Linear relaxation techniques for task management in uncertain settings
Pradeep Reddy Varakantham, Stephen F. Smith · 2008
In this paper, we consider the problem of assisting a busy user in managing her workload of pending tasks. We assume that our user is typically oversubscribed, and is invariably jug-gling multiple concurrent streams of tasks (or work flows) of varying importance and urgency. There is uncertainty with re-spect to the duration of a pending task as well as the amount of follow-on work that may be generated as a result of ex-ecuting the task. The user’s goal is to be as productive as possible; i.e., to execute tasks that realize the maximum cu-mulative payoff. This is achieved by enabling the assistant to provide advice about where and how to shed load when all tasks cannot be done. A simple temporal problem with uncertainty and preferences (called an STPPU) provides a natural framework for rep-resenting the user’s current set of tasks. However, current STPPU solution techniques are inadequate as a basis for gen-erating advice in this context, since they are applicable only in the restrictive case where all pending tasks can be ac-complished within time constraints and our principal con-cern is support in oversubscribed circumstances. We present two techniques that are based on linear relaxation for solv-ing the this oversubscribed problem. Given an ordering of tasks, these algorithms identify which tasks to ignore, which to compress and by howmuch, to maximize quality. We show experimentally that our approaches perform significantly bet-ter than techniques adapted from prior research in oversub-scribed scheduling.