A Gravitational Task Model for Target Sensitive Real-Time Applications

For many years real-time task models have focused the timing constraints on execution windows defined by earliest start times and deadlines for feasibility. However, the utility of some application may vary among scenarios which yield correct behavior, and maximizing this utility improves the resource utilization. For example, target sensitive applications have a target point where execution results in maximized utility, and an execution window for feasibility. Execution around this point and within the execution window is allowed, albeit at lower utility. The intensity of the utility decay accounts for the importance of the application. Examples of such applications include multimedia and control; multimedia application are very popular nowadays and control applications are present in every automated system. In this thesis, we present a novel real-time task model which provides for easy abstractions to express the timing constraints of target sensitive RT applications: the gravitational task model. This model uses a simple gravity pendulum (or bob pendulum) system as a visualization model for trade-offs among target sensitive RT applications. We consider jobs as objects in a pendulum system, and the target points as the central point. Then, the equilibrium state of the physical problem is equivalent to the best compromise among jobs with conflicting targets. Analogies with well-known systems are helpful to fill in the gap between application requirements and theoretical abstractions used in task models. For instance, the so-called nature algorithms use key elements of physical processes to form the basis of an optimization algorithm. Examples include the knapsack problem, traveling salesman problem, ant colony optimization, and simulated annealing. We also present a few scheduling algorithms designed for the gravitational task model which fulfill the requirements for on-line adaptivity. The scheduling of target sensitive RT applications must account for timing constraints, and the trade-off among tasks with conflicting targets. Our proposed scheduling algorithms use the equilibrium state concept to order the execution sequence of jobs, and compute the deviation of jobs from their target points for increased system utility. The execution sequence of jobs in the schedule has a significant impact on the equilibrium of jobs, and dominates the complexity of the problem --- the optimum solution is NP-hard. We show the efficacy of our approach through simulations results and 3 target sensitive RT applications enhanced with the gravitational task model