Neural network and regression based processor load prediction for efficient scaling of Grid and Cloud resources

Imam, Md. Toukir; Miskhat, Sheikh Faisal; Rahman, Rashedur M.; Amin, M. Ashraful

doi:10.1109/iccitechn.2011.6164809

Cited by 14 publications

(5 citation statements)

References 9 publications

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“…Ponokoan online manufacturing service for small scale production iv. Autodesk Forgecloud based software platform for manufacturing and product design v. OnShape -A cloud-based CAD system with API and solution providers for manufacturing The below-mentioned research work in the area of MaaS is presented in [57][58][59][60][61][62].…”

Section: Table2 Service Delivery Models In Cloud Manufacturingmentioning

confidence: 99%

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Machine Learning Algorithms in Cloud Manufacturing - A Review

Syed Omer Farooq Ahmed

2024

IJRITCC

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Cloud computing has advanced significantly in terms of storage, QoS, online service availability, and integration with conventional business models and procedures. The traditional manufacturing firm becomes Cloud Manufacturing when Cloud Services are integrated into the present production process. The capabilities of Cloud Manufacturing are enhanced by Machine Learning. A lot of machine learning algorithms provide the user with the desired outcomes. The main objectives are to learn more about the architecture and analysis of Cloud Manufacturing frameworks and the role that machine learning algorithms play in cloud computing in general and Cloud Manufacturing specifically. Machine learning techniques like SVM, Genetic Algorithm, Ant Colony Optimisation techniques, and variants are employed in a cloud environment.

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Section: Table2 Service Delivery Models In Cloud Manufacturingmentioning

confidence: 99%

“…In [60] -Collaborative smart process monitoring v. In [61] -Collaborative delivery of customized products vi. In [62] -Selling machine capacity The rest of the paper is organized as follows i.…”

Section: Imentioning

confidence: 99%

Machine Learning Algorithms in Cloud Manufacturing - A Review

Syed Omer Farooq Ahmed

2024

IJRITCC

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“…We download and extract the task load data and processor load data over a period of time from the public cloud server Parallel Workloads Archive (Feitelson 2001), and conduct some experiments with the BP neural network algorithm and the linear regression algorithm respectively, and calculate the mean absolute percentage error (MAPE) (Miskhat, Rahman,, & Amin 2011;Islam, Keung, Lee & Liu 2012) of the predicted results. The results of the experiment are as follows:…”

Section: Analysis and Comparison Of These Two Predictionsmentioning

confidence: 99%

“…With the continuous development of cloud computing, the resource scheduling on cloud has become a hot spot on current researches (Lin & Qi 2012;Beloglazov, Abawajy & Buyya 2012;Lin, Liang, Wang & Buyya 2014;Lin, Liu, Zhu & Qi 2013). In order to achieve the effective scheduling and management of cloud resources, some prediction technologies are used by many researchers (Wu, Cao & Li 2013;Zhang, Wu & Lü 2013;Zhou & Cao 2012;Mi, Wang, Yin & Shi 2011;Huang & Yu 2012;Miskhat, Rahman & Amin 2011;Zhang, Chen & Hu 2012;Islam, Keung, Lee & Liu 2012;Prevost, Nagothu, Kelley & Jamshidi 2011). By analysing the present prediction models, the paper (Wu, Cao, & Li 2013) divides the prediction model into three types: 1) basic prediction model, 2) prediction model based on feedback, 3) and prediction model of multiple time sequences.…”

Section: Introductionmentioning

confidence: 99%

“…However, this prediction method just can predict the future demand trends of resources in a relatively short period of time but not in a long period of time. In (Miskhat, Rahman & Amin 2011), the author proposed a prediction method of processor load for grid and efficient extension of cloud resources based on neural network and linear regression. The author uses both the delayed neural network and linear regression to predict the processor load, and compares this two prediction methods through experiment showing that the prediction method based on delayed neural network has a higher precision than the linear regression, and is more suitable to predict the processor load.…”

Section: Introductionmentioning

confidence: 99%

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A Self-Adaptive Prediction Algorithm for Cloud Workloads

Mao

Lin

et al. 2015

International Journal of Grid and High Performance Computing

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It is difficult to analyze the workload in complex cloud computing environments with a single prediction algorithm as each algorithm has its own shortcomings. A self-adaptive prediction algorithm combining the advantages of linear regression (LR) and a BP neural network to predict workloads in clouds is proposed in this paper. The main idea of the self-adaptive prediction algorithm is to choose the better prediction method of the future workload. Some experiments of prediction algorithms are conducted with workloads on the public cloud servers. The experimental results show that the proposed algorithm has a relatively high accuracy on the workload predictions compared with the BP neural network and LR. Furthermore, in order to use the proposed algorithm in a cloud data center, a dynamic scheduling architecture of cloud resources is designed to improve resource utilization and reduce energy consumption.

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