Database management systems will continue to manage large data volumes. Thus, efficient algorithms for accessing and manipulating large sets and sequences will be required to provide acceptable performance. The advent of object-oriented and extensible database systems will not solve this problem. On the contrary, modern data models exacerbate it: In order to manipulate large sets of complex objects as efficiently as today’s database systems manipulate simple records, query processing algorithms and software will become more complex, and a solid understanding of algorithm and architectural issues is essential for the designer of database management software. This survey provides a foundation for the design and implementation of query execution facilities in new database management systems. It describes a wide array of practical query evaluation techniques for both relational and post-relational database systems, including iterative execution of complex query evaluation plans, the duality of sort- and hash-based set matching algorithms, types of parallel query execution and their implementation, and special operators for emerging database application domains.

In a path-breaking paper last year Pat and Betty O'Neil and Gerhard Weikum proposed a self-tuning improvement to the Least Recently Used (LRU) buffer management algorithm[15]. Their improvement is called LRU/k and advocates giving priority to buffer pages based on the kth most recent access. (The standard LRU algorithm is denoted LRU/1 according to this terminology.) If P1's kth most recent access is more more recent than P2's, then P1 will be replaced after P2. Intuitively, LRU/k for k ? 1 is a good strategy, because it gives low priority to pages that have been scanned or to pages that belong to a big randomly accessed file (e.g., the account file in TPC/A). They found that LRU/2 achieves most of the advantage of their method. The one problem of LRU/2 is the processor Supported by U.S. Office of Naval Research #N00014-91-J1472 and #N00014-92-J-1719, U.S. National Science Foundation grants #CCR-9103953 and IRI-9224601, and USRA #5555-19. Part of this work was performed while Theodo...

Modern high-end disk arrays often have several gigabytes of cache RAM. Unfortunately, most array caches use management policies which duplicate the same data blocks at both the client and array levels of the cache hierarchy: they are inclusive. Thus, the aggregate cache behaves as if it was only as big as the larger of the client and array caches, instead of as large as the sum of the two. Inclusiveness is wasteful: cache RAM is expensive. We explore the benefits of a simple scheme to achieve exclusive caching, in which a data block is cached at either a client or the disk array, but not both. Exclusiveness helps to create the effect of a single, large unified cache. We introduce a DEMOTE operation to transfer data ejected from the client to the array, and explore its effectiveness with simulation studies. We quantify the benefits and overheads of demotions across both synthetic and real-life workloads. The results show that we can obtain useful -- sometimes substantial -- speedups. During our investigations, we also developed some new cache-insertion algorithms that show promise for multi-client systems, and report on some of their properties.

Although LRU replacement policy has been commonly used in the buffer cache management, it is well known for its inability to cope with access patterns with weak locality. Previous work, such as LRU-K and 2Q, attempts to enhance LRU capacity by making use of additional history information of previous block references other than only the recency information used in LRU. These algorithms greatly increase complexity and/or can not consistently provide performance improvement. Many recently proposed policies, such as UBM and SEQ, improve replacement performance by exploiting access regularities in references. They only address LRU problems on certain specific and well-defined cases such as access patterns like sequences and loops. Motivated by the limits of previous studies, we propose an efficient buffer cache replacement policy, called Low Interreference Recency Set (LIRS). LIRS effectively addresses the limits of LRU by using recency to evaluate Inter-Reference Recency (IRR) for making a replacement decision. This is in contrast to what LRU does: directly using recency to predict next reference timing. At the same time, LIRS almost retains the same simple assumption of LRU to predict future access behavior of blocks. Our objectives are to effectively address the limits of LRU for a general purpose, to retain the low overhead merit of LRU, and to outperform those replacement policies relying on the access regularity detections. Conducting simulations with a variety of traces and a wide range of cache sizes, we show that LIRS significantly outperforms LRU, and outperforms other existing replacement algorithms in most cases. Furthermore, we show that the additional cost for implementing LIRS is trivial in comparison with LRU.

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In an on-demand video server environment, multimedia objects (e.g. movies) are very large and are read sequentially. Hence it is not economical to cache the entire object. However, caching random fractions of a multimedia object is not beneficial. This is due to the stringent response time requirements where continuous availability of a stream has to be guaranteed; whereas caching random fractions will result in unpredictable load on the disks. Therefore, traditional buffer management policies such as LRU are not effective. In addition, the sequential access implies pages brought in by a stream can be reused by a closely following stream and subsequently discarded, thus buffering only a fraction of the entire object. In this paper, we propose a buffer management policy called the interval caching policy based on the above idea that identifies certain streams and temporarily buffers the pages brought in by those streams. We study the efficacy of this technique for reducing disk overload...

The hot-set model, characterizing the buffer requirements of relational queries, is presented. This model allows the system to determine the optimal buffer space to be allocated to a query; it can also be used by the query optimizer to derive efficient execution plans accounting for the available buffer space, and by a query scheduler to prevent thrashing. The hot-set model is compared with the working-set model. A simulation study is presented. Categories and Subject Descriptors: H.2.4 [Database Management]: Systems-query processing

Multimedia applications demand specific support from database management systems due to the characteristics of multimedia data and their interactive usage. This includes integrated support for high-volume and time-dependent (continuous) data types like audio and video. One critical issue is to provide handling of continuous data streams including buffer management as needed for multimedia presentations. Buffer management strategies for continuous data have to consider specific requirements like providing for continuity of presentations, for immediate continuation of presentations after frequent user interactions by appropriate buffer resource consumption. Existing buffer management strategies do not sufficiently support the handling of continuous data streams in highly interactive multimedia presentations. In this paper we present the “least/most relevant for presentation ” (LJMRP) buffer management strategy whichconsiderspresentationspecificinformationin order to provide an optimized behavior with respect to the requirements mentioned above. WMRP is a framework to formulate specific interaction models and is therefore adaptable to individual multimedia applications. We present a simulation study showing that an instantiated L/MRP outperforms existing approachesforgiventypesofinteractivemultimedia applications. It is shown that UMRP is especially suitable to support highly interactive multimedia presentations.

We propose an I/O classification architecture to close the widening semantic gap between computer systems and storage systems. By classifying I/O, a computer system can request that different classes of data be handled with different storage system policies. Specifically, when a storage system is first initialized, we assign performance policies to predefined classes, such as the filesystem journal. Then, online, we include a classifier with each I/O command (e.g., SCSI), thereby allowing the storage system to enforce the associated policy for each I/O that it receives. Our immediate application is caching. We present filesystem prototypes and a database proof-of-concept that classify all disk I/O — with very little modification to the filesystem, database, and operating system. We associate caching policies with various classes (e.g., large files shall be evicted before metadata and small files), and we show that end-to-end file system performance can be improved by over a factor of two, relative to conventional caches like LRU. And caching is simply one of many possible applications. As part of our ongoing work, we are exploring other classes, policies and storage system mechanisms that can be used to improve end-to-end performance, reliability and security.

The problem of buffer management in database management systems is concerned with the efficient main memory allocation and management for answering database queries. Previous works on buffer allocation are based either exclusively on the availability of buffers at runtime or on the access patterns of queries. In this paper, we first propose a unified approach for buffer allocation in which both of these considerations are taken into account. Our approach is based on the notion of marginal gains which specify the expected reduction in page faults by allocating extra buffers to a query. Then, we extend this approach to support adaptable buffer allocation. An adaptable buffer allocation algorithm automatically optimizes itself for the specific query workload. To achieve this adaptability, we propose using run-time information, such as the load of the system, in buffer allocation decisions. Our approach is to use a simple queueing model to predict whether a buffer allocation will improve ...