The Impact of Infostation Density on Vehicular Data Dissemination

Vehicle-to-Vehicle and Vehicle-to-Roadside communications are going to become an indispensable part of the modern day automotive experience. For people on the move, vehicular networks can provide critical network connectivity and access to real-time information. Infostations play a vital role in these networks by acting as gateways to the Internet and by extending network connectivity. In this context, an important question is “What is the minimum number of infostations that need to be deployed in an area in order to support vehicular applications?” Optimizing infostation density is vital to understanding and reducing the cost of deployment and management. In this paper, we examine the required infostation density in a highway scenario using different data dissemination models. We start from a simple analysis that captures the required density under idealized assumptions. These models are validated by an event-driven simulator. We then run detailed QualNet simulations on both controlled and realistic vehicular traces to observe the information density trends in practical environments, and consequently propose techniques to improve dissemination performance and reduce the required infostation density.     

WCL: A co-ordination language for geographically distributed agents

In this paper a tuple space based co-ordination language, and a run-time system which supports it, is described. The co-ordination language is called WCL, and it is designed to support agent co-ordination over the Internet between agents which are geographically distributed. WCL uses tuple spaces as used in Linda. WCL provides a richer set of primitives than traditional tuple space based systems, and provides asynchronous and synchronous tuple space access, bulk tuple primitives, and streaming primitives which, as a whole, provide a complete framework more suited to co-ordination over the Internet compared with the Linda primitives. The primitives emphasise efficiency and location transparency (of data and agents) and this is exploited in the current run-time system used to support WCL. The run-time system described in this paper is distributed and uses location transparency and dynamic analysis of tuple space usage to migrate tuple spaces around the distributed system. Some initial experimental results are given which demonstrate the performance gains of using the tuple space migration. The paper motivates the inclusion of many of the primitives, and demonstrates how a well designed set of primitives provides performance and efficiency. The JavaSpace primitives are used as an example of how the choice of primitives can detrimentally affect the efficiency of the language, and exclude required co-ordination constructs.     

Scribe: a large-scale and decentralized application-level multicast infrastructure

This paper presents Scribe, a scalable application-level multicast infrastructure. Scribe supports large numbers of groups, with a potentially large number of members per group. Scribe is built on top of Pastry, a generic peer-to-peer object location and routing substrate overlayed on the Internet, and leverages Pastry's reliability, self-organization, and locality properties. Pastry is used to create and manage groups and to build efficient multicast trees for the dissemination of messages to each group. Scribe provides best-effort reliability guarantees, and we outline how an application can extend Scribe to provide stronger reliability. Simulation results, based on a realistic network topology model, show that Scribe scales across a wide range of groups and group sizes. Also, it balances the load on the nodes while achieving acceptable delay and link stress when compared with Internet protocol multicast.     

Delay aware querying with Seaweed

Large highly distributed data sets are poorly supported by current query technologies. Applications such as endsystem-based network management are characterized by data stored on large numbers of endsystems, with frequent local updates and relatively infrequent global one-shot queries. The challenges are scale (10³ to 10⁹ endsystems) and endsystem unavailability. In such large systems, a significant fraction of endsystems and their data will be unavailable at any given time. Existing methods to provide high data availability despite endsystem unavailability involve centralizing, redistributing or replicating the data. At large scale these methods are not scalable. We advocate a design that trades query delay for completeness, incrementally returning results as endsystems become available. We also introduce the idea of completeness prediction, which provides the user with explicit feedback about this delay/completeness trade-off. Completeness prediction is based on replication of compact data summaries and availability models. This metadata is orders of magnitude smaller than the data. Seaweed is a scalable query infrastructure supporting incremental results, online in-network aggregation and completeness prediction. It is built on a distributed hash table (DHT) but unlike previous DHT based approaches it does not redistribute data across the network. It exploits the DHT infrastructure for failure-resilient metadata replication, query dissemination, and result aggregation. We analytically compare Seaweed’s scalability against other approaches and also evaluate the Seaweed prototype running on a large-scale network simulator driven by real-world traces.