Evaluation of Storage Systems for Big Data Analytics

abstract: Recent trends in big data storage systems show a shift from disk centric models to memory centric models. The primary challenges faced by these systems are speed, scalability, and fault tolerance. It is interesting to investigate the performance of these two models with respect to some big data applications. This thesis studies the performance of Ceph (a disk centric model) and Alluxio (a memory centric model) and evaluates whether a hybrid model provides any performance benefits with respect to big data applications. To this end, an application TechTalk is created that uses Ceph to store data and Alluxio to perform data analytics. The functionalities of the application include offline lecture storage, live recording of classes, content analysis and reference generation. The knowledge base of videos is constructed by analyzing the offline data using machine learning techniques. This training dataset provides knowledge to construct the index of an online stream. The indexed metadata enables the students to search, view and access the relevant content. The performance of the application is benchmarked in different use cases to demonstrate the benefits of the hybrid model.Dissertation/ThesisMasters Thesis Computer Science 201

Multithreaded variant calling in elPrep 5

We present elPrep 5, which updates the elPrep framework for processing sequencing alignment/map files with variant calling. elPrep 5 can now execute the full pipeline described by the GATK Best Practices for variant calling, which consists of PCR and optical duplicate marking, sorting by coordinate order, base quality score recalibration, and variant calling using the haplotype caller algorithm. elPrep 5 produces identical BAM and VCF output as GATK4 while significantly reducing the runtime by parallelizing and merging the execution of the pipeline steps. Our benchmarks show that elPrep 5 speeds up the runtime of the variant calling pipeline by a factor 8-16x on both whole-exome and whole-genome data while using the same hardware resources as GATK4. This makes elPrep 5 a suitable drop-in replacement for GATK4 when faster execution times are needed

Persona: A High-Performance Bioinformatics Framework

Next-generation genome sequencing technology has reached a point at which it is becoming cost-effective to sequence all patients. Biobanks and researchers are faced with an oncoming deluge of genomic data, whose processing requires new and scalable bioinformatics architectures and systems. Processing raw genetic sequence data is computationally expensive and datasets are large. Current software systems can require many hours to process a single genome and generally run only on a single computer. Common file formats are monolithic and row-oriented, a barrier to distributed computation. To address these challenges, we built Persona, a cluster-scale, high-throughput bioinformatics framework. Persona currently supports paired-read alignment, sorting, and duplicate marking using well-known algorithms and techniques. Persona can significantly reduce end-to-end processing times for bioinformatics computations. A new Aggregate Genomic Data (AGD) format unifies sample data and analysis results, while enabling efficient distributed computation and I/O. In a case study on sequence alignment, Persona sustains 1.353 gigabases aligned per second with 101 base pair reads on a 32-node cluster and can align a full genome in ~16.7 seconds using the SNAP algorithm. Our results demonstrate that: (1) alignment computation with Persona scales linearly across servers with no measurable completion-time imbalance and negligible framework overheads; (2) on a single server, sorting with Persona and AGD is up to 2.3× faster than commonly used tools, while duplicate marking is 3× faster; (3) with AGD, a 7 node COTS network storage system can service up to 60 alignment compute nodes; (4) server cost dominates for a balanced system running Persona, while long-term data storage dwarfs the cost of computation

Benchmarking Hadoop performance on different distributed storage systems

Distributed storage systems have been in place for years, and have undergone significant changes in architecture to ensure reliable storage of data in a cost-effective manner. With the demand for data increasing, there has been a shift from disk-centric to memory-centric computing - the focus is on saving data in memory rather than on the disk. The primary motivation for this is the increased speed of data processing. This could, however, mean a change in the approach to providing the necessary fault-tolerance - instead of data replication, other techniques may be considered. One example of an in-memory distributed storage system is Tachyon. Instead of replicating data files in memory, Tachyon provides fault-tolerance by maintaining a record of the operations needed to generate the data files. These operations are replayed if the files are lost. This approach is termed lineage. Tachyon is already deployed by many well-known companies. This thesis work compares the storage performance of Tachyon with that of the on-disk storage systems HDFS and Ceph. After studying the architectures of well-known distributed storage systems, the major contribution of the work is to integrate Tachyon with Ceph as an underlayer storage system, and understand how this affects its performance, and how to tune Tachyon to extract maximum performance out of it

Data-intensive Systems on Modern Hardware : Leveraging Near-Data Processing to Counter the Growth of Data

Over the last decades, a tremendous change toward using information technology in almost every daily routine of our lives can be perceived in our society, entailing an incredible growth of data collected day-by-day on Web, IoT, and AI applications. At the same time, magneto-mechanical HDDs are being replaced by semiconductor storage such as SSDs, equipped with modern Non-Volatile Memories, like Flash, which yield significantly faster access latencies and higher levels of parallelism. Likewise, the execution speed of processing units increased considerably as nowadays server architectures comprise up to multiple hundreds of independently working CPU cores along with a variety of specialized computing co-processors such as GPUs or FPGAs. However, the burden of moving the continuously growing data to the best fitting processing unit is inherently linked to today’s computer architecture that is based on the data-to-code paradigm. In the light of Amdahl's Law, this leads to the conclusion that even with today's powerful processing units, the speedup of systems is limited since the fraction of parallel work is largely I/O-bound. Therefore, throughout this cumulative dissertation, we investigate the paradigm shift toward code-to-data, formally known as Near-Data Processing (NDP), which relieves the contention on the I/O bus by offloading processing to intelligent computational storage devices, where the data is originally located. Firstly, we identified Native Storage Management as the essential foundation for NDP due to its direct control of physical storage management within the database. Upon this, the interface is extended to propagate address mapping information and to invoke NDP functionality on the storage device. As the former can become very large, we introduce Physical Page Pointers as one novel NDP abstraction for self-contained immutable database objects. Secondly, the on-device navigation and interpretation of data are elaborated. Therefore, we introduce cross-layer Parsers and Accessors as another NDP abstraction that can be executed on the heterogeneous processing capabilities of modern computational storage devices. Thereby, the compute placement and resource configuration per NDP request is identified as a major performance criteria. Our experimental evaluation shows an improvement in the execution durations of 1.4x to 2.7x compared to traditional systems. Moreover, we propose a framework for the automatic generation of Parsers and Accessors on FPGAs to ease their application in NDP. Thirdly, we investigate the interplay of NDP and modern workload characteristics like HTAP. Therefore, we present different offloading models and focus on an intervention-free execution. By propagating the Shared State with the latest modifications of the database to the computational storage device, it is able to process data with transactional guarantees. Thus, we achieve to extend the design space of HTAP with NDP by providing a solution that optimizes for performance isolation, data freshness, and the reduction of data transfers. In contrast to traditional systems, we experience no significant drop in performance when an OLAP query is invoked but a steady and 30% faster throughput. Lastly, in-situ result-set management and consumption as well as NDP pipelines are proposed to achieve flexibility in processing data on heterogeneous hardware. As those produce final and intermediary results, we continue investigating their management and identified that an on-device materialization comes at a low cost but enables novel consumption modes and reuse semantics. Thereby, we achieve significant performance improvements of up to 400x by reusing once materialized results multiple times