A water cycle optimized wavelet neural network algorithm for demand prediction in cloud computing

Jeddi, Sima; Sharifian, Saeed

doi:10.1007/s10586-019-02916-2

Cited by 16 publications

(7 citation statements)

References 30 publications

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“…For financial time-series forecasting, [30] designed a RNN approach. For cloud workload forecasting, a wavelet neural network based technique is presented in [31] . For a multi-input and single-output set of samples, polynomial neural network or Group meta-data handling (GMDH) approach builds a fusion of models in a self-organized form [4] .…”

Section: Related Workmentioning

confidence: 99%

MAG-D: A multivariate attention network based approach for cloud workload forecasting

Patel

Bedi

2023

Future Generation Computer Systems

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Section: Related Workmentioning

confidence: 99%

MAG-D: A multivariate attention network based approach for cloud workload forecasting

Patel

Bedi

2023

Future Generation Computer Systems

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“…However, this algorithm cannot predict the future VM requests, but it does predict a better solution to solve the workflow scheduling problem, which cannot alleviate the resource allocation delay for the future VM request increases. A hybrid wavelet neural network method has been proposed to improve the prediction accuracy through training the wavelet neural network with two heuristic algorithms [34]. The machine learning-based prediction needs to conduct training using a large amount of data, which increases the time consumption and thus cannot guarantee a timely resource allocation.…”

Section: T Thmentioning

confidence: 99%

A proactive resource allocation method based on adaptive prediction of resource requests in cloud computing

Chen

Wang

Liu

2021

J Wireless Com Network

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With the development of big data and artificial intelligence, cloud resource requests present more complex features, such as being sudden, arriving in batches and being diverse, which cause the resource allocation to lag far behind the resource requests and an unbalanced resource utilization that wastes resources. To solve this issue, this paper proposes a proactive resource allocation method based on the adaptive prediction of the resource requests in cloud computing. Specifically, this method first proposes an adaptive prediction method based on the runs test that improves the prediction accuracy of resource requests, and then, it builds a multiobjective resource allocation optimization model, which alleviates the latency of the resource allocation and balances the utilizations of the different types of resources of a physical machine. Furthermore, a multiobjective evolutionary algorithm, the Nondominated Sorting Genetic Algorithm with the Elite Strategy (NSGA-II), is improved to further reduce the resource allocation time by accelerating the solution speed of the multiobjective optimization model. The experimental results show that this method realizes the balanced utilization between the CPU and memory resources and reduces the resource allocation time by at least 43% (10 threads) compared with the Improved Strength Pareto Evolutionary algorithm (SPEA2) and NSGA-II methods.

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“…Recently, cloud computing services became an integral part of any modern system among both corporations and individuals because of its vast and flexible facilities. Therefore, the huge computing demand can only be met by the cloud computing infrastructure which can lead to an evergrowing complexity to meet both quality of service and service level agreement [27]. Narayanan et al proposed an underground water distribution system based on an IoT architecture that is integrated with Fog computing.…”

Section: Related Workmentioning

confidence: 99%

A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks

2020

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In recent years, scarce water resources became one of the main problems that endanger human species existence and the advancement of any nation. In this research, smart water meters were implemented, distributed, and installed in a regional area in Cairo while data were collected at uniform intervals then sent to the cloud instantly. The solution paradigm uses an Internet of Things (IoT) based on micro-services and containers. The design incorporates real-time streaming and infrastructure performance optimization to store data. A second layer to analyze the acquired data was used to model water consumption using Long Short-Term Memory (LSTM). The designed LSTM is validated and tested to be utilized in the forecast of future water demand. Moreover, two alternative machine learning methods, namely Support Vector Regression and Random Forest commonly utilized in time series forecasting applications, were used for a comparative analysis of which LSTM has proven to be superior. The proper integration of the system elements is the key to the proposed system success. Based on the success of the designed system, it can be applicable on a national scale. That can enable the optimal management of consumers' demand and improve water infrastructure utilization. The proposed paradigm presents a testbed for various scenarios that can be used in water resources management.

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A water cycle optimized wavelet neural network algorithm for demand prediction in cloud computing

Cited by 16 publications

References 30 publications

MAG-D: A multivariate attention network based approach for cloud workload forecasting

MAG-D: A multivariate attention network based approach for cloud workload forecasting

A proactive resource allocation method based on adaptive prediction of resource requests in cloud computing

A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks

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