QL-HEFT: a novel machine learning scheduling scheme base on cloud computing environment

Cloud computing is a computing model that fully utilizes the resources on the Internet to maximize the utilization of resources. Due to a large number of users and tasks, it is important to achieve efficient scheduling of tasks submitted by users. Task scheduling is one of the crucial and challengin...

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Veröffentlicht in:	Neural computing & applications Jg. 32; H. 10; S. 5553 - 5570
Hauptverfasser:	Tong, Zhao, Deng, Xiaomei, Chen, Hongjian, Mei, Jing, Liu, Hong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.05.2020 Springer Nature B.V
Schlagworte:	Advances in Parallel and Distributed Computing for Neural Computing Algorithms Artificial Intelligence Classification Cloud computing Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Image Processing and Computer Vision Machine learning Microprocessors Polynomials Probability and Statistics in Computer Science Response time Scheduling Sorting algorithms Task scheduling Task scheduling Cloud computing Directed acyclic graph Makespan Reinforcement learning
ISSN:	0941-0643, 1433-3058
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Abstract	Cloud computing is a computing model that fully utilizes the resources on the Internet to maximize the utilization of resources. Due to a large number of users and tasks, it is important to achieve efficient scheduling of tasks submitted by users. Task scheduling is one of the crucial and challenging non-deterministic polynomial-hard problems in cloud computing. In task scheduling, obtaining shorter makespan is an important objective and is related to the pros and cons of the algorithm. Machine learning algorithms represent a new method for solving this type of problem. In this paper, we propose a novel task scheduling algorithm called QL-HEFT that combines Q -learning with the heterogeneous earliest finish time (HEFT) algorithm to reduce the makespan. The algorithm uses the upward rank ( rank u ) value of HEFT as the immediate reward in the Q -learning framework. The agent can obtain better learning results to update the Q -table through the self-learning process. The QL-HEFT algorithm is divided into two major phases: a task sorting phase based on Q -learning for obtaining an optimal order and a processor allocation phase using the earliest finish time strategy. Experiments show that QL-HEFT achieves a shorter makespan compared to three other classical scheduling algorithms as well as good performances in terms of the average response time.
AbstractList	Cloud computing is a computing model that fully utilizes the resources on the Internet to maximize the utilization of resources. Due to a large number of users and tasks, it is important to achieve efficient scheduling of tasks submitted by users. Task scheduling is one of the crucial and challenging non-deterministic polynomial-hard problems in cloud computing. In task scheduling, obtaining shorter makespan is an important objective and is related to the pros and cons of the algorithm. Machine learning algorithms represent a new method for solving this type of problem. In this paper, we propose a novel task scheduling algorithm called QL-HEFT that combines Q-learning with the heterogeneous earliest finish time (HEFT) algorithm to reduce the makespan. The algorithm uses the upward rank (ranku) value of HEFT as the immediate reward in the Q-learning framework. The agent can obtain better learning results to update the Q-table through the self-learning process. The QL-HEFT algorithm is divided into two major phases: a task sorting phase based on Q-learning for obtaining an optimal order and a processor allocation phase using the earliest finish time strategy. Experiments show that QL-HEFT achieves a shorter makespan compared to three other classical scheduling algorithms as well as good performances in terms of the average response time. Cloud computing is a computing model that fully utilizes the resources on the Internet to maximize the utilization of resources. Due to a large number of users and tasks, it is important to achieve efficient scheduling of tasks submitted by users. Task scheduling is one of the crucial and challenging non-deterministic polynomial-hard problems in cloud computing. In task scheduling, obtaining shorter makespan is an important objective and is related to the pros and cons of the algorithm. Machine learning algorithms represent a new method for solving this type of problem. In this paper, we propose a novel task scheduling algorithm called QL-HEFT that combines Q -learning with the heterogeneous earliest finish time (HEFT) algorithm to reduce the makespan. The algorithm uses the upward rank ( rank u ) value of HEFT as the immediate reward in the Q -learning framework. The agent can obtain better learning results to update the Q -table through the self-learning process. The QL-HEFT algorithm is divided into two major phases: a task sorting phase based on Q -learning for obtaining an optimal order and a processor allocation phase using the earliest finish time strategy. Experiments show that QL-HEFT achieves a shorter makespan compared to three other classical scheduling algorithms as well as good performances in terms of the average response time.
Author	Mei, Jing Liu, Hong Deng, Xiaomei Chen, Hongjian Tong, Zhao
Author_xml	– sequence: 1 givenname: Zhao orcidid: 0000-0002-8624-6364 surname: Tong fullname: Tong, Zhao email: tongzhao@hunnu.edu.cn organization: College of Information Science and Engineering, Hunan Normal University – sequence: 2 givenname: Xiaomei surname: Deng fullname: Deng, Xiaomei organization: College of Information Science and Engineering, Hunan Normal University – sequence: 3 givenname: Hongjian surname: Chen fullname: Chen, Hongjian organization: College of Information Science and Engineering, Hunan Normal University – sequence: 4 givenname: Jing surname: Mei fullname: Mei, Jing organization: College of Information Science and Engineering, Hunan Normal University – sequence: 5 givenname: Hong surname: Liu fullname: Liu, Hong organization: College of Information Science and Engineering, Hunan Normal University
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