In this paper we present a simple and efficient method, called ARIES ( Algorithm for Recouery and Isolation Exploiting Semantics), which supports partial rollbacks of transactions, finegranularity (e.g., record) locking and recovery using write-ahead logging (WAL). We introduce the paradigm of repeating history to redo all missing updates before performing the rollbacks of the loser transactions during restart after a system failure. ARIES uses a log sequence number in each page to correlate the state of a page with respect to logged updates of that page. All updates of a transaction are logged, including those performed during rollbacks. By appropriate chaining of the log records written during rollbacks to those written during forward progress, a bounded amount of logging is ensured during rollbacks even in the face of repeated failures during restart or of nested rollbacks We deal with a variety of features that are very Important in building and operating an industrial-strength transaction processing system ARIES supports fuzzy checkpoints, selective and deferred restart, fuzzy image copies, media recovery, and high concurrency lock modes (e. g., increment /decrement) which exploit the semantics of the operations and require the ability to perform operation logging. ARIES is flexible with respect to the kinds of buffer management policies that can be implemented. It supports objects of

This paper introduces a new approach to database disk buffering, called the LRU--K method. The basic idea of LRU--K is to keep track of the times of the last K references to popular database pages, using this information to statistically estimate the interarrival time of such references on a page by page basis. Although the LRU--K approach performs optimal statistical inference under relatively standard assumptions, it is fairly simple and incurs little bookkeeping overhead. As we demonstrate with simulation experiments, the LRU--K algorithm surpasses conventional buffering algorithms in discriminating between frequently and infrequently referenced pages. In fact, LRU--K can approach the behavior of buffering algorithms in which page sets with known access frequencies are manually assigned to different buffer pools of specifically tuned sizes. Unlike such customized buffering algorithms however, the LRU--K method is self--tuning, in the sense that it does not rely on external hints abo...

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.

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In addition to maintaining database consistency as in conventional databases, real-time database systems must also handle transactions with timing constraints. While transaction response time and throughput are usually used to measure a conventional database system, the percentage of transactions satisfying the deadlines or a time-critical value function is often used to evaluate a real-time database system. Scheduling real-time transactions is far more complex than traditional real-time scheduling in the sense that (1) worst-case execution times are typically hard to estimate, since not only CPU but also I/O requirement isinvolved � and (2) certain aspects of concurrency control may not integrate well with real-time scheduling. In this paper, we rst develop a taxonomy of the underlying design space of concurrency control including the various techniques for achieving serializability and improving performance. This taxonomy provides us with a foundation for addressing the real-time issues. We then consider the integration of concurrency control with realtime requirements. The implications of using run policies to better utilize real-time scheduling in a database environment are examined. Finally, as timing constraints may be more important than data consistency in certain hard real-time database applications, we also discuss several approaches that explore the non-serializable semantics of real-time transactions to meet the hard deadlines. Index terms: concurrency control, real-time databases, real-time scheduling, real-time transactions, serializability, schedulability.

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...

In this paper we propose and evaluate an approach to DBMS memory management that addresses multiclass workloads with per-class response time goals. It operates by monitoring perclass database reference frequencies as well as the state of the system relative to the goals of each class; the information that it gathers is used to help existing memory allocation and page replacement mechanisms avoid making decisions that may jcopardize performance goals. 1

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We define the magic-sets transformation for traditional relational systems (with duplicates, aggregation and grouping), as well as for relational systems extended with recursion. We compare the magic-sets rewriting to traditional optimization techniques for nonrecursive queries, and use performance experiments to argue that the magic-sets transformation is often a better optimization technique. 1 Introduction "Magic-sets" is the name of a query transformation algorithm ([BMSU86]) (and now a class of algorithms Part of this work was done at the IBM Almaden Research Center. Work at Stanford was supported by an NSF grant IRI87 -22886, an Air Force grant AFOSR-88-0266, and a grant of IBM Corporation. y Author's current affiliation: Tandem Computers. z Part of this work was done while the author was visiting IBM Almaden Research Center. Work at Wisconsin was supported by an IBM Faculty Development Award and an NSF grant IRI-8804319. --- Generalized Magic-sets of [BR87], Magic Tem...