No abstract

With the rapid increase in the amount of data produced and the development of new types of storage devices, storage tiering continues to be a popular way to achieve a good tradeoff between performance and costeffectiveness. In a basic two-tier storage system, a storage tier with higher performance and typically higher cost (the fast tier) is used to store frequently-accessed (active) data while a large amount of less-active data are stored in the lower-performance and low-cost tier (the slow tier). Data are migrated between these two tiers according to their activity. In this article, we propose a Tier-aware Data Deduplication-based File System, called TDDFS, which can operate efficiently on top of a two-tier storage environment. Specifically, to achieve better performance, nearly all file operations are performed in the fast tier. To achieve higher cost-effectiveness, files are migrated from the fast tier to the slow tier if they are no longer active, and this migration is done with data deduplication. The distinctiveness of our design is that it maintains the non-redundant (unique) chunks produced by data deduplication in both tiers if possible. When a file is reloaded (called a reloaded file) from the slow tier to the fast tier, if some data chunks of the file already exist in the fast tier, then the data migration of these chunks from the slow tier can be avoided. Our evaluation shows that TDDFS achieves close to the best overall performance among various file-tiering designs for two-tier storage systems.

Active storage devices and in-storage computing are proposed and developed in recent years to effectively reduce the amount of required data traffic and to improve the overall application performance. They are especially preferred in the compute-storage disaggregated infrastructure. In both techniques, a simple computing module is added to storage devices/servers such that some stored data can be processed in the storage devices/servers before transmitted to application servers. This can reduce the required network bandwidth and offload certain computing requirements from application servers to storage devices/servers. However, several challenges exist when designing an in-storage computing based architecture for applications. These include what computing functions need to be offloaded, how to design the protocol between in-storage module and application servers, and how to deal with the caching issue in application servers. HBase is an important and widely used distributed Key-Value Store. It stores and indexes key-value pairs in large files in a storage system like HDFS. However, its performance, especially read performance, is impacted by the heavy traffics between HBase RegionServers and storage servers in the compute-storage disaggregated infrastructure when the available network bandwidth is limited. We propose an I n- S torage based HBase architecture, called IS-HBase , to improve the overall performance and to address the aforementioned challenges. First, IS-HBase executes a data pre-processing module ( I n- S torage S can N er, called ISSN ) for some read queries and returns the requested key-value pairs to RegionServers instead of returning data blocks in HFile. IS-HBase carries out compactions in storage servers to reduce the large amount of data being transmitted through the network and thus the compaction execution time is effectively reduced. Second, a set of new protocols are proposed to address the communication and coordination between HBase RegionServers at computing nodes and ISSNs at storage nodes. Third, a new self-adaptive caching scheme is proposed to better serve the read queries with fewer I/O operations and less network traffic. According to our experiments, the IS-HBase can reduce up to 97% network traffic for read queries and the throughput (queries per second) is significantly less affected by the fluctuation of available network bandwidth. The execution time of compaction in IS-HBase is only about 6.31% to 41.84% of the execution time of legacy HBase. In general, IS-HBase demonstrates the potential of adopting in-storage computing for other data-intensive distributed applications to significantly improve performance in compute-storage disaggregated infrastructure.