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High Performance File System and I/O Middleware Design for Big Data on HPC Clusters

Islam, Nusrat Sharmin
http://orcid.org/0000-0001-9845-1771
http://rave.ohiolink.edu/etdc/view?acc_num=osu1480476699154944

Abstract Details

2016, Doctor of Philosophy, Ohio State University, Computer Science and Engineering.
Hadoop MapReduce and Spark are the two most popular Big Data processing frameworks of the recent time and Hadoop Distributed File System (HDFS) is the underlying file system for MapReduce, Spark, Hadoop Database, HBase as well as SQL query engines like Hive and Spark SQL. HDFS along with these upper-level middleware is now extensively being used on High Performance Computing (HPC) systems. The large-scale HPC systems, by necessity, are equipped with high performance interconnects like InfiniBand, heterogeneous storage devices like RAM Disk, SSD, HDD, and parallel file systems like Lustre. Non-Volatile Memory (NVM) is emerging and making its way into the HPC systems. Hence, the performance of HDFS, and in turn, the upper-level middleware and applications, is heavily dependent upon how it has been designed and optimized to take the system resources and architecture into account. But HDFS was initially designed to run over low-speed interconnects and disks on commodity clusters. As a result, it cannot efficiently utilize the resources available in HPC systems. For example, HDFS uses Java Socket for communication that leads to the overhead of multiple data copies and offers little overlapping among different phases of operations. Besides, due to the tri-replicated data blocks, HDFS suffers from huge I/O bottlenecks and requires a large volume of local storage. The existing data placement and access policies in HDFS are oblivious to the performance and persistence characteristics of the heterogeneous storage media on modern HPC systems. In addition, even though parallel file systems are optimized for large number of concurrent accesses, Hadoop jobs running over Lustre suffer from huge contention due to the bandwidth limitation of shared file system. This work addresses several of these critical issues in HDFS while proposing efficient and scalable file system and I/O middleware for Big Data in HPC clusters. It proposes an RDMA-Enhanced design of HDFS to improve the communication performance of write and replication. It also proposes a Staged Event Drive Architecture (SEDA)-based approach to maximize overlapping among different phases of HDFS operations. It proposes a hybrid design (Triple-H) to reduce the I/O bottlenecks and local storage requirements of HDFS through enhanced data placement policies that can efficiently utilize the heterogeneous storage devices available in HPC platforms. This thesis studies the impact of in-memory files systems for Hadoop and Spark and presents acceleration techniques for iterative applications with intelligent use of in-memory and heterogeneous storage. It further proposes advanced data access strategies that take into account locality, topology, and storage types for Hadoop and Spark on heterogeneous (storage) HPC clusters. This thesis carefully analyzes the challenges for incorporating NVM for Big Data file system and proposes an NVM-based design of HDFS (NVFS) that leverages the byte-addressability of NVM for HDFS I/O and RDMA communication. It also co-designs Spark and HBase to utilize the NVM in NVFS in a cost-effective manner via identifying the performance-critical data for each of these upper-level middleware. It also proposes the design of a burst buffer system using RDMA-based Memcached for integrating Hadoop with Lustre. The designs proposed in this thesis have been evaluated on 64-nodes (1024 cores) testbed on TACC Stampede and 32-nodes (768 cores) testbed on SDSC Comet. These designs increase the HDFS throughput by up to 7x while improving the performance of Big Data applications by up to 79% and reduce the local storage requirements by 66% over HDFS.
Dhabaleswar Panda (Advisor)
Ponnuswamy Sadayappan (Committee Member)
Radu Teodorescu (Committee Member)
223 p.
Computer Engineering; Computer Science

Recommended Citations

Citations

  • Islam, N. S. (2016). High Performance File System and I/O Middleware Design for Big Data on HPC Clusters [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1480476699154944

    APA Style (7th edition)

  • Islam, Nusrat. High Performance File System and I/O Middleware Design for Big Data on HPC Clusters. 2016. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1480476699154944.

    MLA Style (8th edition)

  • Islam, Nusrat. "High Performance File System and I/O Middleware Design for Big Data on HPC Clusters." Doctoral dissertation, Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1480476699154944

    Chicago Manual of Style (17th edition)

osu1480476699154944
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This open access ETD is published by The Ohio State University and OhioLINK.