Ceph存储系统的动态消息感知通信调度器

Yunjung Han, Kwonyong Lee, Sungyong Park
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引用次数: 5

摘要

随着云计算技术的普及,Ceph(一种基于对象的分布式存储系统)由于其出色的性能、可靠性和可伸缩性而成为构建存储后端的一个有吸引力的替代方案。随着存储系统处理海量数据,以及云计算产生的网络流量的快速增长,在存储系统中设计高性能的信使系统成为了许多具有挑战性的问题。虽然异步消息(Ceph的消息之一)是众所周知的高效和灵活,但由于其简单的基于轮询的调度方案(将连接分配给工作线程,而不考虑通过连接传输的工作负载的数量),它包含几个性能问题。这会导致工作线程的不平衡,对Ceph存储系统的性能产生不利影响。本文提出了一种用于Ceph存储系统的动态消息感知通信调度器,该调度器根据传入消息的类型来平衡工作线程的工作负载,同时避免了工作线程之间不必要的连接移动。我们使用遗传算法(GA)来解决这个问题,并使用异步消息实现所提出的调度算法。基准测试结果表明,在来自客户机的相同工作负载和来自客户机的随机工作负载下,所提出的方法的性能分别比原始异步消息传递方法高出12.5%和24%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Dynamic Message-Aware Communication Scheduler for Ceph Storage System
With the proliferation of cloud computing technologies, the Ceph, a distributed object-based storage system has been an attractive alternative to building a storage backend due to its excellent performance, reliability, and scalability. As the storage system processes huge amount data and the network traffic generated from the cloud increases rapidly, designing a high-performance messenger in the storage system has created a lot of challenging issues. Although the async messenger, one of the Ceph's messengers, is known to be efficient and flexible, it contains several performance problems due to its simple round-robin based scheduling scheme that assigns a connection to a worker thread without any consideration for the amount of workloads transferred through the connections. This causes the imbalance of worker threads and adversely affects the performance of the Ceph storage system. This paper proposes a dynamic message-aware communication scheduler for Ceph storage system that balances the workloads of worker threads based on the types of incoming messages, while avoiding unnecessary connection movements among worker threads. We use genetic algorithm (GA) to solve this problem and implement the proposed scheduling algorithm using the async messenger. The benchmarking results show that the proposed approach outperforms the original async messenger by as much as 12.5% under the same workload from clients and 24% under the random workloads from clients.
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