ARIMA-Based Aging Prediction Method for Cloud Server System

With the rapid development of artificial intelligence, various machine learning algorithms have been widely used in the task of football match result prediction and have achieved certain results. However, traditional machine learning methods usually upload the results of previous competitions to the cloud server in a centralized manner, which brings problems such as network congestion, server computing pressure and computing delay. This paper proposes a football match result prediction method based on edge computing and machine learning technology. Specifically, we first extract some game data from the results of the previous games to construct the common features and characteristic features, respectively. Then, the feature extraction and classification task are deployed to multiple edge nodes.Finally, the results in all the edge nodes are uploaded to the cloud server and fused to make a decision. Experimental results have demonstrated the effectiveness of the proposed method.

Download Full-text

Availability Assessment of a Cloud Server System: Comparing Markov and Semi-Markov Models

10.1109/idaacs53288.2021.9661052 ◽

2021 ◽

Author(s):

Oleg Ivanchenko ◽

Vyacheslav Kharchenko ◽

Boris Moroz ◽

Yuriy Ponochovnyi ◽

Larysa Degtyareva

Keyword(s):

Markov Models ◽

Cloud Server ◽

Availability Assessment ◽

Server System

Download Full-text

Rancang Bangun Local Cloud Server Dengan NextCloud Pada Centos 7 Di SRH Training Center

JUTSI (Jurnal Teknologi dan Sistem Informasi) ◽

10.33330/jutsi.v1i1.1045 ◽

2021 ◽

Vol 1 (1) ◽

pp. 39-44

Author(s):

Ifvan Limalasa Mayendra ◽

Herman Saputra ◽

Uswatun Hasanah

Keyword(s):

Local Area Network ◽

Web Server ◽

Local Area ◽

Teaching Staff ◽

Area Network ◽

Training Center ◽

Computer Laboratory ◽

Cloud Server ◽

Local Cloud ◽

Server System

Abstract: The file becomes an important thing in any case. Especially in the world of education which is something that can’t be avoided that students must be able to create and save files correctly, both assignments and reports. Files stored on a computer do not guarantee that the data will be forever stored, because every month a computer maintenance must be held in a computer laboratory. Local cloud server technology with Nextcloud that uses the CentOs 7 operating system is very suitable to be applied in the SRH Training Center laboratory as a means of storing files and managing files in a Local Area Network. In this study the local cloud server was built with apache web server and mysql database and also with several packages supporting the Nextcloud application. With the construction of a local cloud server system with Nextcloud, students and teaching staff can easily manage the required learning files. Keywords: CentOs 7, Local Area Network, Local Cloud Server, Nextcloud, Web Server. Abstrak: File menjadi suatu hal yang penting dalam hal apapun. Apalagi di dunia pendidikan yang menjadi suatu hal yang tidak bisa dihindari bahwa siswa harus mampu membuat dan menyimpan file dengan benar, baik itu tugas maupun laporan. File yang disimpan dalam komputer tidak menjamin data akan selamanya tersimpan, karena setiap bulan pasti diadakan maintenance komputer yang ada di laboratorium komputer. Teknologi local cloud server dengan Nextcloud yang menggunakan sistem operasi CentOs 7 sangat cocok diterapkan dalam laboratorium SRH Training Center sebagai sarana penyimpanan file dan memanajemen file dalam satu jaringan Local Area Network. Pada penelitian ini local cloud server dibangun dengan web server apache dan database mysql dan juga dengan beberapa paket pendukung aplikasi Nextcloud. Dengan dibangunnya sistem local cloud server dengan Nextcloud, maka siswa-siswi dan tenaga pengajar dapat mudah dalam memanejemen file pembelajaran yang dibutuhkan. Kata Kunci: CentOs 7, Local Area Network, Local Cloud Server, Nextcloud, Web Server.

Download Full-text

Analytical Modeling and Optimization of an Elastic Cloud Server System

From Parallel to Emergent Computing ◽

10.1201/9781315167084-2 ◽

2019 ◽

pp. 31-48

Author(s):

Keqin Li

Keyword(s):

Analytical Modeling ◽

Modeling And Optimization ◽

Cloud Server ◽

Server System

Download Full-text

An efficient mobility prediction model for resource allocation in mobile cloud computing

International Journal of Knowledge-based and Intelligent Engineering Systems ◽

10.3233/kes-210060 ◽

2021 ◽

Vol 25 (1) ◽

pp. 149-157

Author(s):

Praveena Akki ◽

V. Vijayarajan

Keyword(s):

Cloud Computing ◽

Mobile Cloud Computing ◽

Path Loss ◽

Prediction Method ◽

Access Point ◽

Mobile Cloud ◽

Mobility Prediction ◽

Cloud Server ◽

History Of ◽

Cloud Resources

Mobile cloud computing (MCC) is a technology which provides cloud server resources to mobile users with optimized latency. MCC allows mobile device to access cloud resources and to offload tasks to cloud let servers at any time and from anywhere. The cloud let servers are attached to wireless Access points. But mobility plays an important role which leads to the loss of connectivity of mobile devices because of varying signal strengths. On the other hand, optimal code execution is a challenge. In this paper, a connectivity base mobility prediction method is proposed to assign the cloud resources to the user without loss in the connection. The past accessing history of the users and path loss factors are taken into consideration to predict proper access point. From the performance evaluation the performance of the proposed method is increased by 15.23% when compared to other existing methods.

Download Full-text

Client-server system

AccessScience ◽

10.1036/1097-8542.757549 ◽

2015 ◽

Keyword(s):

Client Server ◽

Server System

Download Full-text

Selection of a leading edge noise prediction method for PNoise

The Academic Society Journal ◽

10.32640/tasj.2018.4.214 ◽

2018 ◽

pp. 214-223

Author(s):

AM Faria ◽

MM Pimenta ◽

JY Saab Jr. ◽

S Rodriguez

Keyword(s):

Wind Turbine ◽

Turbine Blades ◽

Prediction Method ◽

Leading Edge ◽

Wind Farms ◽

Broadband Noise ◽

Turbulence Energy ◽

Noise Prediction ◽

Migration Routes ◽

Turbulent Inflow

Wind energy expansion is worldwide followed by various limitations, i.e. land availability, the NIMBY (not in my backyard) attitude, interference on birds migration routes and so on. This undeniable expansion is pushing wind farms near populated areas throughout the years, where noise regulation is more stringent. That demands solutions for the wind turbine (WT) industry, in order to produce quieter WT units. Focusing in the subject of airfoil noise prediction, it can help the assessment and design of quieter wind turbine blades. Considering the airfoil noise as a composition of many sound sources, and in light of the fact that the main noise production mechanisms are the airfoil self-noise and the turbulent inflow (TI) noise, this work is concentrated on the latter. TI noise is classified as an interaction noise, produced by the turbulent inflow, incident on the airfoil leading edge (LE). Theoretical and semi-empirical methods for the TI noise prediction are already available, based on Amiet’s broadband noise theory. Analysis of many TI noise prediction methods is provided by this work in the literature review, as well as the turbulence energy spectrum modeling. This is then followed by comparison of the most reliable TI noise methodologies, qualitatively and quantitatively, with the error estimation, compared to the Ffowcs Williams-Hawkings solution for computational aeroacoustics. Basis for integration of airfoil inflow noise prediction into a wind turbine noise prediction code is the final goal of this work.

Download Full-text