A scheduling scheme in the cloud computing environment using deep Q-learning

Tong, Zhao; Chen, Hongjian; Li, Xiaoxue; Li, Kenli; Li, Keqin

doi:10.1016/j.ins.2019.10.035

Cited by 171 publications

(72 citation statements)

References 32 publications

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“…Turkish researchers are also concerned with the use of AI in predicting human behavior and social exchange (Abubakar et al, 2019). Zhao Tong et al (2020) offer new approaches to using AI in the cloud computing environment based on Q-learning. K.-R. Koch and J.M.…”

Section: Literature Reviewmentioning

confidence: 99%

The impact of the pandemic on the quality of education and the image of a university

Vinichenko¹,

Vinogradova²,

Nikiporets-Takigawa³

et al. 2021

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The objective of the article is to identify the nature of the impact of the pandemic on the quality of education and the image of a university that uses distance education and artificial intelligence. The research methodology includes a complex of general scientific and special methods. A questionnaire and detailed interview have been formed with regard to the indicators of the leading rating systems of higher education. The data obtained have been summarized and analyzed using a focus group. The regression analysis has been used to examine the connection between students' motives for making life easier in the context of the pandemic and distance education and the dangers of the professor-student-administration system. The sustainable links and tendencies of changes in the quality of education and the image of a university have been revealed. The following traps for students are referred to the stable connections: the absence of a valid system of control over the authorship of the executed works; the possibility of the unauthorized use of various electronic sources during the answer; the coronavirus quarantine leads to the erasure of the boundaries between the study and everyday life, personal space and social environment of students; the growth of students' desire to have high marks in the subjects with the decrease of interest to study. Tendencies: the increase of the workload of professors and supporting (technical) staff; constant redistribution of time spent on the performance of official duties with a different degree of efficiency; the growth of willingness, a desire of the administration and university professors to improve the competitiveness of the university as a high-tech educational organization, included in the world map of science; the growth of dissatisfaction with the distance form of education, the degree of efficiency of return on investment in human development; the decrease in the responsibility of students to acquire knowledge through distance learning. The practical relevance of the work lies in the possibility of considering the identified sustainable links and tendencies to improve the education system at both national and global levels. This will enable attention to problem areas at the local level to achieve their overcoming with minimal financial and human losses through proactive actions. The theoretical relevance of the work lies in adding scientific and methodological elements to the theoretical foundations of the concept of development of higher education. The authors studied for the first time the problems of the impact of the COVID-19 pandemic on the quality of education and the image of a university. The originality of the research is determined by the original methodological model, which allowed the participants of the educational process to assess the essence of the ongoing changes associated with the coronavirus quarantine and with the help of an expert group to identify sustainable links and tendencies in changes in the quality of education and the image of a university

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Section: Literature Reviewmentioning

confidence: 99%

The impact of the pandemic on the quality of education and the image of a university

Vinichenko¹,

Vinogradova²,

Nikiporets-Takigawa³

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Since its introduction by Mnih et al [40] , DRL has been mainly used in-game scenarios to accelerate the convergence rate of the games. With the breakthrough of DRL [41] in the field of Artificial Intelligence (AI), it has been gradually applied to various industries as well.…”

Section: Smart Transportationmentioning

confidence: 99%

A survey on algorithms for intelligent computing and smart city applications

Tong

Feng

Yan

et al. 2021

Big Data Min. Anal.

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With the rapid development of human society, the urbanization of the world's population is also progressing rapidly. Urbanization has brought many challenges and problems to the development of cities. For example, the urban population is under excessive pressure, various natural resources and energy are increasingly scarce, and environmental pollution is increasing, etc. However, the original urban model has to be changed to enable people to live in greener and more sustainable cities, thus providing them with a more convenient and comfortable living environment. The new urban framework, the smart city, provides excellent opportunities to meet these challenges, while solving urban problems at the same time. At this stage, many countries are actively responding to calls for smart city development plans. This paper investigates the current stage of the smart city. First, it introduces the background of smart city development and gives a brief definition of the concept of the smart city. Second, it describes the framework of a smart city in accordance with the given definition. Finally, various intelligent algorithms to make cities smarter, along with specific examples, are discussed and analyzed.

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“…Workflow scheduling methods are still limited in aspects such as task dependency. Zhao Tong et al [25] proposed an intelligent method called deep Q learning task scheduling (DQTS) to solve the problem of directed acyclic graph (DAG) task scheduling. Wang et al [26] realized multi-objective workflow scheduling based on multi-DQN agent reinforcement learning.…”

Section: A Research On Job Schedulingmentioning

confidence: 99%

A Two-Stage Framework for the Multi-User Multi-Data Center Job Scheduling and Resource Allocation

et al. 2020

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With the development of artificial intelligence and the Internet of things, the prospects of cloud computing applications have become broader, and the number of users and cloud data centers (CDCs) has exploded. It is a challenge to realize efficient job scheduling and resource allocation of multiple users and data centers. However, the traditional scheduling model based on heuristic algorithm has some limitations in the complex and changeable cloud environment. In addition, many existing single-agent models rarely consider the multi-objective global optimization of the system. Therefore, this paper proposes a two-stage job scheduling and resource allocation framework that adopts multiple intelligent schedulers to solve the cooperative scheduling problem between job scheduling and resource allocation. A heterogeneous distributed deep learning (HDDL) model is used in the job scheduling stage to schedule multiple jobs to multiple cloud data centers. The deep Q-network (DQN) model is a resource scheduler to deploy virtual machine to physical servers for execution. Extensive numerical results show that both HDDL-baesd job scheduler and DQN-based resource allocator outperform the benchmark algorithm in terms of optimizing energy consumption and job delay. Furthermore, the proposed framework not only can achieve a global near-optimum by achieving local optimization at each stage but also has good scalability and low computation delay.

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A scheduling scheme in the cloud computing environment using deep Q-learning

Cited by 171 publications

References 32 publications

The impact of the pandemic on the quality of education and the image of a university

The impact of the pandemic on the quality of education and the image of a university

A survey on algorithms for intelligent computing and smart city applications

A Two-Stage Framework for the Multi-User Multi-Data Center Job Scheduling and Resource Allocation

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