To programmatically discover and interact with services in ubiquitous computing environments, an application needs to solve two problems: (1) is it semantically meaningful to interact with a service? If the task is "printing a file", a printer service would be appropriate, but a screen rendering service or CD player service would not. (2) If yes, what are the mechanics of interacting with the service - remote invocation mechanics, names of methods, numbers and types of arguments, etc.? Existing service frameworks such as Jini and UPnP conflate these problems - two services are "semantically compatible" if and only if their interface signatures match. As a result, interoperability is severely restricted unless there is a single, globally agreed-upon, unique interface for each service type. By separating the two subproblems and delegating different parts of the problem to the user and the system, we show how applications can interoperate with services even when globally unique interfaces do not exist for certain services.

The dynamism and heterogeneity in ubicomp environments on both short and long time scales implies that middleware platforms for these environments need to be designed ground up for portability, extensibility and robustness. In this paper, we describe how we met these requirements in iROS, a middleware platform for a class of ubicomp environments, through the use of three guiding principles - economy of mechanism, client simplicity and levels of indirection. Apart from theoretical arguments and experimental results, experience through several deployments with a variety of apps, in most cases not done by the original designers of the system, provides some validation in practice that the design decisions have in fact resulted in the intended portability, extensibility and robustness. A retrospective examination of the system leads the authors to the following lesson: A logically-centralized design and physically-centralized implementation enables the best behavior in terms of extensibility and portability along with ease of administration, and sufficient behavior in terms of scalability and robustness.

FLEX is a new scalable "locality-aware" solution for achieving both load balancing and efficient memory usage on a cluster of machines hosting several Web sites. FLEX allocates the sites to different machines in the cluster based on their traffic characteristics. We propose a set of new methods and algorithms (Simple+, Advanced and Advanced+) to improve the allocation of the Web sites to different machines. New methods show additional performance benefits as compared to the original Simple strategy. Experiments show that FLEX outperforms traditional load-balancing solutions by 50% to 100% (in throughput), even for a four-node cluster. The miss ratio is improved 2-3 times.