Research on Cloud Computing Resources Provisioning Based on Reinforcement Learning

Peng, Zhiping; Cui, Delong; Zuo, Jiakuo; Lin, Weiwei

doi:10.1155/2015/916418

Cited by 11 publications

(3 citation statements)

References 14 publications

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“…RL is a model-free learning method with powerful decision-making capability and can effectively solve multi-constrained MOO problem. Peng et al [12,13] utilized RL to find the optimal scheduling strategy and solve TSRA problem in cloud environments. Cui et al [14] proposed a task scheduling scheme based on RL, which also applies multi-agent and parallel technology to balance exploration and exploitation in the learning process, and achieves the maximum reduction of task makespan under the constraints of task deadlines and VM resources.…”

Section: Literature Reviewmentioning

confidence: 99%

UDL: a cloud task scheduling framework based on multiple deep neural networks

Peng

Cui

et al. 2023

J Cloud Comp

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Cloud task scheduling and resource allocation (TSRA) constitute a core issue in cloud computing. Batch submission is a common user task deployment mode in cloud computing systems. In this mode, it has been a challenge for cloud systems to balance the quality of user service and the revenue of cloud service provider (CSP). To this end, with multi-objective optimization (MOO) of minimizing task latency and energy consumption, we propose a cloud TSRA framework based on deep learning (DL). The system solves the TSRA problems of multiple task queues and virtual machine (VM) clusters by uniting multiple deep neural networks (DNNs) as task scheduler of cloud system. The DNNs are divided into exploration part and exploitation part. At each scheduling time step, the model saves the best outputs of all scheduling policies from each DNN to the experienced sample memory pool (SMP), and periodically selects random training samples from SMP to train each DNN of exploitation part. We designed a united deep learning (UDL) algorithm based on this framework. Experimental results show that the UDL algorithm can effectively solve the MOO problem of TSRA for cloud tasks, and performs better than benchmark algorithms such as heterogeneous distributed deep learning (HDDL) in terms of task scheduling performance.

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Section: Literature Reviewmentioning

confidence: 99%

UDL: a cloud task scheduling framework based on multiple deep neural networks

Peng

Cui

et al. 2023

J Cloud Comp

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“…First, we must specify the kernel size of a convolution kernel, it is often an odd number, and in most of the proposed CNN models, the kernel size is 3, 5, or 7. Since a larger size kernel often contains more parameters and we can get the same effect with a large kernel with multiple small kernels, we can just use the small kernels in the convolutional network [19].…”

Section: Deep Learning-related Technologiesmentioning

confidence: 99%

A Business Management Resource-Scheduling Method based on Deep Learning Algorithm

Wang

2022

Mathematical Problems in Engineering

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As China’s economic level and industrial volume continue to develop and expand, the traditional methods of business management and resource scheduling demonstrate significant limitations and misalignments with China’s daily economic activities. Traditional industrial and commercial management resource scheduling is highly reliant on manual labor. When confronted with the requirements of large-scale project management, the pure manual mode cannot track the real-time progress of the project flexibly, effectively, and in a timely manner, resulting in the inability to complete the corresponding resource-scheduling work efficiently, which will affect the overall operation progress of the project. Deep neural networks have a high-application potential for solving extremely complex and highly nonlinear optimization problems. Artificial intelligence and deep learning research have advanced at a rapid pace over the past few decades, thanks to the efforts of numerous researchers. Combined with GPU technology, the deep learning framework can provide an extremely complex optimization problem with a practical and feasible optimization scheme and corresponding solution path in a very short amount of time. Therefore, this paper explores the potential application of deep learning technology to industrial and commercial resource-scheduling management. By analyzing the benefits of deep learning technology and the bottleneck issues of existing industrial and commercial resource scheduling, a real-time optimized industrial and commercial resource-scheduling model based on deep learning technology is developed. The model is evaluated using the respective data set. The test results demonstrate that the resource-scheduling model proposed in this paper has strong real-time and high-operation efficiency and can assist engineers in completing the corresponding resource-scheduling tasks.

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“…Previous works focusing on the performance of the MapReduce job have indicated the performance degradation in the virtual clusters [4][5][6][7]. Other researchers have found that the performance interference [8][9][10] is one of the important factors causing such degradation.…”

Section: Introductionmentioning

confidence: 99%

Optimized Speculative Execution to Improve Performance of MapReduce Jobs on Virtualized Computing Environment

Yang

Zhang

2017

Mathematical Problems in Engineering

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Recently, virtualization has become more and more important in the cloud computing to support efficient flexible resource provisioning. However, the performance interference among virtual machines may affect the efficiency of the resource provisioning. In a virtualized environment, where multiple MapReduce applications are deployed, the performance interference can also affect the performance of the Map and Reduce tasks resulting in the performance degradation of the MapReduce jobs. Then, in order to ensure the performance of the MapReduce jobs, a framework for scheduling the MapReduce jobs with the consideration of the performance interference among the virtual machines is proposed. The core of the framework is to identify the straggler tasks in a job and back up these tasks to make the backed up one overtake the original tasks in order to reduce the overall response time of the job. Then, to identify the straggler task, this paper uses a method for predicting the performance interference degree. A method for scheduling the backing-up tasks is presented. To verify the effectiveness of our framework, a set of experiments are done. The experiments show that the proposed framework has better performance in the virtual cluster compared with the current speculative execution framework.

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Research on Cloud Computing Resources Provisioning Based on Reinforcement Learning

Cited by 11 publications

References 14 publications

UDL: a cloud task scheduling framework based on multiple deep neural networks

UDL: a cloud task scheduling framework based on multiple deep neural networks

A Business Management Resource-Scheduling Method based on Deep Learning Algorithm

Optimized Speculative Execution to Improve Performance of MapReduce Jobs on Virtualized Computing Environment

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