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Abstract-This work considers the satellite data processing portion of a space-based weather monitoring system. It uses a heterogeneous distributed processing platform. There is uncertainty in the arrival time of new data sets to be processed, and resource allocation must be robust with respect to this uncertainty. The tasks to be executed by the platform are classified into two broad categories: high priority (e.g., telemetry, tracking, and control), and revenue generating (e.g., data processing and data research). In this environment, the resource allocation of the high-priority tasks must be done before the resource allocation of the revenue generating tasks. A two-part allocation scheme is presented in this research. The goal of first part is to find a resource allocation that minimizes makespan of the high-priority tasks. The robustness for the first part of the mapping is defined as the difference between this time and the expected arrival of the next data set. For the second part, the robustness of the mapping is the difference between the expected arrival time and the time at which the revenue earned is equal to the operating cost. Thus, the heuristics for the second part find a mapping that minimizes the time for the revenue (gained by completing revenue generating tasks) to be equal to the cost. Different resource allocation heuristics are designed and evaluated using simulations, and their performance is compared to a mathematical bound.

Abstract

Abstract. Autonomous mobile robots have been achieving significant improvement in recent years. Intelligent mobile robots may detect hazardous materials or survivors after a disaster. Mobile robots usually carry limited energy (mostly rechargeable batteries) so energy conservation is crucial. In a mobile robot, the processor and the motors are two major energy consumers. While a robot is moving, it has to detect an obstacle before a collision. This results in a real-time constraint: the processor has to distinguish an obstacle within the traveled time interval. This constraint requires that the processor run at a high frequency. Alternatively, the robot's motors can slow down to enlarge the time interval. This paper presents a new approach to simultaneously adjust the processor's frequency and the motors' speed to conserve energy and meet the real-time constraint. We formulate the problem as non-linear optimization and solve the problem using a genetic algorithm for both continuous and discrete cost functions. Our experiments demonstrate that more energy can be saved by adjusting both the frequency and the speed simultaneously.