Online energy-efficient scheduling of DAG tasks on heterogeneous embedded platforms

Hu, Bi Ying; Yang, Xing; Zhao, Mingguo

doi:10.1016/j.sysarc.2023.102894

Cited by 9 publications

(2 citation statements)

References 58 publications

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“…References [ [36] , [37] , [38] , [39] , [40] ] confirm the role of Particle Swarm Optimization algorithms in enhancing MEC by improving energy efficiency and reducing latency, thus improving service quality for IoT devices. Similarly, references [ [41] , [42] , [43] , [44] , [45] ] demonstrates that effective task scheduling can significantly reduce energy consumption. Together, these studies substantiate our study's objectives to optimize energy consumption and task scheduling efficiency in the MEC environment, showing that strategic task scheduling and the use of sophisticated algorithms like Particle Swarm Optimization are vital for enhancing energy efficiency and operational performance in such systems.…”

Section: Conclusion and Limitationmentioning

confidence: 94%

Optimizing low-power task scheduling for multiple users and servers in mobile edge computing by the MUMS framework

Li,

2024

Heliyon

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Section: Conclusion and Limitationmentioning

confidence: 94%

Optimizing low-power task scheduling for multiple users and servers in mobile edge computing by the MUMS framework

Li,

2024

Heliyon

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“…Biao H et al investigated how to dynamically schedule dynamic applications on heterogeneous embedded systems and proposed energy-efficient scheduling algorithms for the optimization of the system energy consumption problem. However, this paper uses an objective function and a constraint formulation to design the scheduling problem, which has a relatively high time complexity [21]. Tegg T et al proposed SoCRATE, a task scheduling scheme for the domain of System-on-a-Chip based on the DRL algorithm.…”

Section: Related Workmentioning

confidence: 99%

MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform

Peng,

Wang

2023

Electronics

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Heterogeneous architecture-based systems-on-chip enable the development of flexible and powerful multifunctional RF systems. In complex and dynamic environments where applications arrive continuously and stochastically, real-time scheduling of multiple applications to appropriate processor resources is crucial for fully utilizing the heterogeneous SoC’s resource potential. However, heterogeneous resource-scheduling algorithms still face many problems in practical situations, including generalized abstraction of applications and heterogeneous resources, resource allocation, efficient scheduling of multiple applications in complex mission scenarios, and how to ensure the effectiveness combining with real-world applications of scheduling algorithms. Therefore, in this paper, we design the Multi-Application Scheduling Algorithm, named MASA, which is a two-phase scheduler architecture based on Deep Reinforcement Learning. The algorithm is made up of neural network scheduler-based task prioritization for dynamic encoding of applications and heuristic scheduler-based task mapping for solving the processor resource allocation problem. In order to achieve stable and fast training of the network scheduler based on the actor–critic strategy, we propose optimization methods for the training of MASA: reward dynamic alignment (RDA), earlier termination of the initial episodes, and asynchronous multi-agent training. The performance of the MASA is tested with classic directed acyclic graph and six real-world application datasets, respectively. Experimental results show that MASA outperforms other neural scheduling algorithms and heuristics, and ablation experiments illustrate how these training optimizations improve the network’s capacity.

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Tournament based equilibrium optimization for minimizing energy consumption on dynamic task scheduling in cloud-edge computing

Souri,

Mood,

Gao

et al. 2024

Cluster Comput

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Online energy-efficient scheduling of DAG tasks on heterogeneous embedded platforms

Cited by 9 publications

References 58 publications

Optimizing low-power task scheduling for multiple users and servers in mobile edge computing by the MUMS framework

Optimizing low-power task scheduling for multiple users and servers in mobile edge computing by the MUMS framework

MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform

Tournament based equilibrium optimization for minimizing energy consumption on dynamic task scheduling in cloud-edge computing

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