Self-Optimization of Task Execution in Pervasive Computing Environments

Pervasive computing environments feature massively distributed systems containing a large number of devices, services and applications that help end-users perform various kinds of tasks. However, these systems are very complex to configure and manage. They are highly dynamic and fault-prone. Another challenge is that since these environments are rich in devices and services, they offer different ways of performing the same task; hence, it is sometimes difficult to choose the "best" resources and strategies to use at any point of time. In this paper, we describe a framework that allows the development of autonomic programs for pervasive computing environments in the form of high-level, parameterized tasks. Each task is associated with various parameters, the values of which may be either provided by the end-user or automatically inferred by the framework based on the current state of the environment, context-sensitive policies, and learned user preferences. A novel multidimensional utility function that uses both quantifiable and non-quantifiable metrics is used to pick the optimal way of executing the task. This framework allows these environments to be self-configuring, self-repairing and adaptive, and to require minimal user intervention. We have developed and used a prototype task execution framework within our pervasive computing system, Gaia (Roman et al., 2002)