Research on information classification and storage in cloud computing data center based on group collaboration intelligent clustering

2021 ◽  
pp. 1-10
Author(s):  
Linlin Zhang ◽  
Sujuan Zhang
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Information Acquisition ◽  
Clustering Algorithm ◽  
Loss Rate ◽  
Information Classification ◽  
Storage Location ◽  
Long Time ◽  
Optimization Function ◽  
And Storage

In order to overcome the problems of long time and low accuracy of traditional methods, a cloud computing data center information classification and storage method based on group collaborative intelligent clustering was proposed. The cloud computing data center information is collected in real time through the information acquisition terminal, and the collected information is transmitted. The optimization function of information classification storage location was constructed by using the group collaborative intelligent clustering algorithm, and the optimal solutions of all storage locations were evolved to obtain the elite set. According to the information attribute characteristics, different information was allocated to different elite sets to realize the classified storage of information in the cloud computing data center. The experimental results show that the longest time of information classification storage is only 0.6 s, the highest information loss rate is 10.0%, and the highest accuracy rate is more than 80%.

scholarly journals REVERT: A Network Failure Recovery Method for Data Center Networks

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1187
Author(s):  
Yunhe Cui ◽  
Qing Qian ◽  
Guowei Shen ◽  
Chun Guo ◽  
Saifei Li
Keyword(s):  
Data Center ◽  
Clustering Algorithm ◽  
High Reliability ◽  
Failure Recovery ◽  
Data Center Networks ◽  
Recovery Method ◽  
Link Failures ◽  
Network Failure ◽  
Network Failures ◽  
And Storage

As a repository that holds computing facilities, storage facilities, network facilities and other facilities, the Software Defined Data Center (SDDC) can provide computing and storage resources for users. For a SDDC, it is important to provide continuous services for users. Hence, in order to achieve high reliability in Software Defined Data Center Networks (SDDCNs), a network failure recovery method for software defined data center networks (REVERT) is proposed to recover failures in SDDCNs. In REVERT, the network failures that occurred in SDDCNs are classified into three types, which are switch failure, failure of links among switches and failure of links between switches and servers. Specially, except recovering the switch failure and failure of links between switches, REVERT can also recover the failures of links between the switches and servers. To achieve that, a failure preprocessing method used to classify the network failures, a data structure for storing and finding the affected flows, a server cluster agent for communicating with the server clustering algorithm and a routing path calculation method are designed in REVERT. Meanwhile, REVERT has been implemented and evaluated on RYU controller and Mininet using three routing algorithms. Compared with the link usage before recovering the network failures, when there are more than 200 flows in the network, the mean link usages only slightly increase at about 1.83 percent. More importantly, the evaluation results also demonstrate that except recovering switch failures, intra-topo link failures, REVERT has the ability of recovering failures of links between servers and edge switches successfully.

Clustering Columns of the Wide-Table in Cloud Computing

2012 ◽  
Vol 433-440 ◽  
pp. 5129-5135
Author(s):  
Bin Huang ◽  
Yu Xing Peng
Keyword(s):  
Cloud Computing ◽  
Clustering Algorithm ◽  
Web Applications ◽  
Second Law Of Thermodynamics ◽  
Access Time ◽  
Materialized Views ◽  
It Use ◽  
Consistent Hashing ◽  
Single Column ◽  
And Storage

Various data-centric web applications are becoming the developing trend of information society. Cloud computing currently adopt column-oriented storage wide table to represent the heterogeneous structured data of these applications. The wide table reduces the waste of storage space, but slows down query efficiency. The paper implements the hybrid partition on access frequent (HPAF) to horizontally and vertically partition a wide table. It uses a variant of consistent hashing to dynamically horizontally partition a wide table across multiple storage nodes on each node’s performance; It use entropy to represent the number of reducing access data block from the table with N columns than from N column-oriented storage tables. According to the second law of thermodynamics, the paper designs an entropy increasing clustering algorithm to classify the columns of a wide table. The algorithm finds a cluster with multiple classes which save maximum access time. The paper implements an algorithm for structured query across multiple materialized views too. Lastly the paper demonstrates the query performance and storage efficiency of our strategy compared to single column storage.

A survey of data center consolidation in cloud computing systems

2021 ◽  
Vol 39 ◽  
pp. 100366
Author(s):  
Leila Helali ◽  
Mohamed Nazih Omri
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Computing Systems

scholarly journals Clustering-Based Component Fraction Estimation in Solid–Liquid Two-Phase Flow in Dredging Engineering

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5697
Author(s):  
Chang Sun ◽  
Shihong Yue ◽  
Qi Li ◽  
Huaxiang Wang
Keyword(s):  
Clustering Algorithm ◽  
Two Phase Flow ◽  
Low Cost ◽  
Fast Response ◽  
New Method ◽  
Phase Flow ◽  
Reference Distribution ◽  
Two Phase ◽  
Long Time ◽  
Solid Liquid

Component fraction (CF) is one of the most important parameters in multiple-phase flow. Due to the complexity of the solid–liquid two-phase flow, the CF estimation remains unsolved both in scientific research and industrial application for a long time. Electrical resistance tomography (ERT) is an advanced type of conductivity detection technique due to its low-cost, fast-response, non-invasive, and non-radiation characteristics. However, when the existing ERT method is used to measure the CF value in solid–liquid two-phase flow in dredging engineering, there are at least three problems: (1) the dependence of reference distribution whose CF value is zero; (2) the size of the detected objects may be too small to be found by ERT; and (3) there is no efficient way to estimate the effect of artifacts in ERT. In this paper, we proposed a method based on the clustering technique, where a fast-fuzzy clustering algorithm is used to partition the ERT image to three clusters that respond to liquid, solid phases, and their mixtures and artifacts, respectively. The clustering algorithm does not need any reference distribution in the CF estimation. In the case of small solid objects or artifacts, the CF value remains effectively computed by prior information. To validate the new method, a group of typical CF estimations in dredging engineering were implemented. Results show that the new method can effectively overcome the limitations of the existing method, and can provide a practical and more accurate way for CF estimation.

scholarly journals Optimization of Network Carbon Capture and Storage System (CCS) Using Mathematical Approach

2015 ◽  
Vol 9 (7) ◽  
pp. 161
Author(s):  
David Licindo ◽  
Arinne Christin Paramudita ◽  
Renanto Handogo ◽  
Juwari Purwo Sutikno
Keyword(s):  
Carbon Capture ◽  
Storage System ◽  
Carbon Capture And Storage ◽  
Exact Result ◽  
Maximum Load ◽  
Mathematical Approach ◽  
Source To Sink ◽  
Storage Location ◽  
The Cost ◽  
And Storage

Carbon capture and storage (CCS) is one of the technologies to reduce greenhouse gas emissions (GHG) tocapture of CO2 from the flue gas of a power plant that typically use coal as a Source of energy and then store it ina suitable geological storage (in specific locations). In practice, these sites may not be readily available forstorage at the same time that the Sources (GHG producing) are operating which gives rise to multi – periodplanning problems. This study presents a mathematical approach by considering constraints limit flowratereceived by Sink, various time availability of Sink and Source and calculation with the purpose to determine theminimum cost network which is getting the maximum load that is exchanged from Source to Sink. Illustrativecase studies are given to demonstrate the application of mathematical models to obtained with the exact result ofthe exchange network from Source to Sink. Derived from network obtained from the calculation of theMaximum Load Source to Sink and results may vary in accordance with the limitations that exist in themathematical model. The case study has been prepared with 2 cases, first 6 Source and 3 Sink with value ofSource Load is greater than the amount available on the Sink. Also, second case is 2 Source and 5 Sinkwithvalue of Source Load is smaller than the amount available on the Sink. In addition, Case Studies tominimize the cost of pipeline construction and distribution of CO2 by plant and storage location determination inJava. Flowrate restriction factor that goes into Sink, Source and Sink establishment time and cost are taken intoaccount can affect the networks that can be exchanged from the Source to the Sink.

Data center network architecture in cloud computing: review, taxonomy, and open research issues

2014 ◽  
Vol 15 (9) ◽  
pp. 776-793 ◽  
Author(s):  
Han Qi ◽  
Muhammad Shiraz ◽  
Jie-yao Liu ◽  
Abdullah Gani ◽  
Zulkanain Abdul Rahman ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Network Architecture ◽  
Data Center Network ◽  
Research Issues ◽  
Open Research

scholarly journals A Survey on Mobile Edge Computing: Focusing on Service Adoption and Provision

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Kai Peng ◽  
Victor C. M. Leung ◽  
Xiaolong Xu ◽  
Lixin Zheng ◽  
Jiabin Wang ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Mobile Networks ◽  
Edge Computing ◽  
Computation Offloading ◽  
Battery Life ◽  
Mobile Edge Computing ◽  
Service Adoption ◽  
Comprehensive Survey ◽  
And Storage ◽  
Execution Delay

Mobile cloud computing (MCC) integrates cloud computing (CC) into mobile networks, prolonging the battery life of the mobile users (MUs). However, this mode may cause significant execution delay. To address the delay issue, a new mode known as mobile edge computing (MEC) has been proposed. MEC provides computing and storage service for the edge of network, which enables MUs to execute applications efficiently and meet the delay requirements. In this paper, we present a comprehensive survey of the MEC research from the perspective of service adoption and provision. We first describe the overview of MEC, including the definition, architecture, and service of MEC. After that we review the existing MUs-oriented service adoption of MEC, i.e., offloading. More specifically, the study on offloading is divided into two key taxonomies: computation offloading and data offloading. In addition, each of them is further divided into single MU offloading scheme and multi-MU offloading scheme. Then we survey edge server- (ES-) oriented service provision, including technical indicators, ES placement, and resource allocation. In addition, other issues like applications on MEC and open issues are investigated. Finally, we conclude the paper.

scholarly journals Self-Adaptive K-Means Based on a Covering Algorithm

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Yiwen Zhang ◽  
Yuanyuan Zhou ◽  
Xing Guo ◽  
Jintao Wu ◽  
Qiang He ◽  
...  
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Real Data ◽  
Second Phase ◽  
Data Sets ◽  
Number Of Clusters ◽  
Large Scale Data ◽  
Long Time ◽  
Two Phases ◽  
Selection Of

The K-means algorithm is one of the ten classic algorithms in the area of data mining and has been studied by researchers in numerous fields for a long time. However, the value of the clustering number k in the K-means algorithm is not always easy to be determined, and the selection of the initial centers is vulnerable to outliers. This paper proposes an improved K-means clustering algorithm called the covering K-means algorithm (C-K-means). The C-K-means algorithm can not only acquire efficient and accurate clustering results but also self-adaptively provide a reasonable numbers of clusters based on the data features. It includes two phases: the initialization of the covering algorithm (CA) and the Lloyd iteration of the K-means. The first phase executes the CA. CA self-organizes and recognizes the number of clusters k based on the similarities in the data, and it requires neither the number of clusters to be prespecified nor the initial centers to be manually selected. Therefore, it has a “blind” feature, that is, k is not preselected. The second phase performs the Lloyd iteration based on the results of the first phase. The C-K-means algorithm combines the advantages of CA and K-means. Experiments are carried out on the Spark platform, and the results verify the good scalability of the C-K-means algorithm. This algorithm can effectively solve the problem of large-scale data clustering. Extensive experiments on real data sets show that the accuracy and efficiency of the C-K-means algorithm outperforms the existing algorithms under both sequential and parallel conditions.

Pretesting the Channels of Distribution for a Nutrition Education Leaflet

1974 ◽  
Vol 2 (3) ◽  
pp. 135-140 ◽  
Author(s):  
Leena Räsänen ◽  
Antti Ahlström ◽  
Matti Rimpelä
Keyword(s):  
Nutrition Education ◽  
Information Acquisition ◽  
Loss Rate ◽  
Telephone Interview ◽  
Relative Effectiveness ◽  
Information Campaign ◽  
Telephone Number ◽  
Interview Method ◽  
Local Newspaper ◽  
Circular Letter

In connection with an extensive health education project in Finland, the so-called North Karelia Project, a pretest program was carried out with the object of studying the relative effectiveness of three different channels as disseminators of a nutrition education leaflet addressed to housewives. A total of 256 20–49-year-old housewives were interviewed in the investigation. The telephone interview method was shown to be suitable for this type of information acquisition despite the problems arising in telephone number sampling. Statistically significant differences were noted between the channels used. Almost half of the leaflets taken home from school by pupils failed to reach the housewife, whereas the loss rate in cases where the leaflet was sent as a circular letter or as a supplement to the local newspaper was below 30%. Although the majority of all those who received the leaflet said that they had read it, only a quarter of these could be said to have familiarized themselves with the contents of the leaflet. There was only a weak correlation between background variables and reading of the leaflet or recall of its contents. The results indicate that the efficacy of distributing single educational leaflets is questionable, but the use of leaflets could be defended as part of a largescale information campaign.

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