Energy-aware scheduling on heterogeneous multi-core systems with guaranteed probability

Liu, Ying; Niu, Jianwei; Atiquzzaman, Mohammed; Long, Xiang

doi:10.1016/j.jpdc.2016.11.014

Cited by 18 publications

(4 citation statements)

References 27 publications

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“…Compared with [12], the approach in [11] mainly focuses on system-level DVFS, and the frequency of each processor cannot be adjusted individually. In [13] and [14], DVFS is combined into the task allocation process to minimize energy consumption under real-time constraints. Besides DVFS and DPM, task migration is also used in multi-core platforms to optimize energy consumption.…”

Section: A Related Workmentioning

confidence: 99%

“…Remark 2.1: Although the number of optional cycles o i is an integer variable, we consider o i a continuous variable to simplify the problem. After problem ( 14) is solved, the result of o i is rounded down, which does not affect the real-time constraint (9) and the energy constraint (13). The influence of this one-cycle approximation is small since a task is usually executed in hundreds and thousands of cycles.…”

Section: Continuous Variablesmentioning

confidence: 99%

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Approximation-Aware Task Deployment on Heterogeneous Multicore Platforms With DVFS

Kritikakou

et al. 2023

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Heterogeneous multi-core platforms, such as ARM big.LITTLE are widely used to execute embedded applications under multiple and contradictory constraints, such as energy consumption and real-time execution. To fulfill these constraints and optimize system performance, application tasks should be efficiently mapped on multi-core platforms. Embedded applications are usually tolerant to approximated results but acceptable Quality-of-Service (QoS). Modeling embedded applications by using the elastic task model, namely, Imprecise Computation (IC) task model, can balance system QoS, energy consumption, and real-time performance during task deployment. However, stateof-the-art approaches seldom consider the problem of IC task deployment on heterogeneous multi-core platforms. They typically neglect task migration, which can improve the solutions due to its flexibility during the task deployment process. This paper proposes a novel QoS-aware task deployment method to maximize system QoS under energy and real-time constraints, where frequency assignment, task allocation, scheduling, and migration are optimized simultaneously. The task deployment problem is formulated as mixed-integer non-linear programming. Then, it is linearized to mixed-integer linear programming to find the optimal solution. Furthermore, based on problem structure and problem decomposition, we propose a novel heuristic with low computational complexity. The sub-problems regarding frequency assignment, task allocation, scheduling, and adjustment are considered and solved in sequence. Finally, the simulation results show that the proposed task deployment method improves the system QoS by 31.2% on average (up to 112.8%) compared to the state-of-theart methods and the designed heuristic achieves about 53.9% (on average) performance of the optimal solution with a negligible computing time.

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Section: A Related Workmentioning

confidence: 99%

Section: Continuous Variablesmentioning

confidence: 99%

Approximation-Aware Task Deployment on Heterogeneous Multicore Platforms With DVFS

Kritikakou

et al. 2023

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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“…Applications such as image processing, high-definition television, and video games are being implemented in embedded system [1], [2]. Due to performance limitations of single-core processors nowadays, the embedded systems generally run on heterogeneous multi-core hardware platforms that include different processing units such as CPU, DSP, FPGA and so on [3]. This structure greatly improves the performance of the embedded system while brings a lot of energy consumption [4], [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

“…In an embedded real-time system, ensuring the real-time characteristics of the task directly determines the baseline for system performance [3]. Therefore, both the time performance and energy cost should be considered in the energy optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Energy-aware Task Scheduling Strategies for Multi-core Embeddded Systems

Liu¹,

Liu²

2018

EasyChair Preprints

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In this paper, we propose two energy-aware scheduling algorithms---(1) Reinforcement learning-based multiprocessor scheduling (RL) algorithm and (2) Mathematical morphology multiprocessor scheduling (MMS) algorithm---for scheduling time-constrained Directed Acyclic Graph (DAG) tasks in an embedded multiprocessor system with Dynamic Voltage And Frequency Scaling (DVFS) and Dynamic Power Management (DPM) technology. Unlike other heuristic scheduling algorithms, the proposed reinforcement learning (RL) is a machine learning algorithm, rarely considered for energy-aware scheduling in DAG tasks. The MMS, inspired by Mathematical morphology that is often used in image processing, continuously adjusts the coded scheduling through a probe matrix to optimize energy consumption. In this paper the genetic algorithm (GA) is compared with these two proposed algorithms by rigorous simulation. The results demonstrate that our algorithms are more energy efficient. Compared with the GA algorithm, the RL and the MMS algorithm significantly improve the energy consumption reduction rate by an average of 13.37% and 72.92% respectively. In addition, MMS algorithm shows better performance in high-density and high-complexity DAG tasks.

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A fast MILP solver for high-level synthesis based on heuristic model reduction and enhanced branch and bound algorithm

et al. 2023

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Energy-aware scheduling on heterogeneous multi-core systems with guaranteed probability

Cited by 18 publications

References 27 publications

Approximation-Aware Task Deployment on Heterogeneous Multicore Platforms With DVFS

Approximation-Aware Task Deployment on Heterogeneous Multicore Platforms With DVFS

Energy-aware Task Scheduling Strategies for Multi-core Embeddded Systems

A fast MILP solver for high-level synthesis based on heuristic model reduction and enhanced branch and bound algorithm

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