Sensor networks (SN) are rapidly becoming the tool of choice for monitoring. Their versatility makes them useful in numerous and diverse application domains. However, most SN deployments assume that the area and events to monitor/control are well known/understood at design time. Thus, sensors' configurations can be defined prior to their deployment. Nevertheless, when the purpose of an SN is to monitor the events of an uncharted environment, where the distribution and nature of events is uncertain, it is rather intricate to configure its sensors at design time. Instead, sensors should be able to self-configure at run time. In this paper, we propose a low-cost (in terms of energy and computation) collective approach that allows the sensors in an SN to collaboratively search for their most appropriate configurations only using their local knowledge. We empirically show that our approach can help sensors efficiently monitor environments where various dynamic events exist.

We propose a computational model that facilitates agents in a MAS to collaboratively evolve their norms to reach the best norm conventions. Our approach borrows from the social contagion phenomenon to exploit the notion of positive infection: agents with good behaviors become infectious to spread their norms in the agent society. By combining infection and innovation, our computational model helps a MAS establish better norm conventions even when a sub-optimal one has fully settled in the population.

This paper discusses process analysis issues, especially in the context of outsourced processes. In particular, we describe a process warehousing solution developed at HP along with the challenges we had to face and the lessons we learned in implementing and deploying it.

No abstract available.

We describe a new class of utility-maximization scheduling problem with precedence constraints, the disconnected staged scheduling problem (DSSP). DSSP is a nonpreemptive multiprocessor deadline scheduling problem that arises in several commercially-important applications, including animation rendering, protein analysis, and seismic signal processing. DSSP differs from most previously-studied deadline scheduling problems because the graph of precedence constraints among tasks within jobs is disconnected, with one component per job. Another difference is that in practice we often lack accurate estimates of task execution times, and so purely offline solutions are not possible. However we do know the set of jobs and their precedence constraints up front and therefore some offline planning is possible.Our solution decomposes DSSP into an offline job selection phase followed by an online task dispatching phase. We model the former as a knapsack problem and explore several solutions to it, describe a new dispatching algorithm for the latter, and compare both with existing methods. Our theoretical results show that while DSSP is NP-hard and inapproximable in general, our two-phase scheduling method guarantees a good performance bound for many special cases. Our empirical results include an evaluation of scheduling algorithms on a real animation-rendering workload; we present a characterization of this workload in a companion paper. The workload records eight weeks of activity on a 1,000-CPU cluster used to render portions of the full-length animated feature film Shrek 2 in 2004. We show that our improved scheduling algorithms can substantially increase the aggregate value of completed jobs compared to existing practices. Our new task dispatching algorithm LCPF performs well by several metrics, including job completion times as well as the aggregate value of completed jobs.