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Elastic distributed storage systems have been increasingly studied in recent years because power consumption has become a major problem in data centers. Much progress has been made in improving the agility of resizing small- and large-scale distributed storage systems. However, most of these studies focus on metadata based distributed storage systems. On the other hand, emerging consistent hashing...
High performance computing (HPC) applications are becoming more data-intensive and produce increasingly large I/O demands on storage systems. New storage devices such as SSD which has nearly no seek latency and high throughput have been widely used together with HDD to serve as a hybrid storage system. To solve the I/O bottleneck problem, existing hybrid storage solutions such as Burst Buffer have...
High-performance computing (HPC) systems face increasingly critical metadata management challenges, especially in the approaching exascale era. These challenges arise not only from exploding metadata volumes but also from increasingly diverse metadata, which contains data provenance and user-defined attributes in addition to traditional POSIX metadata. This "rich" metadata is critical to...
Distributed storage systems play an increasingly critical role in data centers to meet the ever-increasing data growth demand. Heterogeneous storage systems, with the coexistence of hard disk drives (HDDs) and solid state drives (SSDs), can be an attractive distributed store solution due to the balanced performance, large capacity, and economic cost. The consistent hashing distribution algorithm that...
Fast growing "Big Data" demands present new challenges to the traditional distributed storage system solutions. In order to support cloud-scale data centers, new types of distributed storage systems are emerging. They are designed to scale to thousands of nodes, maintain petabytes of data and be highly reliable. The support for virtual machines is also becoming essential as it is one of...
Property graphs are a promising data model for rich metadata management in high-performance computing (HPC) systems because of their ability to represent not only metadata attributes but also the relationships between them. A property graph can be used to record the relationships between users, jobs, and data, for example, with unique annotations for each entity. This high-volume, power-law distributed...
Most mass data processing applications nowadays often need long, continuous, and uninterrupted data access. Parallel/distributed file systems often use multiple metadata servers to manage the global namespace and provide a reliability guarantee. With the rapid increase of data amount and system scale, the probability of hardware or software failures keeps increasing, which easily leads to multiple...
Hadoop is a popular open-source framework that allows distributed analysis of large datasets using the MapReduce programming model. A distributed file system HDFS is implemented to provide high-throughput access to datasets. HDFS can achieve high performance metadata service but has two disadvantages. First, when the metadata server stores metadata on persistent devices, it is restricted to read and...
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