Adaptive scheduling of real-time systems cosupplied by renewable and nonrenewable energy sources
ACM Transactions on Embedded Computing Systems (TECS) - Special Section on ESTIMedia'10
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In battery-powered embedded systems, the limit of battery charge creates a challenge in scheduling tasks to meet both their deadlines and Quality of Service (QoS) requirements. Harvesting energy from the surrounding environment continuously eliminates the concern of limited battery charge. However, the uncertainty in availability of energy brings challenges in embedded systems. In this paper, we propose an energy management technique to maximize QoS of the system. Our technique is composed of two steps: an offline step and an online step. In the offline step we use frame-based energy harvesting prediction in one harvesting period, in order to find the best QoS level for the tasks and maximize the energy utilization. The information provided from the offline step guides the online scheduler to decide about job scheduling at run-time to minimize the QoS violation. We compared our scheduler with other approaches and on average we reduce the violation count by 22%.