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A Study on Multi-core task scheduling algorithm based on Artificial Intelligence

INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2016年第12期9卷 307-320页

作者： Hu Zhiyu Li Li Jingdezhen Univ Jingdezhen City 333000 Jiangxi Peoples R China

With the rapid development of science technology, the multi-core processor system has been become one of the hottest issues in the high performance computation field at present. At the same time, there are some problems in the application and the process of development. In order to find the more efficient task scheduling algorithm, this paper will research the multi-core processors task scheduling algorithm. With exploring the existing task scheduling algorithm principle, the heterogeneous multi-core processor system task scheduling mathematical model is be built, and based on the genetic algorithm, the paper proposes the heterogeneous multi-core processor system scheduling based on population genetic algorithm. Then, through the feasibility, parameter analysis, verification algorithm, the improved genetic algorithm effectively improves the system performance, and reduces the running time. The model, in a certain extent, increases the application and development of artificial intelligence, and provides a theoretical basis for related research.

关键词： multi-core processor system task scheduling algorithm heterogeneous multi-core system task scheduling algorithm model genetic algorithm verification

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Grid task scheduling algorithm Based on Market Economy Models

Grid Task Scheduling Algorithm Based on Market Economy Model...

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2011 4th IEEE International Conference on Computer Science and Information Technology(ICCSIT 2011)

作者： Lei Chen 1 1Computer Science Deparment, Chongqing University of Arts and Sciences, Chongqing,China

Meeting user task's Quality of Service(QoS) requirements,providing different QoS requirement for Grid users is an important target and a new issue of task scheduling in Service Grid *** the basis of the price model drove by supply and demand in economy,this paper designs the Grid task scheduling algorithm in the market economy. The improved algorithm can achieves the optimized resource allocation strategy on user demand,and improves the system's *** is implemented and the advantages of the new ant algorithm are investigated in a grid simulator called OptorSim after the simulator has been *** experiment results demonstrate the effectiveness of the newly proposed algorithm in terms of users'QoS guarantee,which can reduce data access latency,decrease bandwidth consumption and distribute storage site *** approach is compared with others based on the quality of the prediction formulated by inaccurate information

关键词： market economy model QoS guarantee task scheduling algorithm

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A High Priority Random task Fuzzy scheduling algorithm for CPS 31

A High Priority Random Task Fuzzy Scheduling Algorithm for C...

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31st Chinese Control And Decision Conference (CCDC)

作者： Zhang, Jing Chen, Cheng Zheng, Huan-ke Luo, Qing-yi Kunming Univ Sci & Teclmol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Yunnan Xiaorun Technol Serv Co Ltd Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9781728101057

In the real-time task scheduling of Cyber Physical System (CPS), the existing scheduling algorithms can't effectively differentiate the importance level of random tasks, resulting in poor real-time scheduling of important random tasks and insufficient submission rate. This paper comprehensively considers the factors of recent available time, criticality and execution time, and then proposes a fuzzy scheduling algorithm of high priority random tasks (FSA-HPRT) for CPS. The algorithm first obtains the applicable time slice by maximizing delayed the execution time of the periodic task, and calculates the difference between the end time of the periodic task and the deadline time as the error time slice. Then use the fuzzy set to process the latest available time, criticality and execution time of the random task to decide the random task priority. Finally, the task-scheduling queue is established for the system to perform scheduling according to the priority of the random task by using the applicable time slice and the error time slice. The simulation results show that compared with the ISS algorithm, the proposed algorithm can significantly improve the submission rate of important random tasks. At the same time, high-priority random tasks can get responses preferentially.

关键词： Idle time Random task Fuzzy set task submission rate CPS task scheduling algorithm

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Multidomain least load algorithm: A task scheduling algorithm on internet

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Wuhan Daxue Xuebao/Journal of Wuhan University 1999年第5期45卷 754-754页

作者： Deng, Wan-Tao Peng, De-Chun Lin, Zi-Yu Stt. Key Lab. of Software Eng. Wuhan University Wuhan 430072 China

Describes a task scheduling algorithm based on Internet-Multidomain Least Load scheduling algorithm. MD Least presents some special strategy to solve problems about the task scheduling at Internet, such as scalability and message passing delay. The result of simulation shows the performance of MD Least is much better than some others.

关键词： Internet Multidomain least load scheduling algorithm task scheduling algorithm

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Queue-priority optimized algorithm: a novel task scheduling for runtime systems of application integration platforms

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JOURNAL OF SUPERCOMPUTING 2022年第1期78卷 1501-1531页

作者： Freire, Daniela L. Frantz, Rafael Z. Roos-Frantz, Fabricia Basto-Fernandes, Vitor Inst Univ Lisboa ISCTE IUL ISTAR IUL Lisbon Portugal Unijui Univ Dept Exact Sci & Engn Ijui Brazil

The need for integration of applications and services in business processes from enterprises has increased with the advancement of cloud and mobile applications. Enterprises started dealing with high volumes of data from the cloud and from mobile applications, besides their own. This is the reason why integration tools must adapt themselves to handle with high volumes of data, and to exploit the scalability of cloud computational resources without increasing enterprise operations costs. Integration platforms are tools that integrate enterprises' applications through integration processes, which are nothing but workflows composed of a set of atomic tasks connected through communication channels. Many integration platforms schedule tasks to be executed by computational resources through the First-in-first-out heuristic. This article proposes a Queue-priority algorithm that uses a novel heuristic and tackles high volumes of data in the task scheduling of integration processes. This heuristic is optimized by the Particle Swarm Optimization computational method. The results of our experiments were confirmed by statistical tests, and validated the proposal as a feasible alternative to improve integration platforms in the execution of integration processes under a high volume of data.

关键词： task scheduling algorithm System integration Application integration Optimization Heuristic

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KANETAS: an elastic scheduler for heterogeneous many-core systems

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CCF TRANSACTIONS ON HIGH PERFORMANCE COMPUTING 2025年 1-15页

作者： Mao, Zhao Zhang, Xingjun Wang, Longxiang Xi'an Jiaotong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China

Efficient program execution on massively parallel clusters is critical for fields like scientific computing and artificial intelligence. However, traditional task scheduling algorithms do not fully leverage platform characteristics, resulting in inefficiency and long task execution times. We propose KANETAS, a reinforcement learning-based DAG (Directed Acyclic Graph) elastic task scheduling algorithm, designed to adapt to DAG tasks of various scales and structures. Kolmogorov-Arnold Network (KAN) was applied to the DAG scheduling problem. It enhances the efficiency of heterogeneous hardware by using Graph Convolutional Networks (GCN) and Actor-Critic algorithm (A2C), recognizing hardware features and assigning tasks to appropriate computing units. We have conducted extensive experiments to evaluate the proposed solution with four strong baseline algorithms, including the state-of-the-art heuristics method and a variety of deep reinforcement learning based algorithms. The experimental results suggest that KANETAS can reduce the average makespan of the best baseline algorithm by 13.1% at most. Furthermore, compared to the MLP version, the KAN version showed superior performance. The proposed model demonstrates a clear advantage in load balancing.

关键词： Reinforcement learning task scheduling algorithm Graph neural network Heterogeneous computing Kolmogorov-Arnold network

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Posterior task scheduling algorithms for heterogeneous computing systems

Posterior task scheduling algorithms for heterogeneous compu...

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7th International Conference on High Performance Computing for Computational Science

作者： Shen, Linshan Choe, Tae-Young Kumoh Natl Inst Technol Sch Comp Engn Kumi 730701 South Korea

ISBN: (纸本)9783540713500

The task scheduling problem in heterogeneous system is known as NP-hard. Recently, Bajaj and Agrawal proposed an algorithm TANH (task duplication-based scheduling algorithm for Network of Heterogeneous systems) with optimality conditions, which are wider than previous optimality conditions. TANH algorithm combines the clustering technique with task duplication. We propose two postprocessing algorithms, HPSA1 (Heterogeneous Posterior scheduling algorithm) and HPSA2, to reduce the schedule length for DAGs which don't satisfy the optimality conditions of TANH algorithm. Our algorithms reduce the schedule length by exchanging task clusters in which its parent tasks reside. We compare with HCNF (Heterogeneous Critical Node First) algorithm by illustrating an example to show how our algorithms operate.

关键词： heterogeneous computing system DAG task scheduling algorithm postprocessing clustering.

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A New Static Data Flow Clustering algorithm for task scheduling of Irregular Mesh in NoCs Based on Complex Networks

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INTERNATIONAL JOURNAL OF FUTURE GENERATION COMMUNICATION AND NETWORKING 2016年第9期9卷 181-190页

作者： Liu, Yue Cao, MengMeng Jie, Kong Kaifeng Univ Inst Informat Engn Kaifeng 475004 Peoples R China Naval Acad Armament Beijing 100091 Peoples R China

The majority of recent embedded systems are based on MPSoCs (Multi-Processors System on Chip) architectures. The topologies and the interconnections inside multi processors almost adopt NoCs (Networks on Chip) whose topology and task scheduling algorithm have a direct impact on its performances. In this paper, by using static data flow, a task scheduling algorithm which would automatically assign the application tasks onto different processors is proposed based on complex network. The goal of our algorithm is to replace the static data flow subnetwork by a single dynamic data flow actor such that the global performance in terms of latency and throughput is optimized. Through complex network, it greatly enhances the power of our algorithm in terms of avoiding deadlock, saving energy and providing for integration with more general models of computation. Experimental results show up to 60% performance improvement for real-world examples.

关键词： NoC MPSoC static data flow task scheduling algorithm complex network

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Power Supply Noise Aware task scheduling on Homogeneous 3D MPSoCs Considering the Thermal Constraint

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Journal of Computer Science & Technology 2018年第5期33卷 966-983页

作者： Ying-Lin Zhao Jian-Lei Yang Wei-Sheng Zhao Aida Todri-Sanial Yuan-Qing Cheng Fert Beijing Research Institute Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing 100191 China School of Electronic and Information Engineering Beihang University Beijing 100191 China Qingdao Research Institute Beihang University Qingdao 266041 China School of Computer Science and Engineering Beihang University Beijing 100191 China Laboratory of Informaties Robotics and Mieroeleetronics University of Montpellier Montpellier 34095 France National Center for Scientific Research MontpeUier 34095 France

Thanks to the emerging 3D integration technology, The multiprocessor system on chips （MPSoCs） can now integrate more IP cores on chip with improved energy efficiency. However, several severe challenges also rise up for 3D ICs due to the die-stacking architecture. Among them, power supply noise becomes a big concern. In the paper, we investigate power supply noise （PSN） interactions among different cores and tiers and show that PSN variations largely depend on task assignments. On the other hand, high integration density incurs a severe thermal issue on 3D ICs. In the paper, we propose a novel task scheduling framework considering both the PSN and the thermal issue. It mainly consists of three parts. First, we extract current stimuli of running tasks by analyzing their power traces derived from architecture level simulations. Second, we develop an efficient power delivery network （PDN） solver to evaluate PSN magnitudes efficiently. Third, we propose a heuristic algorithm to solve the formulated task scheduling problem. Compared with the state-of-the-art task assignment algorithm, the proposed method can reduce PSN by 12% on a 2 × 2 × 2 3D MPSoCs and by 14% on a 3 × 3 × 3 3D MPSoCs. The end-to-end task execution time also improves as much as 5.5% and 7.8% respectively due to the suppressed PSN.

关键词： MPSoCs power supply noise （PSN） power delivery network （PDN） task scheduling algorithm temperature 3D

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Performance evaluation of political teaching MOOC system based on task scheduling and AI system

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SOFT COMPUTING 2023年第14期27卷 10379-10388页

作者： Cui, Wanru Zhengzhou Railway Vocat & Tech Coll Ideol & Polit Theory Teaching Dept Zhengzhou 451460 Henan Peoples R China

In the network control system, due to the limitation of network bandwidth and control system conditions, the information transmission of sampling and control must be completed in a certain time, otherwise it will cause information transmission delay, thus reducing the control performance of the network system. This paper builds a MOOC political teaching platform based on task scheduling and artificial intelligence system, and the use of MOOC ideological and political education model can improve students' participation and practicability. Under the MOOC model, students should be able to learn by participating in online video courses, skillfully applying theoretical knowledge of ideological and political education, and then by participating in physical classroom discussions. The simulation results show that the system can realize real-time data transmission, and the information transmission of sampling and control must be completed in a certain time, otherwise it will cause information transmission delay, thus reducing the control performance of the network system. Due to the limitation of network bandwidth, if multiple control systems work together, there will be transmission delays and other situations, which will directly affect the control performance of the system and cause system instability.

关键词： MOOC task scheduling algorithm Virtual simulation Political teaching

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