In this paper we present the design and implementation of a space service that gives pervasive computing applications both a hierarchical and coordinate-based view of physical space. The space service supports both indoor and outdoor campus environments and, besides relational and coordinate space information, it also gives information on how spaces are connected and relevant properties of spaces. We also evaluate the effectiveness of our design using a set of diverse applications, including finding the best "walking" path between two locations, giving walking directions to people, WiFi-based localization, and drawing and annotating maps. We have found that all of our applications depend on both the hierarchical and coordinate views offered by the service, suggesting that the hybrid space model is a very powerful paradigm.

Remos provides resource information to distributed applications. Its design goals of scalability, flexibility, and portability are achieved through an architecture that allows components to be positioned across the network, each collecting information about its local network. To collect information from different types of networks and from hosts on those networks, Remos provides several collectors that use different technologies, such as SNMP or benchmarking. By matching the appropriate collector to each particular network environment and by providing an architecture for distributing the output of these collectors across all querying environments, Remos collects appropriately detailed information at each site and distributes this information where needed in a scalable manner. Prediction services are integrated at the user-level, allowing history-based data collected across the network to be used to generate the predictions needed by a particular user. Remos has been implemented and tested in a variety of networks and is in use in a number of different environments.

Network-aware applications, i.e., applications that adapt to network conditions in an application-specific way, need both static and dynamic information about the network to be able to adapt intelligently to network conditions. The CMU Remos interface gives applications access to a wide range of information in a network-independent fashion. Remos uses a logical topology to capture the network information that is relevant to applications in a concise way. However, collecting this information efficiently is challenging for several reasons: networks use diverse technologies and can be very large (Internet); applications need diverse information; and network managers might have concerns about leaking confidential information. In this paper we present an architecture for a hierarchical collector of network information. The decentralized architecture relies on data collectors that collect information on individual subnets; data collectors can collect information in manner that is appropriate for that subnet and can control the distribution of the information. For application queries that involve multiple subnets, we use a set of master collectors to partition requests and distribute subrequests to individual data collectors and to combine the results. Collectors cache recent network information to improve efficiency and responsiveness. This paper presents and justifies the collector architecture, describes a prototype implementation, and presents preliminary measurements characterizing its operation.

Development of portable network-aware applications demands an interface to the network that allows an application to obtain information about its execution environment. The paper motivates and describes the design of Remos, an API that allows network-aware applications to obtain relevant information. The major challenges in defining a uniform interface are network heterogeneity, diversity in traffic requirements, variability of the information, and resource sharing in the network. Remos addresses these issues with two abstraction levels, explicit management of resource sharing, and statistical measurements. The flows abstraction captures the communication between nodes, and the topologies abstraction provides a logical view of network connectivity. Remos measurements are made at network level, and therefore information to manage sharing of resources is available. Remos is designed to deliver best effort information to applications, and it explicitly adds statistical reliability and variability measures to the core information. The paper also presents preliminary results and experience with a prototype Remos implementation for a high speed IP based network testbed.