Swarup Acharya

Balancing push and pull for data broadcast

The increasing ability to interconnect computers through internet-working, wireless networks, high-bandwidth satellite, and cable networks has spawned a new class of information-centered applications based on data dissemination. These applications employ broadcast to deliver data to very large client populations. We have proposed the Broadcast Disks paradigm [Zdon94, Acha95b] for organizing the contents of a data broadcast program and for managing client resources in response to such a program. Our previous work on Broadcast Disks focused exclusively on the “push-based” approach, where data is sent out on the broadcast channel according to a periodic schedule, in anticipation of client requests. In this paper, we study how to augment the push-only model with a “pull-based” approach of using a backchannel to allow clients to send explicit requests for data to the server. We analyze the scalability and performance of a broadcast-based system that integrates push and pull and study the impact of this integration on both the steady state and warm-up performance of clients. Our results show that a client backchannel can provide significant performance improvement in the broadcast environment, but that unconstrained use of the backchannel can result in scalability problems due to server saturation. We propose and investigate a set of three techniques that can delay the onset of saturation and thus, enhance the performance and scalability of the system.

Join synopses for approximate query answering

In large data warehousing environments, it is often advantageous to provide fast, approximate answers to complex aggregate queries based on statistical summaries of the full data. In this paper, we demonstrate the difficulty of providing good approximate answers for join-queries using only statistics (in particular, samples) from the base relations. We propose join synopses as an effective solution for this problem and show how precomputing just one join synopsis for each relation suffices to significantly improve the quality of approximate answers for arbitrary queries with foreign key joins. We present optimal strategies for allocating the available space among the various join synopses when the query work load is known and identify heuristics for the common case when the work load is not known. We also present efficient algorithms for incrementally maintaining join synopses in the presence of updates to the base relations. Our extensive set of experiments on the TPC-D benchmark database show the effectiveness of join synopses and various other techniques proposed in this paper.

Dissemination-based data delivery using broadcast disks

Mobile computers and wireless networks are emerging technologies which promise to make ubiquitous computing a reality. One challenge that must be met in order to truly realize this potential is that of providing mobile clients with ubiquitous access to data. One way (and perhaps the only way) to address these challenges is to provide stationary server machines with a relatively high-bandwidth channel over which to broadcast data to a client population in anticipation of the need for that data by the clients. Such a system can be said to be asymmetric due to the disparity in the transmission capacities of clients and servers. We have proposed a mechanism called broadcast disks to provide database access in this environment as well as in other asymmetric systems such as cable and direct broadcast satellite television networks and information distribution services. The broadcast disk approach enables the creation of an arbitrarily fine-grained memory hierarchy on the broadcast medium. This hierarchy, combined with the inversion of the traditional relationship between clients and servers that occurs in a broadcast-based system, raises fundamental new issues for client cache management and data prefetching. In this article we present a brief overview of asymmetric environments and describe our approaches to broadcast disk organization, client cache management, and prefetching.

Scheduling on-demand broadcasts: new metrics and algorithms

This paper examines the scheduling problems that occur in on-demand broadcast environments. The authors describe an algorithm named MAX which is capable of finding the right balance between the individual and global performance.

Selectivity estimation in spatial databases

Selectivity estimation of queries is an important and well-studied problem in relational database systems. In this paper, we examine selectivity estimation in the context of Geographic Information Systems, which manage spatial data such as points, lines, poly-lines and polygons. In particular, we focus on point and range queries over two-dimensional rectangular data. We propose several techniques based on using spatial indices, histograms, binary space partitionings (BSPs), and the novel notion of spatial skew. Our techniques carefully partition the input rectangles into subsets and approximate each partition accurately. We present a detailed experimental study comparing the proposed techniques and the best known sampling and parametric techniques. We evaluate them using synthetic as well as real-life TIGER datasets. Based on our experiments, we identify a BSP based partitioning that we call Min-Skew which consistently provides the most accurate selectivity estimates for spatial queries. The Min-Skew partitioning can be constructed efficiently, occupies very little space, and provides accurate selectivity estimates over a broad range of spatial queries.

Congressional samples for approximate answering of group-by queries

In large data warehousing environments, it is often advantageous to provide fast, approximate answers to complex decision support queries using precomputed summary statistics, such as samples. Decision support queries routinely segment the data into groups and then aggregate the information in each group (group-by queries). Depending on the data, there can be a wide disparity between the number of data items in each group. As a result, approximate answers based on uniform random samples of the data can result in poor accuracy for groups with very few data items, since such groups will be represented in the sample by very few (often zero) tuples. In this paper, we propose a general class of techniques for obtaining fast, highly-accurate answers for group-by queries. These techniques rely on precomputed non-uniform (biased) samples of the data. In particular, we propose congressional samples, a hybrid union of uniform and biased samples. Given a fixed amount of space, congressional samples seek to maximize the accuracy for all possible group-by queries on a set of columns. We present a one pass algorithm for constructing a congressional sample and use this technique to also incrementally maintain the sample up-to-date without accessing the base relation. We also evaluate query rewriting strategies for providing approximate answers from congressional samples. Finally, we conduct an extensive set of experiments on the TPC-D database, which demonstrates the efficacy of the techniques proposed.

The Aqua approximate query answering system

Aqua is a system for providing fast, approximate answers to aggregate queries, which are very common in OLAP applications. It has been designed to run on top of any commercial relational DBMS. Aqua precomputes synopses (special statistical summaries) of the original data and stores them in the DBMS. It provides approximate answers along with quality guarantees by rewriting the queries to run on these synopses. Finally, Aqua keeps the synopses up-to-date as the database changes, using fast incremental maintenance techniques.

Intelligent file hoarding for mobile computers

Mobile computing adds a new wrinkle to the ageold problem of caching. Today’s wireless links are both slow and expensive, and are not always available to a user. Therefore, when a mobile user is disconnected, a cache miss means (at best) a substantial cost in time and money, or (at worst) a complete halt to work if critical information has not been cached. Existing solutions to this problem rely on some combination of explicit hoard profiles and spying on a user’s file accesses. Neither of these approaches is ideal in terms of reliability or user-friendliness. Our solution might be called transparent analytical spying. Instead of simply recording a list of file accesses, we analyze program executions and tie them to specific files. By observing multiple executions of a program, we are able to build sets of files associated with each program, and can heuristically separate application files from user files. We can then present a high-level view to the user that is similar to loading a briefcase. We have an implementation of our hoarding tool running under OS/2.

Are “Disks in the Air” Just Pie in the Sky?

Mobile computers and wireless networks are emerging technologies which will soon make ubiquitous computing a reality. In the wireless environment, mobile clients may often be disconnected from stationary server machines or may have only a low-bandwidth channel for sending messages to servers. This environment raises new challenges for the support of database applications for three reasons: 1) the limited storage capacities of mobile machines, 2) the inability to accurately predict the future data needs of many data-intensive applications, and 3) the need to provide clients with new or updated data values in order to ensure consistent data access. One way (perhaps the only way) to address these challenges is to provide the stationary server machines with a relatively high bandwidth channel over which to broadcast portions of the database. Wireless networks are but one compelling example of the more general class of asymmetric cornmunication environments, which also includes CATV networks and information distribution services. We have proposed a mechanism called Broadcast Disks to provide database access in asymmetric communication environments. By appropriate organization of the broadcast program, an arbitrarlly fine-grained memory hierarchy can be created. Such a hierarchy raises fundamental new issues for pre-fetching and client cache management. In this short paper we present a brief overview of asymmetric environments, our novel approach to organizing broadcast disks and some initial simulation results.

Hitless network engineering of SONET rings

Shrinking capital expenditure budgets are forcing telecom service providers to explore engineering tools that extract greater utilization from their existing transport infrastructure. In this paper, we explore the challenges in engineering SONET rings to support higher traffic volumes. Unlike network design tools, engineering operations have to reconfigure live traffic and do so in a hitless manner. We present an efficient algorithm called MobiRing that accounts for the unique constraints imposed by the SONET standard. In spite of the hardness of the optimization problems, we show using extensive simulations that the greedy approach of MobiRing performs extremely well. More importantly, MobiRing is able to achieve hitless reconfigurations and therefore, very attractive to use in practice. This ability to do effective, hitless engineering opens the possibility that network engineering, traditionally a very infrequent operation, can now be done proactively to maintain high levels of network utilization.