Efficient Processing of Continuous Join Queries using Distributed Hash Tables

International audienceThis paper addresses the problem of computing approximate answers to continuous join queries. We present a new method, called DHTJoin, which combines hash-based placement of tuples in a Distributed Hash Table (DHT) and dissemination of queries using a gossip style protocol. We provide a performance evaluation of DHTJoin which shows that DHTJoin can achieve significant performance gains in terms of network traffic

Statistical structures for internet-scale data management

Efficient query processing in traditional database management systems relies on statistics on base data. For centralized systems, there is a rich body of research results on such statistics, from simple aggregates to more elaborate synopses such as sketches and histograms. For Internet-scale distributed systems, on the other hand, statistics management still poses major challenges. With the work in this paper we aim to endow peer-to-peer data management over structured overlays with the power associated with such statistical information, with emphasis on meeting the scalability challenge. To this end, we first contribute efficient, accurate, and decentralized algorithms that can compute key aggregates such as Count, CountDistinct, Sum, and Average. We show how to construct several types of histograms, such as simple Equi-Width, Average-Shifted Equi-Width, and Equi-Depth histograms. We present a full-fledged open-source implementation of these tools for distributed statistical synopses, and report on a comprehensive experimental performance evaluation, evaluating our contributions in terms of efficiency, accuracy, and scalability

Small-world networks, distributed hash tables and the e-resource discovery problem

Resource discovery is one of the most important underpinning problems behind producing a scalable, robust and efficient global infrastructure for e-Science. A number of approaches to the resource discovery and management problem have been made in various computational grid environments and prototypes over the last decade. Computational resources and services in modern grid and cloud environments can be modelled as an overlay network superposed on the physical network structure of the Internet and World Wide Web. We discuss some of the main approaches to resource discovery in the context of the general properties of such an overlay network. We present some performance data and predicted properties based on algorithmic approaches such as distributed hash table resource discovery and management. We describe a prototype system and use its model to explore some of the known key graph aspects of the global resource overlay network - including small-world and scale-free properties

Wireless mobile ad-hoc sensor networks for very large scale cattle monitoring

This paper investigates the use of wireless mobile ad hoc sensor networks in the nationwide cattle monitoring systems. This problem is essential for monitoring general animal health and detecting outbreaks of animal diseases that can be a serious threat for the national cattle industry and human health. We begin by describing a number of related approaches for supporting animal monitoring applications and identify a comprehensive set of requirements that guides our approach. We then propose a novel infrastructure-less, self -organized peer to peer architecture that fulfills these requirements. The core of our work is the novel data storage and routing protocol for large scale, highly mobile ad hoc sensor networks that is based on the Distributed Hash Table (DHT) substrate that we optimize for disconnections. We show over a range of extensive simulations that by exploiting nodes’ mobility, packet overhearing and proactive caching we significantly improve availability of sensor data in these extreme conditions

DHTJoin: Processing Continuous Join Queries Using DHT Networks

International audienceContinuous query processing in data stream management systems (DSMS) has received considerable attention recently. Many applications share the same need for processing data streams in a continuous fashion. For most distributed streaming applications, the centralized processing of continuous queries over distributed data is simply not viable. This paper addresses the problem of computing approximate answers to continuous join queries over distributed data streams. We present a new method, called DHTJoin, which combines hash-based placement of tuples in a Distributed Hash Table (DHT) and dissemination of queries by exploiting the embedded trees in the underlying DHT, thereby incuring little overhead. DHTJoin also deals with join attribute value skew which may hurt load balancing and result completeness. We provide a performance evaluation of DHTJoin which shows that it can achieve significant performance gains in terms of network traffic