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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Vydáno v:Neural computing & applications Ročník 32; číslo 10; s. 5553 - 5570
Hlavní autoři: Tong, Zhao, Deng, Xiaomei, Chen, Hongjian, Mei, Jing, Liu, Hong
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.05.2020
Springer Nature B.V
Témata:
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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Popis
Shrnutí: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.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-019-04118-8