Zhike Li , Yong Wang , Shiqiang Nie , Jinyu Wang , Chi Zhang , Fangxing Yu , Zhankun Zhang , Song Liu , Weiguo Wu
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引用次数: 0
Abstract
With the adoption of new storage technologies like NVMs, tiered storage has gained popularity in large-scale, hyper-converged clusters. The storage back-end of hyper-converged systems supports data storage on devices such as SSDs and HDDs, yet lacks fine-grained tiered storage solutions. For example, Ceph selects storage nodes based primarily on limited criteria, such as node storage capacity, disregarding the diverse performance characteristics of various storage media. In this study, we introduce Olsync, an object-level tiering and coordination system designed to enhance storage resource utilization and data access performance. Specifically, Olsync employs PIPO (Packet-In-Packet-Out), an innovative network communication framework based on Software-defined Networking (SDN), to collaboratively optimize both the network control plane and underlying data plane. Additionally, Olsync can offer efficient object-level tiering and coordination services using the global views obtained by PIPO (e.g., data access patterns and interfering object requests) to make tiered storage and performance optimization decisions. We incorporated the Olsync prototype into Ceph and performed a thorough comparison with contemporary state-of-the-art systems. The evaluation results demonstrate that Olsync significantly enhances system response time (up to 68%), I/O throughput (up to 24%), and 99th percentile latency (up to 16%) in various environments.
期刊介绍:
Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications.
Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration.
Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.