Thermal-aware resource allocation in earliest deadline first using fluid scheduling

Thermal issues in microprocessors have become a major design constraint because of their adverse effects on the reliability, performance and cost of the system. This article proposes an improvement in earliest deadline first, a uni-processor scheduling algorithm, without compromising its optimality in order to reduce the thermal peaks and variations. This is done by introducing a factor of fairness to earliest deadline first algorithm, which introduces idle intervals during execution and allows uniform distribution of workload over the time. The technique notably lowers the number of context switches when compare with the previous thermal-aware scheduling algorithm based on the same amount of fairness. Although, the algorithm is proposed for uni-processor environment, it is also applicable to partitioned scheduling in multi-processor environment, which primarily converts the multi-processor scheduling problem to a set of uni-processor scheduling problem and thereafter uses a uni-processor scheduling technique for scheduling. The simulation results show that the proposed approach reduces up to 5% of the temperature peaks and variations in a uni-processor environment while reduces up to 7% and 6% of the temperature spatial gradient and the average temperature in multi-processor environment, respectively.

TA-MCF: Thermal-Aware Fluid Scheduling for Mixed-Criticality System

Fluid scheduling allows tasks to be allocated with fractional processing capacity, which significantly improves the schedulability performance. For dual-criticality systems (DCS), dual-rate fluid-based scheduling has been widely studied, e.g., the state-of-the-art approaches mixed-criticality fluid scheduling (MCF) and MC-Sort. However, most of the existing works on DCS either only focus on the schedulability analysis or minimize the energy consumption treating leakage power as a constant. To this end, this paper considers the effect of temperature on leakage power and proposes a thermal and power aware fluid scheduling strategy, referred to as thermal and energy aware (TA)-MCF which minimizes both the energy consumption and temperature, while ensuring a comparable schedulability ratio compared with the MCF and MC-Sort. Extensive experiments validate the efficiency of TA-MCF.