Task Scheduling in Real Time Systems with Energy Harvesting and Energy Minimization

Abstract

Energy is a major concern in battery operated real time devices (e.g. sensor nodes situated in remote areas) as battery life is solely responsible for the functioning of such devices. For such systems to work continuously and successively a regular source of energy is required. The lifetime of such systems can be increased by reducing the energy consumption and increasing energy production. Dynamic Voltage Scaling (DVS) scheduling algorithms is an existing approach for minimizing energy consumption. Energy harvesting is used for energy production (as a source of energy) and Earliest Deadline First (EDF) is an existing traditional approach for task scheduling in real time systems. In this paper, we propose a real time task scheduling algorithm using both DVS and EDF with energy harvesting. The proposed approach is designed to reduce energy consumption and enhanced the system lifetime. Our experimental results show that the proposed approach increases the task feasibility and reduces the energy consumption to increase the system lifetime.

Online Unit Profit Knapsack with Untrusted Predictions

“…Much additional work has been done for other online problems, studying variants with predictions (machine-learned advice, for instance), initiated by the work of Lykouris and Vassilvitskii [33,34] and Purohit et al [40] in 2018, with further work in the directions of search-like problems [2,7,14,30,31,36], scheduling [1,5,10,21,27,28,32,37], rental problems [20,26,43], caching/paging [13, 23,24,41,44], and other problems [6,8,9,12,38,42], while some papers attack multiple problems [3,11,29,45]. For a survey, see [39].…”