightly-coupled consistency-critical distributed applications, and it will enable further broader impacts through graduate and undergraduate level curriculum development, enhancing scientific/technological understanding via organizing academic workshops, outreaching to K-12 students, recruitment of minority groups, and distributing tools and software to the community.

并发执行任务的可伸缩协调是大规模分布式系统中一个具有挑战性的开放性问题。对于需要很少或不需要通信的松散依赖任务，简单的抽象（如屏障同步）就足以进行协调。然而，对于需要更紧密同步的大规模云计算和web服务应用程序（如在线事务处理系统、分布式文件系统和图形处理应用程序），需要细粒度的复杂协调服务。特别是随着对电子商务、社交网络和物联网的大规模web服务需求的增加，跨广域（即跨集群、跨数据中心和跨Internet）的任务协调最近变得更加重要。传统的分布式协调技术已经无法适应广域网，无法支持这些新一代的应用。集中式协调在广域范围内的距离增加时无法扩展，而分布式协调在涉及的节点数量增加时无法扩展。该研究项目声称，使用一种名为Maestro的新型混合设计，可以在广域范围内实现分布式任务的可扩展协调。Maestro框架将解决以下研究问题：(1)完全集中和完全分散的解决方案对协调的限制是什么，混合分层方法的可扩展性优势是什么？(2)如何利用位置感知实现广域高性能？(3)如何利用分区感知来实现广域一致性？Maestro框架将采用分层锁代理架构，具有新颖的锁租赁机制和智能/自适应锁迁移。这种组合允许控制的灵活性，并提供集中式和分散式方法的最佳选择。作为各自领域的权威，代理在运行时学习和适应任务的访问模式，以提高锁定局部性，从而提高可伸缩性，同时它们还具有自主权，允许以分散的方式启动和执行独立任务。Maestro将提供优化，例如在请求锁之前就主动将锁租赁给服务器、锁迁移（更改锁的主站点分配）和共享/分数锁租赁，以便在适当的时候选择性地允许分散协调和放松一致性。提出的Maestro框架将在两个流行的分布式应用领域进行评估：用于分布式协调的广域ZooKeeper和广域分布式元数据管理。Maestro将填补紧密耦合一致性关键分布式应用程序的广域可扩展协调的重要空白，它将通过研究生和本科生水平的课程开发，通过组织学术研讨会，向K-12学生推广，招募少数群体以及向社区分发工具和软件来增强科学/技术理解，从而产生更广泛的影响。