An Efficient Threshold-Based Power Management Mechanism for Heterogeneous Soft Real-Time Clusters

2012 IEEE International Conference on Embedded and Real-Time Computing Systems and Applications

Deogun

2012

Self Cite

Next-generation multi-core multiprocessor real-time systems consume less energy at the cost of increased powerdensity. This increase in power-density results in high heatdensity 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 thermal-constrained energyaware partitioning of periodic real-time tasks in heterogeneous multi-core multiprocessor systems. We adopt a power model which considers the impact of temperature and voltage on a processor's static power consumption. Two types of thermal models are used to respectively capture negligible and non-negligible amount of heat transfer among cores. We develop a novel geneticalgorithm based approach to solve the heterogeneous multicore multiprocessor partitioning problem. Extensive simulations were performed to validate the effectiveness of the approach. Experimental results show that integrating a worst-fit based partitioning heuristic with the genetic algorithm can significantly reduce the total energy consumption of a heterogeneous multicore multiprocessor real-time system.

Section: A Results Using Heat-independent Thermal Modelmentioning

confidence: 99%

“…For simple representation, we normalize the core frequency with respect to f max i . A multiprocessor unit's throughput (or capacity) is assumed to be proportional to the normalized operating frequencies f i,j of its cores [19]. The capacity of ρ i , denoted by μ i , is thus expressed as…”

Section: A Multiprocessor Modelmentioning

confidence: 99%

Thermal-Constrained Energy-Aware Partitioning for Heterogeneous Multi-core Multiprocessor Real-Time Systems

2012 IEEE International Conference on Embedded and Real-Time Computing Systems and Applications

Deogun

2012

Self Cite

“…For simple representation, we normalize the processor frequency with respect to f max i . A processor's throughput (or capacity) is assumed to be proportional to its normalized operating frequency f i [20]. The capacity of ρ i , denoted by μ i , is thus expressed as α i f i , where α i is the performance coefficient of ρ i .…”

Section: A System Modelmentioning

confidence: 99%

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

Barrefors

2014 IEEE 33rd International Performance Computing and Communications Conference (IPCCC)

et al. 2014

Self Cite

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.

“…For simplicity, the frequency of a core is normalized with respect to (wrt) f max i , i.e., we assume f max i = 1. The throughput (or capacity) of a core is proportional to its operating frequency [22]. The capacity of a core M i,j is denoted by µ i,j and is equal to α i f i,j , where α i is the performance coefficient of M i .…”

Section: A Multiprocessor Modelmentioning

confidence: 99%

Adaptive energy-efficient task partitioning for heterogeneous multi-core multiprocessor real-time systems

Deogun

2012 International Conference on High Performance Computing &Amp; Simulation (HPCS)

2012

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The designs of heterogeneous multi-core multiprocessor real-time systems are evolving for higher energy efficiency at the cost of increased heat density. This adversely effects the reliability and performance of the real-time systems. Moreover, the partitioning of periodic real-time tasks based on their worst case execution time can lead to significant energy wastage.In this paper, we investigate adaptive energy-efficient task partitioning for heterogeneous multi-core multiprocessor realtime systems. We use a power model which incorporates the impact of temperature and voltage of a processor on its static power consumption. Two different thermal models are used to estimate the peak temperature of a processor. We develop two feedback-based optimization and control approaches for adaptively partitioning real-time tasks according to their actual utilizations. Simulation results show that the proposed approaches are effective in minimizing the energy consumption and reducing the number of task migrations.