METSM: Multiobjective energy-efficient task scheduling model for an edge heterogeneous multiprocessor system

Jiang, Qiangqiang; Xin, Xu; Yao, Libo; Chen, Bo

doi:10.1016/j.future.2023.10.024

Cited by 6 publications

(3 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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

View full text Add to dashboard Cite

“…Lastly, in order to reduce energy usage within the time slice while maintaining the execution needs of the planned activities, HEALERS applies DVFS and DPM mechanisms to all processor cores. [33] discusses the challenge of energy-efficient task scheduling on edge devices with limited power, proposing a multi-objective energy-efficient task scheduling technique (METSM). This technique establishes a mathematical model considering make-span and total energy consumption as optimization objectives, and it introduces a problem-specific algorithm called the iterated greedy-based multi-objective optimizer (IMO) to address task scheduling and resource allocation.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Real-Time Tasks Scheduling on High-Performance Edge-Computing Systems Using Genetic Algorithm

Hussain,

Zakarya,

Ali

et al. 2024

IEEE Access

View full text Add to dashboard Cite

With an increase in the number of processing cores or systems, the high-performance edge-computing system's power consumption along with its computational speed will increase, essentially. However, this comes at the expense of high-energy utilization. One notable solution to reduce the energy consumption of these systems is to execute these systems at the slowest feasible speed so that the job's deadline times are met. Unfortunately, this method is at the expense of more response time and performance loss. To resolve this issue, in this paper, we propose a scheduling approach that associates the genetic algorithm (GA) with the first feasible speed (FiFeS) technique i.e. GA-FiFeS algorithm. This does not jeopardize real-time tasks' deadlines. The GA-FiFeS algorithm proposes an energy-efficient schedule while still ensuring high response times. The results of the proposed approach, using plausible assumptions and experimental parameters, are compared with currently in-practice approaches, i.e. FiFeS and LeFeS (least feasible speed) approaches. Using numerical simulations and plausible assumptions, our investigation suggests that the proposed GA-FiFeS technique outperforms the FiFeS technique in terms of energy consumption (∼18.56%) and response times (∼2.78%). Furthermore, the GA-FiFeS has comparable outcomes with the LeFeS method while taking the expected time of execution as an assessment feature for analysis.

show abstract

“… Jiang et al (2024) proposed a multi-objective energy-efficient task scheduling technique (METSM) to address the challenge of improving task execution performance and reducing energy consumption in edge computing environments, specifically on edge heterogeneous multiprocessor systems (EHMPS). The approach began by establishing a mathematical model that considered both makespan and total energy consumption as optimization objectives, along with decision variables such as task execution sequence, processor assignment, and dynamic voltage and frequency scaling levels.…”

Section: Heuristic Approaches For Task Schedulingmentioning

confidence: 99%

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

Alsadie

2024

PeerJ Computer Science

View full text Add to dashboard Cite

Fog computing has emerged as a prospective paradigm to address the computational requirements of IoT applications, extending the capabilities of cloud computing to the network edge. Task scheduling is pivotal in enhancing energy efficiency, optimizing resource utilization and ensuring the timely execution of tasks within fog computing environments. This article presents a comprehensive review of the advancements in task scheduling methodologies for fog computing systems, covering priority-based, greedy heuristics, metaheuristics, learning-based, hybrid heuristics, and nature-inspired heuristic approaches. Through a systematic analysis of relevant literature, we highlight the strengths and limitations of each approach and identify key challenges facing fog computing task scheduling, including dynamic environments, heterogeneity, scalability, resource constraints, security concerns, and algorithm transparency. Furthermore, we propose future research directions to address these challenges, including the integration of machine learning techniques for real-time adaptation, leveraging federated learning for collaborative scheduling, developing resource-aware and energy-efficient algorithms, incorporating security-aware techniques, and advancing explainable AI methodologies. By addressing these challenges and pursuing these research directions, we aim to facilitate the development of more robust, adaptable, and efficient task-scheduling solutions for fog computing environments, ultimately fostering trust, security, and sustainability in fog computing systems and facilitating their widespread adoption across diverse applications and domains.

show abstract

METSM: Multiobjective energy-efficient task scheduling model for an edge heterogeneous multiprocessor system

Cited by 6 publications

References 38 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

Energy Efficient Real-Time Tasks Scheduling on High-Performance Edge-Computing Systems Using Genetic Algorithm

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

Contact Info

Product

Resources

About