OceanStore is an Internet-scale, persistent data store designed for incremental scalability, secure sharing, and long-term durability. Pond is the OceanStore prototype; it contains many of the features of a complete system including location-independent routing, Byzantine update commitment, push-based update of cached copies through an overlay multicast network, and continuous archiving to erasure-coded form. In the wide area, Pond outperforms NFS by up to a factor of 4.6 on readintensive phases of the Andrew benchmark, but underperforms NFS by as much as a factor of 7.3 on writeintensive phases. Microbenchmarks show that write performance is limited by the speed of erasure coding and threshold signature generation, two important areas of future research. Further microbenchmarks show that Pond manages replica consistency in a bandwidthefficient manner and quantify the latency cost imposed by this bandwidth savings.

Recent years have witnessed the rapid evolution of commercially available mobile computing environments. This has given rise to the presence of several viable, but noninteroperable wireless networking technologies - each targeting a niche mobility environment and providing a distinct quality of service. The lack of a uniform set of standards, the heterogeneity in the quality of service, and the diversity in the networking approaches makes it difficult for a mobile computing environment to provide seamless mobility across different wireless networks. Besides, inter-network mobility will typically be accompanied by a change in the quality of service. The application and the environment need to collaboratively adapt their communication and data management strategies in order to gracefully react to the dynamic operating conditions. This paper presents the important challenges in building a mobile computing environment which provides seamless mobility and adaptive computing over commerciall...

Our goal is to define a communication architecture for nomadic applications. We notice

Mobile devices are becoming a primary medium for personal information gathering, management, and sharing. Managing personal image data on mobile platforms is a difficult problem due to large data set size, content diversity, heterogeneous individual usage patterns, and resource constraints. This article presents a user-centric system, called iScope, for personal image management and sharing on mobile devices. iScope uses multi-modality clustering of both content and context information for efficient image management and search, and online learning techniques for predicting images of interest. It also supports distributed content-based search among networked devices while maintaining the same intuitive interface, enabling efficient information sharing among people. We have implemented iScope and conducted in-field experiments using networked Nokia N810 portable Internet tablets. Energy efficiency was a primary design focus during the design and implementation of the iScope search algorithms. Experimental results indicate that iScope improves search time and search energy by 4.1 × and 3.8 × on average, relative to browsing.

Abstract not found