Extending Battery Life of a Multi-buffered, Single-Threaded Processor in a Mobile Computing Device

We introduce an online speed-scaling algorithm that is used to determine the optimum processing rate of executing a set of N jobs by a single processor of a mobile computing device under the single-threading (multi-buffered) computing architecture. We consider heterogeneous tasks that could differ in computation volume, memory and processing requirements. By using speed-scaling, where the processor´s speed is able to dynamically change within hardware and software processing constraints, the algorithm explicitly determines the optimum processing rate of executing each task. This optimum processing rate was found to be a function of the number of ´alive´ tasks (N), the remaining battery energy percentage, the processor´s energy inefficiency coefficient, the unit price of response time and lastly, the unit price of energy. The algorithm allows the user or OS to specify the unit cost of energy and response time for executing all tasks. The algorithm has an operation mode where all tasks´ unit cost of energy is also heuristically affected by the device´ remaining battery energy percentage in accordance with the micro-economic laws of demand and supply. We synthesize the algorithm by analytically minimizing the total cost of both response time and energy consumption of tasks. We also consider other conventional performance metrics to evaluate the algorithm. Using numerical simulations, we show that when the remaining battery energy percentage is factored, the algorithm performs slightly slower (mildly more slower when the battery is almost drained out), but consumes far less energy, can complete significantly more jobs and ultimately allows the mobile computing device to last longer on the go.