DBN Based Cloud Service Response Time Prediction Method

Gao, Yan; Zhang, Bin; Wang, Si; Ma, Anxiang

doi:10.23919/icact.2019.8701922

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…When there are multiple hidden layers, the efficiency of back propagation will be greatly reduced. Therefore, this paper uses a DBN with multiple hidden layers for soft measurement modeling [17,18]. A DBN can learn complex data features by combining multi-layer networks to realize multi-level representation learning and represent the implicit information of data, thus making the prediction more accurate.…”

Section: Overall Architecture Of the Proposed Methodsmentioning

confidence: 99%

A Soft Measurement Method for the Tail Diameter in the Growing Process of Czochralski Silicon Single Crystals

Jiang,

Teng,

Zhao

2024

Applied Sciences

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In the Czochralski silicon single crystal growth process, the tail diameter is a key parameter that cannot be directly measured. In this paper, we propose a real-time soft measurement method that combines a deep belief network (DBN) and a support vector regression (SVR) network based on system identification to accurately predict the crystal diameter. The main steps of the proposed method are as follows: First, we address the delay problem of the effects of the temperature and crystal pulling speed on the tail diameter growth by using a back propagation (BP) neural network based on the mean impact value (MIV) method to determine the optimal delay time. Second, we construct a prediction model of the tail diameter by using the DBN network with the temperature and crystal pulling speed as input variables in the crystal growth process. Third, we improve the DBN network by using the SVR network to enhance its linear regression capability. We also employ the ant colony optimization (ACO) algorithm to obtain the optimal parameters of the SVR network. Finally, we compare the performance of the DBN-ACO-SVR network based on system identification with the DBN and SVR networks, and the results show that our method can effectively deal with the delay problem and achieve the accurate prediction of the tail diameter in the Czochralski silicon single crystal growth process.

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Section: Overall Architecture Of the Proposed Methodsmentioning

confidence: 99%

A Soft Measurement Method for the Tail Diameter in the Growing Process of Czochralski Silicon Single Crystals

Jiang,

Teng,

Zhao

2024

Applied Sciences

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“…And they proved the model had improved generalization capability especially when some features were missing or noisy [5]. Yan Gao et al applied the DBN algorithm to the problem of the performance prediction of cloud service, and proved its effectiveness by experiment [8]. Jinsheng Yang et al constructed a Wireless Local Area Network (WLAN) fingerprint location database with an improved DBN algorithm [22].…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Belief Network and Case Reasoning Based Decision Model for Emergency Rescue

Chang

Fan

Sun

2020

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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The frequent occurrence of major public emergencies in China has caused significant human and economic losses. To carry out successful rescue operations in such emergencies, decisions need to be made as efficiently as possible. Using earthquakes as an example of a public emergency, this paper combines the Deep Belief Network (DBN) and Case-Based Reasoning (CBR) models to improve the case representation and case retrieval steps in the decision-making process, then designs and constructs a decision-making model. The validity of the model is then verified by an example. The results of this study can be applied to maximize the efficiency of emergency rescue decisions.

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“…Several studies examined the performance of IaaS cloud using various performance metrics such as [11], [12], [13], [14] and [15]. Ref.…”

Section: Introductionmentioning

confidence: 99%

“…The performance assessment of an IaaS cloud, some performance metrics are derived using a taxonomy model. The performance analysis of the IaaS cloud involved CPU, memory [11], and response time [12] by adding the availability of cloud service [16] and throughput transfer parameters. This research referred to the taxonomy metrics metamodel for cloud services [17].…”

Section: Introductionmentioning

confidence: 99%

Peer to peer (P2P) and cloud computing on infrastructure as a service (IaaS) performance analysis

Ernawati¹,

Febiansyah²

2022

J.INFOTEL

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The resources of information technology and the availability of services on non-cloud network systems are limited. This constitutes problems for companies, especially in the efficient management of information technology. The high investment in infrastructure procurement is an obstacle in building centralized systems, including the adoption of cloud computing through Infrastructure as a Service (IaaS), as an elective solution. This research aims to analyze the performance of cloud servers on IaaS services using the parameters of cloud service availability, resource utilization, and throughput transfer which were implemented in companies engaged in the toll road concession sector. Furthermore, the results are expected to be a reference in supporting company decisions/policies related to cloud system adoption. The methodology involved the Network Development Life Cycle (NDLC), a system constituted by 6 (six) stages of management, namely user, proxy server, database, web service, monitoring service, and Remote Desktop Protocol (RDP). The results of cloud service availability indicate that the cloud system provides service availability (system interface, broad network access, and resource pooling). Furthermore, cloud systems have a significant performance on resource utilization (CPU) and throughput transfer parameters, while non-cloud systems only excel in response time and resource utilization (Memory) parameters. The overall result analysis based on this research scenario showed that the cloud system provides services according to user needs and has a better speed in data transmission, but has shortcomings in response time.

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DBN Based Cloud Service Response Time Prediction Method

Cited by 8 publications

References 10 publications

A Soft Measurement Method for the Tail Diameter in the Growing Process of Czochralski Silicon Single Crystals

A Soft Measurement Method for the Tail Diameter in the Growing Process of Czochralski Silicon Single Crystals

A Deep Belief Network and Case Reasoning Based Decision Model for Emergency Rescue

Peer to peer (P2P) and cloud computing on infrastructure as a service (IaaS) performance analysis

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