The state of SQL-on-Hadoop in the cloud

Managed Hadoop in the cloud, especially SQL-on-Hadoop, has been gaining attention recently. On Platform-as-a-Service (PaaS), analytical services like Hive and Spark come preconfigured for general-purpose and ready to use. Thus, giving companies a quick entry and on-demand deployment of ready SQL-like solutions for their big data needs. This study evaluates cloud services from an end-user perspective, comparing providers including: Microsoft Azure, Amazon Web Services, Google Cloud, and Rackspace. The study focuses on performance, readiness, scalability, and cost-effectiveness of the different solutions at entry/test level clusters sizes. Results are based on over 15,000 Hive queries derived from the industry standard TPC-H benchmark. The study is framed within the ALOJA research project, which features an open source benchmarking and analysis platform that has been recently extended to support SQL-on-Hadoop engines. The ALOJA Project aims to lower the total cost of ownership (TCO) of big data deployments and study their performance characteristics for optimization. The study benchmarks cloud providers across a diverse range instance types, and uses input data scales from 1GB to 1TB, in order to survey the popular entry-level PaaS SQL-on-Hadoop solutions, thereby establishing a common results-base upon which subsequent research can be carried out by the project. Initial results already show the main performance trends to both hardware and software configuration, pricing, similarities and architectural differences of the evaluated PaaS solutions. Whereas some providers focus on decoupling storage and computing resources while offering network-based elastic storage, others choose to keep the local processing model from Hadoop for high performance, but reducing flexibility. Results also show the importance of application-level tuning and how keeping up-to-date hardware and software stacks can influence performance even more than replicating the on-premises model in the cloud.This work is partially supported by the Microsoft Azure for Research program, the European Research Council (ERC) under the EUs Horizon 2020 programme (GA 639595), the Spanish Ministry of Education (TIN2015-65316-P), and the Generalitat de Catalunya (2014-SGR-1051).Peer ReviewedPostprint (author's final draft

고성능 컴퓨팅 시스템에서 버스트 버퍼를 위한 I/O 분리 기법의 실증적 구현

학위논문(석사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2019. 8. 엄현상.To meet the exascale I/O requirements in the High-Performance Computing (HPC), a new I/O subsystem, named Burst Buffer, based on non-volatile memory, has been developed. However, the diverse HPC workloads and the bursty I/O pattern cause severe data fragmentation to SSDs, which creates the need for expensive garbage collection (GC) and also increase the number of bytes actually written to SSD. The new multi-stream feature in SSDs offers an option to reduce the cost of garbage collection. In this paper, we leverage this multi-stream feature to group the I/O streams based on the user IDs and implement this strategy in a burst buffer we call BIOS, short for Burst Buffer with an I/O Separation scheme. Furthermore, to optimize the I/O separation scheme in burst buffer environments, we propose a stream-aware scheduling policy based on burst buffer pools in workload manager and implement the real burst buffer system, BIOS framework, by integrating the BIOS with workload manager. We evaluate the BIOS and framework with a burst buffer I/O traces from Cori Supercomputer including a diverse set of applications. We also disclose and analyze the benefits and limitations of using I/O separation scheme in HPC systems. Experimental results show that the BIOS could improve the performance by 1.44× on average and reduce the Write Amplification Factor (WAF) by up to 1.20×, and prove that the framework can keep on the benefits of the I/O separation scheme in the HPC environment.Abstract Introduction 1 Background and Challenges 5 Burst Buffer 5 Write Amplification in SSDs 6 Multi-streamed SSD 7 Challenges of Multi-stream Feature in Burst Buffers 7 I/O Separation Scheme in Burst Buffer 10 Stream Allocation Criteria 10 Implementation 12 Limitations of User ID-based Stream Allocation 14 BIOS Framework 15 Support in Workload Manager 15 Burst Buffer Pools 16 Stream-Aware Scheduling Policy 18 Workflow of BIOS Framework 20 Evaluation 21 Experiment Setup 21 Evaluation with Synthetic Workload 21 Evaluation with HPC Applications 25 Evaluation with Emulated Workload 27 Evaluation with Different Striping Configuration 29 Evaluation on BIOS Framework 30 Summary and Lessons Learned 33 An I/O Separation Scheme in Burst Buffer 33 Evaluation with Synthetic Workload 33 Evaluation with HPC Applications 33 Evaluation with Emulated Workload 34 Evaluation with Striping Configurations 34 A BIOS Framework 34 Evaluation with Real Burst Buffer Environments 34 Discussion 36 Limited Number of Nodes 36 Advanced BIOS Framework 37 Related work 38 Conclusions 40 Bibliography 42 초록 48Maste

CRAID: Online RAID upgrades using dynamic hot data reorganization

Current algorithms used to upgrade RAID arrays typically require large amounts of data to be migrated, even those that move only the minimum amount of data required to keep a balanced data load. This paper presents CRAID, a self-optimizing RAID array that performs an online block reorganization of frequently used, long-term accessed data in order to reduce this migration even further. To achieve this objective, CRAID tracks frequently used, long-term data blocks and copies them to a dedicated partition spread across all the disks in the array. When new disks are added, CRAID only needs to extend this process to the new devices to redistribute this partition, thus greatly reducing the overhead of the upgrade process. In addition, the reorganized access patterns within this partition improve the array’s performance, amortizing the copy overhead and allowing CRAID to offer a performance competitive with traditional RAIDs. We describe CRAID’s motivation and design and we evaluate it by replaying seven real-world workloads including a file server, a web server and a user share. Our experiments show that CRAID can successfully detect hot data variations and begin using new disks as soon as they are added to the array. Also, the usage of a dedicated partition improves the sequentiality of relevant data access, which amortizes the cost of reorganizations. Finally, we prove that a full-HDD CRAID array with a small distributed partition (<1.28% per disk) can compete in performance with an ideally restriped RAID-5 and a hybrid RAID-5 with a small SSD cache.Peer ReviewedPostprint (published version

Survey of storage systems for high-performance computing

In current supercomputers, storage is typically provided by parallel distributed file systems for hot data and tape archives for cold data. These file systems are often compatible with local file systems due to their use of the POSIX interface and semantics, which eases development and debugging because applications can easily run both on workstations and supercomputers. There is a wide variety of file systems to choose from, each tuned for different use cases and implementing different optimizations. However, the overall application performance is often held back by I/O bottlenecks due to insufficient performance of file systems or I/O libraries for highly parallel workloads. Performance problems are dealt with using novel storage hardware technologies as well as alternative I/O semantics and interfaces. These approaches have to be integrated into the storage stack seamlessly to make them convenient to use. Upcoming storage systems abandon the traditional POSIX interface and semantics in favor of alternative concepts such as object and key-value storage; moreover, they heavily rely on technologies such as NVM and burst buffers to improve performance. Additional tiers of storage hardware will increase the importance of hierarchical storage management. Many of these changes will be disruptive and require application developers to rethink their approaches to data management and I/O. A thorough understanding of today's storage infrastructures, including their strengths and weaknesses, is crucially important for designing and implementing scalable storage systems suitable for demands of exascale computing

Understanding and Optimizing Flash-based Key-value Systems in Data Centers

Flash-based key-value systems are widely deployed in today’s data centers for providing high-speed data processing services. These systems deploy flash-friendly data structures, such as slab and Log Structured Merge(LSM) tree, on flash-based Solid State Drives(SSDs) and provide efficient solutions in caching and storage scenarios. With the rapid evolution of data centers, there appear plenty of challenges and opportunities for future optimizations. In this dissertation, we focus on understanding and optimizing flash-based key-value systems from the perspective of workloads, software, and hardware as data centers evolve. We first propose an on-line compression scheme, called SlimCache, considering the unique characteristics of key-value workloads, to virtually enlarge the cache space, increase the hit ratio, and improve the cache performance. Furthermore, to appropriately configure increasingly complex modern key-value data systems, which can have more than 50 parameters with additional hardware and system settings, we quantitatively study and compare five multi-objective optimization methods for auto-tuning the performance of an LSM-tree based key-value store in terms of throughput, the 99th percentile tail latency, convergence time, real-time system throughput, and the iteration process, etc. Last but not least, we conduct an in-depth, comprehensive measurement work on flash-optimized key-value stores with recently emerging 3D XPoint SSDs. We reveal several unexpected bottlenecks in the current key-value store design and present three exemplary case studies to showcase the efficacy of removing these bottlenecks with simple methods on 3D XPoint SSDs. Our experimental results show that our proposed solutions significantly outperform traditional methods. Our study also contributes to providing system implications for auto-tuning the key-value system on flash-based SSDs and optimizing it on revolutionary 3D XPoint based SSDs

Probabilistic Page Replacement Policy in Buffer Cache Management for Flash-Based Cloud Databases

In the fast evolution of storage systems, the newly emerged flash memory-based Solid State Drives (SSDs) are becoming an important part of the computer storage hierarchy. Amongst the several advantages of flash-based SSDs, high read performance, and low power consumption are of primary importance. Amongst its few disadvantages, its asymmetric I/O latencies for read, write and erase operations are the most crucial for overall performance. In this paper, we proposed two novel probabilistic adaptive algorithms that compute the future probability of reference based on recency, frequency, and periodicity of past page references. The page replacement is performed by considering the probability of reference of cached pages as well as asymmetric read-write-erase properties of flash devices. The experimental results show that our proposed method is successful in minimizing the performance overheads of flash-based systems as well as in maintaining the good hit ratio. The results also justify the utility of a genetic algorithm in maximizing the overall performance gains

RAID Organizations for Improved Reliability and Performance: A Not Entirely Unbiased Tutorial (1st revision)

RAID proposal advocated replacing large disks with arrays of PC disks, but as the capacity of small disks increased 100-fold in 1990s the production of large disks was discontinued. Storage dependability is increased via replication or erasure coding. Cloud storage providers store multiple copies of data obviating for need for further redundancy. Varitaions of RAID based on local recovery codes, partial MDS reduce recovery cost. NAND flash Solid State Disks - SSDs have low latency and high bandwidth, are more reliable, consume less power and have a lower TCO than Hard Disk Drives, which are more viable for hyperscalers.Comment: Submitted to ACM Computing Surveys. arXiv admin note: substantial text overlap with arXiv:2306.0876

Achieving High Reliability and Efficiency in Maintaining Large-Scale Storage Systems through Optimal Resource Provisioning and Data Placement

With the explosive increase in the amount of data being generated by various applications, large-scale distributed and parallel storage systems have become common data storage solutions and been widely deployed and utilized in both industry and academia. While these high performance storage systems significantly accelerate the data storage and retrieval, they also bring some critical issues in system maintenance and management. In this dissertation, I propose three methodologies to address three of these critical issues. First, I develop an optimal resource management and spare provisioning model to minimize the impact brought by component failures and ensure a highly operational experience in maintaining large-scale storage systems. Second, in order to cost-effectively integrate solid-state drives (SSD) into large-scale storage systems, I design a holistic algorithm which can adaptively predict the popularity of data objects by leveraging temporal locality in their access pattern and adjust their placement among solid-state drives and regular hard disk drives so that the data access throughput as well as the storage space efficiency of the large-scale heterogeneous storage systems can be improved. Finally, I propose a new checkpoint placement optimization model which can maximize the computation efficiency of large-scale scientific applications while guarantee the endurance requirements of the SSD-based burst buffer in high performance hierarchical storage systems. All these models and algorithms are validated through extensive evaluation using data collected from deployed large-scale storage systems and the evaluation results demonstrate our models and algorithms can significantly improve the reliability and efficiency of large-scale distributed and parallel storage systems