A novel thermal-constrained energy-aware partitioning algorithm for heterogeneous multiprocessor real-time systems

Abstract

Next-generation multiprocessor real-time systems consume less energy at the cost of increased power density. This increase in power density results in high heat density and may affect the reliability and performance of real-time systems. Thus, incorporating maximum temperature constraints in scheduling of real-time task sets is an important challenge. This paper investigates a novel algorithm for thermal-constrained energyaware partitioning of periodic real-time tasks in heterogeneous multiprocessor systems. When designing our new algorithm, we have applied insights gained from a famous knapsack problem solution. Both simulation and experimental results show that our new branch-and-bound based partitioning algorithm can significantly reduce the total energy consumption of multiprocessor real-time systems.

The Thermal-Constrained Real-Time Systems Design on Multi-Core Platforms -- An Analytical Approach

“…In addition, both [117] and [54] are computationally expensive as the design space becomes larger and/or when they are incorporated in other optimization loops. Barrefors et al [11] formulated the task partitioning as a knapsack problem to minimize the energy under a thermal constraint. However, it ignored the heat transfer among cores and, thus, it is overly optimistic, especially for 3D-ICs.…”