University of Houston

The problems of considering energy usage in real-time system scheduling and assignment are discussed in this dissertation. Issues covered include energy-efficient scheduling for multiple feasible interval jobs on a single processor, and assigning frame real-time tasks on multiprocessor systems with rechargeable battery. Both problems are NP-Hard, therefore requiring efficient methods to solve them. For the first problem of multiple feasible interval jobs scheduling, a Simulated Annealing (SA) approach and an on-line greedy heuristic are used to save the energy consumption during execution. In the second problem, the recharge rate of the battery becomes a constraint, and we develop four techniques as solutions, namely Minimum Schedule Length (MSL), Min-min Schedule Length (MmSL), Genetic Algorithm (GA), and Ant Colony Optimization (ACO). The effectiveness of the approaches or techniques for each problem is shown by experimental results, respectively.