Effect of Virtual Machine Deployment Policies on the Efficiency Data Processing Centers

Vasil’eva, Yu. O.; Костенко, В. А.; Chupakhin, Andrey A.

doi:10.1134/s1064230720030168

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…In the current third-party logistics services, the problem of low data processing efficiency caused by massive logistics data still limits the orderly development of logistics services, and it is very important to improve the data processing speed. Vasil'Eva et al (2020) pointed out that in order to simplify the management mode of the data processing center and improve the processing efficiency, it is necessary to apply the deployment strategy when developing the virtual machine.…”

Section: Discussionmentioning

confidence: 99%

Study on the application of big data techniques for the third-party logistics using novel support vector machine algorithm

Sun

Shi

2021

JEIM

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PurposeThis paper aims to effectively explore the application effect of big data techniques based on an α-support vector machine-stochastic gradient descent (SVMSGD) algorithm in third-party logistics, obtain the valuable information hidden in the logistics big data and promote the logistics enterprises to make more reasonable planning schemes.Design/methodology/approachIn this paper, the forgetting factor is introduced without changing the algorithm's complexity and proposed an algorithm based on the forgetting factor called the α-SVMSGD algorithm. The algorithm selectively deletes or retains the historical data, which improves the adaptability of the classifier to the real-time new logistics data. The simulation results verify the application effect of the algorithm.FindingsWith the increase of training times, the test error percentages of gradient descent (GD) algorithm, gradient descent support (SGD) algorithm and the α-SVMSGD algorithm decrease gradually; in the process of logistics big data processing, the α-SVMSGD algorithm has the efficiency of SGD algorithm while ensuring that the GD direction approaches the optimal solution direction and can use a small amount of data to obtain more accurate results and enhance the convergence accuracy.Research limitations/implicationsThe threshold setting of the forgetting factor still needs to be improved. Setting thresholds for different data types in self-learning has become a research direction. The number of forgotten data can be effectively controlled through big data processing technology to improve data support for the normal operation of third-party logistics.Practical implicationsIt can effectively reduce the time-consuming of data mining, realize the rapid and accurate convergence of sample data without increasing the complexity of samples, improve the efficiency of logistics big data mining, reduce the redundancy of historical data, and has a certain reference value in promoting the development of logistics industry.Originality/valueThe classification algorithm proposed in this paper has feasibility and high convergence in third-party logistics big data mining. The α-SVMSGD algorithm proposed in this paper has a certain application value in real-time logistics data mining, but the design of the forgetting factor threshold needs to be improved. In the future, the authors will continue to study how to set different data type thresholds in self-learning.

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Section: Discussionmentioning

confidence: 99%

Study on the application of big data techniques for the third-party logistics using novel support vector machine algorithm

Sun

Shi

2021

JEIM

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“…The literature [12] proposed a reputation-based VM deployment strategy to reduce the risk of coexistence attacks by evaluating the reputation of VMs and deploying VMs with high reputations on the same physical host. The literature [13] compared the impact of different VM deployment policies on the efficiency of data processing centers through simulation experiments, and he concluded that different VM deployment policies significantly affect the performance and resource utilization efficiency of data processing centers. The literature [14] proposed a VM deployment strategy based on queuing theory, which aims to optimize the deployment and scheduling of VMs and improve the resource utilization of cloud data centers.…”

Section: Introductionmentioning

confidence: 99%

Minimal cut of strongly connected directed random graphs studied in virtual machine deployment

Zhang

2023

Applied Mathematics and Nonlinear Sciences

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This paper first presents the basic concepts of random graph theory, including undirected, directed, and generalized random graphs. A random homomorphic number mapping is introduced to solve the cut distance problem of directed random graphs, and a minimum cut distance is proposed. Next, a virtual machine deployment strategy (VMDS) based on the minimum cut algorithm is proposed to construct VM clusters based on similarity and to cut VM clusters based on the minimum cut of directed random graphs. According to the results, the MAE (1.3875) and MSE (2.7783) of both the conventional AR algorithm and ES algorithm are the lowest. Compared to the MAE (1.7381) and MSE (3.7840) of the VMDS algorithm, both significantly decrease. The research results presented in this paper are useful for applying virtual machine deployment techniques in cloud computing.

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Effect of Virtual Machine Deployment Policies on the Efficiency Data Processing Centers

Cited by 2 publications

References 10 publications

Study on the application of big data techniques for the third-party logistics using novel support vector machine algorithm

Study on the application of big data techniques for the third-party logistics using novel support vector machine algorithm

Minimal cut of strongly connected directed random graphs studied in virtual machine deployment

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