Researches On Reverse Lookup Problem In Distributed File System

Recent years have witnessed an increasing demand for super data clusters. The super data clusters have reached the petabyte-scale can consist of thousands or tens of thousands storage nodes at a single site. For this architecture, reliability is becoming a great concern. In order to achieve a high reliability, data recovery and node reconstruction is a must. Although extensive research works have investigated how to sustain high performance and high reliability in case of node failures at large scale, a reverse lookup problem, namely finding the objects list for the failed node remains open. This is especially true for storage systems with high requirement of data integrity and availability, such as scientific research data clusters and etc. Existing solutions are either time consuming or expensive. Meanwhile, replication based block placement can be used to realize fast reverse lookup. However, they are designed for centralized, small-scale storage architectures. In this thesis, we propose a fast and efficient reverse lookup scheme named Group-based Shifted Declustering (G-SD) layout that is able to locate the whole content of the failed node. G-SD extends our previous shifted declustering layout and applies to large-scale file systems. Our mathematical proofs and real-life experiments show that G-SD is a scalable reverse lookup scheme that is up to one order of magnitude faster than existing schemes

An R*-Tree Based Semi-Dynamic Clustering Method for the Efficient Processing of Spatial Join in a Shared-Nothing Parallel Database System

The growing importance of geospatial databases has made it essential to perform complex spatial queries efficiently. To achieve acceptable performance levels, database systems have been increasingly required to make use of parallelism. The spatial join is a computationally expensive operator. Efficient implementation of the join operator is, thus, desirable. The work presented in this document attempts to improve the performance of spatial join queries by distributing the data set across several nodes of a cluster and executing queries across these nodes in parallel. This document discusses a new parallel algorithm that implements the spatial join in an efficient manner. This algorithm is compared to an existing parallel spatial-join algorithm, the clone join. Both algorithms have been implemented on a Beowulf cluster and compared using real datasets. An extensive experimental analysis reveals that the proposed algorithm exhibits superior performance both in declustering time as well as in the execution time of the join query

Scheduling policies for disks and disk arrays

Recent rapid advances of magnetic recording technology have enabled substantial increases in disk capacity. There has been less than 10% improvement annually in the random access time to small data blocks on the disk. Such accesses are very common in OLTP applications, which tend to have stringent response time requirements. Scheduling of disk requests is intended to improve their response time, reduce disk service time, and increase disk access bandwidth with respect to the default FCFS scheduling policy. Shortest Access Time First policy has been shown to outperform other classical disk scheduling policies in numerous studies. Before verifying this conclusion, this dissertation develops an empirical analysis of the SATF policy, and produces a valuable by-product, expressed as x[m] = mp, during the study. Classical scheduling policies and some well-known variations of the SATE policy are re-evaluated, and three extensions are proposed. The performance evaluation uses self-developed simulators containing detailed disk information. The simulators, driven with both synthetic and trace workloads, report the measurements of requests, such as the mean and the 95th percentile of the response times, as well as the measurements of the system, such as the maximum throughput. A comprehensive arrangement of routing and scheduling schemes is presented or mirrored disk systems, or RAIDi. The performance evaluation is based on a twodimensional configuration classification: independent queues (i.e. a router sends the requests to one of the disks as soon as these requests arrive) versus a shared queue (i.e. the requests are held in a common queue at the router and are scheduled to be served); normal data layout versus transposed data layout (i.e. the data stored on the inner cylinders of one disk is duplicated on the outer cylinders of the mirrored disk). The availability of a non-volatile storage or NVS, which allows the processing of write requests to be deferred, is also investigated. Finally, various strategies of mirrored disk declustering are compared against the basic disk mirroring. Their competence of load balancing and their reliability are examined in both normal mode and degraded mode

Selective replicated declustering for arbitrary queries

Data declustering is used to minimize query response times in data intensive applications. In this technique, query retrieval process is parallelized by distributing the data among several disks and it is useful in applications such as geographic information systems that access huge amounts of data. Declustering with replication is an extension of declustering with possible data replicas in the system. Many replicated declustering schemes have been proposed. Most of these schemes generate two or more copies of all data items. However, some applications have very large data sizes and even having two copies of all data items may not be feasible. In such systems selective replication is a necessity. Furthermore, existing replication schemes are not designed to utilize query distribution information if such information is available. In this study we propose a replicated declustering scheme that decides both on the data items to be replicated and the assignment of all data items to disks when there is limited replication capacity. We make use of available query information in order to decide replication and partitioning of the data and try to optimize aggregate parallel response time. We propose and implement a Fiduccia-Mattheyses-like iterative improvement algorithm to obtain a two-way replicated declustering and use this algorithm in a recursive framework to generate a multi-way replicated declustering. Experiments conducted with arbitrary queries on real datasets show that, especially for low replication constraints, the proposed scheme yields better performance results compared to existing replicated declustering schemes. © 2009 Springer

Utilizing query logs for data replication and placement in big data applications

Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2012.Thesis (Ph. D.) -- Bilkent University, 2012.Includes bibliographical refences.The growth in the amount of data in todays computing problems and the level of parallelism dictated by the large-scale computing economics necessitates highlevel parallelism for many applications. This parallelism is generally achieved via data-parallel solutions that require effective data clustering (partitioning) or declustering schemes (depending on the application requirements). In addition to data partitioning/declustering, data replication, which is used for data availability and increased performance, has also become an inherent feature of many applications. The data partitioning/declustering and data replication problems are generally addressed separately. This thesis is centered around the idea of performing data replication and data partitioning/declustering simultenously to obtain replicated data distributions that yield better parallelism. To this end, we utilize query-logs to propose replicated data distribution solutions and extend the well known Fiduccia-Mattheyses (FM) iterative improvement algorithm so that it can be used to generate replicated partitioning/declustering of data. For the replicated declustering problem, we propose a novel replicated declustering scheme that utilizes query logs to improve the performance of a parallel database system. We also extend our replicated declustering scheme and propose a novel replicated re-declustering scheme such that in the face of drastic query pattern changes or server additions/removals from the parallel database system, new declustering solutions that require low migration overheads can be computed. For the replicated partitioning problem, we show how to utilize an effective single-phase replicated partitioning solution in two well-known applications (keyword-based search and Twitter). For these applications, we provide the algorithmic solutions we had to devise for solving the problems that replication brings, the engineering decisions we made so as to obtain the greatest benefits from the proposed data distribution, and the implementation details for realistic systems. Obtained results indicate that utilizing query-logs and performing replication and partitioning/declustering in a single phase improves parallel performance.Türk, AtaPh.D

Alpha Entanglement Codes: Practical Erasure Codes to Archive Data in Unreliable Environments

Data centres that use consumer-grade disks drives and distributed peer-to-peer systems are unreliable environments to archive data without enough redundancy. Most redundancy schemes are not completely effective for providing high availability, durability and integrity in the long-term. We propose alpha entanglement codes, a mechanism that creates a virtual layer of highly interconnected storage devices to propagate redundant information across a large scale storage system. Our motivation is to design flexible and practical erasure codes with high fault-tolerance to improve data durability and availability even in catastrophic scenarios. By flexible and practical, we mean code settings that can be adapted to future requirements and practical implementations with reasonable trade-offs between security, resource usage and performance. The codes have three parameters. Alpha increases storage overhead linearly but increases the possible paths to recover data exponentially. Two other parameters increase fault-tolerance even further without the need of additional storage. As a result, an entangled storage system can provide high availability, durability and offer additional integrity: it is more difficult to modify data undetectably. We evaluate how several redundancy schemes perform in unreliable environments and show that alpha entanglement codes are flexible and practical codes. Remarkably, they excel at code locality, hence, they reduce repair costs and become less dependent on storage locations with poor availability. Our solution outperforms Reed-Solomon codes in many disaster recovery scenarios.Comment: The publication has 12 pages and 13 figures. This work was partially supported by Swiss National Science Foundation SNSF Doc.Mobility 162014, 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN

A Survey on Array Storage, Query Languages, and Systems

Since scientific investigation is one of the most important providers of massive amounts of ordered data, there is a renewed interest in array data processing in the context of Big Data. To the best of our knowledge, a unified resource that summarizes and analyzes array processing research over its long existence is currently missing. In this survey, we provide a guide for past, present, and future research in array processing. The survey is organized along three main topics. Array storage discusses all the aspects related to array partitioning into chunks. The identification of a reduced set of array operators to form the foundation for an array query language is analyzed across multiple such proposals. Lastly, we survey real systems for array processing. The result is a thorough survey on array data storage and processing that should be consulted by anyone interested in this research topic, independent of experience level. The survey is not complete though. We greatly appreciate pointers towards any work we might have forgotten to mention.Comment: 44 page

Redundancy schemes for high availability computer clusters

The primary goal of computer clusters is to improve computing performances by taking advantage of the parallelism they intrinsically provide. Moreover, their use of redundant hardware components enables them to offer high availability services. In this paper, we present an analytical model for analyzing redundancy schemes and their impact on the cluster’s overall performance. Furthermore, several cluster redundancy techniques are analyzed with an emphasis on hardware and data redundancy, from which we derive an applicable redundancy scheme design. Also, our solution provides a disaster recovery mechanism that improves the cluster’s availability. In the case of data redundancy, we present improvements to the replication and parity data replication techniques for which we investigate the availability of the cluster under several scenarios that take into account, among other things, the number of replicated nodes, the number of CPUs that hold parity data and the relation between primary and replicated data. For this purpose, we developed a simulator that analyzes the impact of a redundancy scheme on the processing rate of the cluster. We also studied the performance of two well-known schemes according to the usage rate of the CPUs. We found that two important aspects influencing the performance of a transaction-oriented cluster were the cluster’s failover and data redundancy schemes. We simulated several data redundancy schemes and found that data replication offered higher cluster availability than the parity model