Abstract. Many emerging on-line data analysis applications require applying continuous query operations such as correlation, aggregation, and filtering to data streams in real-time. Distributed stream processing systems allow in-network stream processing to achieve better scalability and quality-of-service (QoS) provision. In this paper we present Synergy, a distributed stream processing middleware that provides sharing-aware component composition. Synergy enables efficient reuse of both data streams and processing components, while composing distributed stream processing applications with QoS demands. Synergy provides a set of fully distributed algorithms to discover and evaluate the reusability of available data streams and processing components when instantiating new stream applications. For QoS provision, Synergy performs QoS impact projection to examine whether the shared processing can cause QoS violations on currently running applications. We have implemented a prototype of the Synergy middleware and evaluated its performance on both PlanetLab and simulation testbeds. The experimental results show that Synergy can achieve much better resource utilization and QoS provision than previously proposed schemes, by judiciously sharing streams and processing components during application composition.
Modern mobile devices can form ad-hoc networks to autonomously share data and services. While such self-organizing, peer-to-peer communities offer exciting collaboration opportunities, deciding whether to trust another peer can be challenging. In this work we propose a decentralized trust management middleware for ad-hoc, peer-to-peer networks, based on reputation. Our middleware's protocols take advantage of the unstructured nature of the network to render malicious behavior, such as lying and colluding, risky. The reputation information of each peer is stored in its neighbors and piggy-backed on its replies. By simulating the behavior of networks both with and without a rating scheme we were able to show that just a few dishonest peers can flood the network with false results, whereas this phenomenon is virtually eliminated when using our middleware.
In this paper we propose adaptive content-driven routing and data dissemination algorithms for intelligently routing search queries in a peer-to-peer network that supports mobile users. In our mechanism nodes build content synopses of their data and adaptively disseminate them to the most appropriate nodes. Based on the content synopses, a routing mechanism is being built to forward the queries to those nodes that have a high probability of providing the desired results. Our simulation results show that our approach is highly scalable and significantly improves resources usage by saving both bandwidth and processing power.
-Efficient routing protocols can provide significant benefits to mobile ad hoc networks, in terms of both performance and reliability. Many routing protocols for such networks have been proposed so far. Amongst the most popular ones are Dynamic Source Routing (DSR), Ad hoc On-demand Distance Vector (AODV), TemporallyOrdered Routing Algorithm (TORA) and Location-Aided Routing (LAR). Despite the popularity of those protocols, research efforts have not focused in evaluating their performance when applied to large-scale wireless networks. Such networks are comprised of hundreds of nodes, connected via long routes. This greatly affects the network efficiency, since it necessitates frequent exchange of routing information. In this paper we present our observations regarding the behavior of the above protocols, in large-scale mobile ad hoc networks (MANETs). We consider wireless mobile terminals spread over a large geographical area, and we perform extensive simulations, using the QualNet and NS-2 simulators. The results of the simulations yield some interesting conclusions: AODV suffers in terms of packet delivery fraction (PDF) but scales very well in terms of end-to-end delay. DSR on the other hand scales well in terms of packet delivery fraction but suffers an important increase of end-to-end delay, as compared to its performance achieved in smallscale topologies. Also, the effect of maximum connections is severe on TORA, which seems unable to route large amounts of traffic. LAR, seems to scale very well, in terms of all metrics employed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.