Run-Time Management for Multicore Embedded Systems with energy Harvesting (solar energy)

Through energy harvesting system, new energy sources are made available immediately for many advanced applications based on environmentally embedded systems. However, the harvested power, such as the solar energy, varies significantly under different ambient conditions, which in turn affects the energy conversion efficiency. There are two approach for designing power-adaptive computing systems to maximize the energy utilization under variable solar power supply, which I will b discussing in this paper. First paper —Power Adaptive Computing System for Solar-Energy-Powered Embedded Systems‖ uses the power adaptive computing systems to maximize the energy utilization under variable solar power supply. Here the geometric programming technique is exploited which can generate a customized parallel computing structure effectively. Second paper —Run-Time Management for Multicore Embedded Systems With Energy Harvesting‖ proposes a novel semidynamic algorithm (SDA)-based framework with energy budgeting that manages energy and workload allocation at runtime for multicore embedded systems with solar energy harvesting capability.