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

Rebuild performance enhancement using onboard caching and delayed vacation termination in clustered raid 5

The Clustered Raid 5 (CRAID5) architecture with a parity group size(G) smaller than the number of disks(N) increases the load by the declustering ratio denoted by α = (G -1)/(N -1), which can be lesser than that in Raid 5 while switching to, and subsequently operating in rebuild mode. The Nearly Random Permutation (NRP) layout provides the flexibility to vary the declustering ratio (α) for a given N, and the Vacationing Server Model (VSM) of processing the rebuild requests provides acceptable rebuild and user response times. The rebuild performance and the user response time can be improved by introducing an onboard buffer in the disks, which caches a single track upon arrival of a rebuild request while in rebuild mode. Such an enhancement is proposed, and the architecture is described along with an analysis using the DASim simulation toolkit developed at NJIT. Also proposed is the delayed termination of vacations with two user requests as this improves the rebuild performance with a negligible negative impact on user response time. Finally, the effect of limiting the rebuild buffer on the rebuild performance is presented in the context of three different disk utilizations and declustering ratios

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

Scalability analysis of declustering methods for multidimensional range queries

Abstract—Efficient storage and retrieval of multiattribute data sets has become one of the essential requirements for many data-intensive applications. The Cartesian product file has been known as an effective multiattribute file structure for partial-match and best-match queries. Several heuristic methods have been developed to decluster Cartesian product files across multiple disks to obtain high performance for disk accesses. Although the scalability of the declustering methods becomes increasingly important for systems equipped with a large number of disks, no analytic studies have been done so far. In this paper, we derive formulas describing the scalability of two popular declustering methods¦Disk Modulo and Fieldwise Xor¦for range queries, which are the most common type of queries. These formulas disclose the limited scalability of the declustering methods, and this is corroborated by extensive simulation experiments. From the practical point of view, the formulas given in this paper provide a simple measure that can be used to predict the response time of a given range query and to guide the selection of a declustering method under various conditions

Data allocation in disk arrays with multiple raid levels

There has been an explosion in the amount of generated data, which has to be stored reliably because it is not easily reproducible. Some datasets require frequent read and write access. like online transaction processing applications. Others just need to be stored safely and read once in a while, as in data mining. This different access requirements can be solved by using the RAID (redundant array of inexpensive disks) paradigm. i.e., RAIDi for the first situation and RAID5 for the second situation. Furthermore rather than providing two disk arrays with RAID 1 and RAID5 capabilities, a controller can be postulated to emulate both. It is referred as a heterogeneous disk array (HDA). Dedicating a subset of disks to RAID 1 results in poor disk utilization, since RAIDi vs RAID5 capacity and bandwidth requirements are not known a priori. Balancing disk loads when disk space is shared among allocation requests, referred to as virtual arrays - VAs poses a difficult problem. RAIDi disk arrays have a higher access rate per gigabyte than RAID5 disk arrays. Allocating more VAs while keeping disk utilizations balanced and within acceptable bounds is the goal of this study. Given its size and access rate a VA\u27s width or the number of its Virtual Disks -VDs is determined. VDs allocations on physical disks using vector-packing heuristics, with disk capacity and bandwidth as the two dimensions are shown to be the best. An allocation is acceptable if it does riot exceed the disk capacity and overload disks even in the presence of disk failures. When disk bandwidth rather than capacity is the bottleneck, the clustered RAID paradigm is applied, which offers a tradeoff between disk space and bandwidth. Another scenario is also considered where the RAID level is determined by a classification algorithm utilizing the access characteristics of the VA, i.e., fractions of small versus large access and the fraction of write versus read accesses. The effect of RAID 1 organization on its reliability and performance is studied too. The effect of disk failures on the X-code two disk failure tolerant array is analyzed and it is shown that the load across disks is highly unbalanced unless in an NxN array groups of N stripes are randomly rotated

Data partitioning and load balancing in parallel disk systems

Parallel disk systems provide opportunities for exploiting I/O parallelism in two possible ways, namely via inter-request and intra-request parallelism. In this paper we discuss the main issues in performance tuning of such systems, namely striping and load balancing, and show their relationship to response time and throughput. We outline the main components of an intelligent file system that optimizes striping by taking into account the requirements of the applications, and performs load balancing by judicious file allocation and dynamic redistributions of the data when access patterns change. Our system uses simple but effective heuristics that incur only little overhead. We present performance experiments based on synthetic workloads and real-life traces

Options in Scan Processing for Shared-Disk Parallel Database Systems

Shared-disk database systems offer a high degree of freedom in the allocation of workload compared to shared-nothing architectures. This creates a great potential for load balancing but also introduces additional complexity into the process of query scheduling. This report surveys the problems and opportunities faced in scan processing in a shared-disk environment. We list the parameters to tune and the decisions to make, as well as some known solutions and commonsense considerations, in order to identify the most promising areas of future research

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

Partial Replica Location And Selection For Spatial Datasets

As the size of scientific datasets continues to grow, we will not be able to store enormous datasets on a single grid node, but must distribute them across many grid nodes. The implementation of partial or incomplete replicas, which represent only a subset of a larger dataset, has been an active topic of research. Partial Spatial Replicas extend this functionality to spatial data, allowing us to distribute a spatial dataset in pieces over several locations. We investigate solutions to the partial spatial replica selection problems. First, we describe and develop two designs for an Spatial Replica Location Service (SRLS), which must return the set of replicas that intersect with a query region. Integrating a relational database, a spatial data structure and grid computing software, we build a scalable solution that works well even for several million replicas. In our SRLS, we have improved performance by designing a R-tree structure in the backend database, and by aggregating several queries into one larger query, which reduces overhead. We also use the Morton Space-filling Curve during R-tree construction, which improves spatial locality. In addition, we describe R-tree Prefetching(RTP), which effectively utilizes the modern multi-processor architecture. Second, we present and implement a fast replica selection algorithm in which a set of partial replicas is chosen from a set of candidates so that retrieval performance is maximized. Using an R-tree based heuristic algorithm, we achieve O(n log n) complexity for this NP-complete problem. We describe a model for disk access performance that takes filesystem prefetching into account and is sufficiently accurate for spatial replica selection. Making a few simplifying assumptions, we present a fast replica selection algorithm for partial spatial replicas. The algorithm uses a greedy approach that attempts to maximize performance by choosing a collection of replica subsets that allow fast data retrieval by a client machine. Experiments show that the performance of the solution found by our algorithm is on average always at least 91% and 93.4% of the performance of the optimal solution in 4-node and 8-node tests respectively