the ability to adapt at run time, taking advantage of local computing devices, and coping with dynamically changing resources. Three specific technical challenges in satisfying this requirement are to (1) select an appropriate set of applications or services to carry out a user's task, (2) allocate (possibly scarce) resources among those applications, and (3) reconfigure the applications or resource assignments if the situation changes. In this paper we show how to provide a shared infrastructure that automates configuration decisions given a specification of the user's task. The heart of the approach is an analytical model and an efficient algorithm that can be used at run time to make near-optimal (re)configuration decisions. We validate this approach both analytically and by applying it to a representative scenario.

Abstract. Computers support more and more tasks in the personal and professional activities of users. Such user tasks increasingly span large periods of time and many locations across the enterprise space and beyond. Recently there has been a growing interest in developing applications that can cope with the specific environmental conditions at each location, and adapt to dynamic changes in system resources. However, in a given situation there may be many possible configuration solutions, and an awareness of the user's intent for each task is a critical element in knowing which one to pick. In this paper, we discuss the limitations of building such awareness into applications, and propose to factor the awareness of user tasks into a common software layer. That however, brings up the problem of coordinating the system-wide adaptation performed by such a layer with fine-grain adaptation performed by resource-aware applications. We summarize the main features of an architectural framework that incorporates such a layer, and distill some of the lessons learned in implementing the framework.

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This material is based upon work supported by the National Science Foundation (NSF) under

An important emerging requirement for computing systems is the ability to adapt at run time, taking advantage of local computing devices, and coping with dynamically changing resources. Three specific technical challenges in satisfying this requirement are to (1) select an appropriate set of applications or services to carry out a user’s task, (2) allocate (possibly scarce) resources among those applications, and (3) reconfigure the applications or resource assignments if the situation changes. In this paper we show how to provide a shared infrastructure that automates configuration decisions given a specification of the user’s task. The heart of the approach is an analytical model and an efficient algorithm that can be used at run time to make near-optimal (re)configuration decisions. We validate this approach both analytically and by applying it to a representative scenario.

How to allocate computing and communication resources in a way that maximizes the effectiveness of control and signal processing has been an important area of research. The characteristic of a multi-hop Real-Time Wireless Sensor Network raises new challenges. First, the constraints are more complicated and a new solution method is needed. Second, we need a distributed solution to achieve scalability. This paper presents solutions to both of the new challenges. The first solution to the optimal frequency allocation is a centralized solution that can handle the more general form of constraints as compared with prior research. The second solution is a distributed version for large networks using a pricing scheme. It is capable of incremental adjustment when utility functions change.

the author and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the US government or any other entity. Vahe Poladian ii Tailoring Configuration to User’s Tasks under Uncertainty The expansion of computing infrastructure has opened the possibility of a world in which users can compute everywhere. Despite such advances, computing resources are often scarce and changing, limiting a user’s ability to take advantage of the applications and devices, and requiring changes to the application runtime settings. Currently, the burden of managing the computing environment (devices, applications, and resources) falls on the user. A user must manually start applications and adjust their settings according to the available resources. Assigning such chores of configuration to the user has a number of disadvantages. First, it consumes user’s precious cognitive resources. Second, effectively managing the environment requires skills that a typical user might not have. Third, even with adequate low-level expertise, managing the environment optimally (or even adequately) can be difficult.