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.

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 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.

Cloud Computing

2012 ◽  
Vol 3 (2) ◽  
pp. 51-59 ◽  
Author(s):  
Nawsher Khan ◽  
A. Noraziah ◽  
Elrasheed I. Ismail ◽  
Mustafa Mat Deris ◽  
Tutut Herawan
Keyword(s):  
Cloud Computing ◽  
High Availability ◽  
Cloud Environment ◽  
Network Resource ◽  
Business World ◽  
Computing Services ◽  
Cloud Computing Services ◽  
Similarities And Differences ◽  
Comprehensive Classification ◽  
And Storage

Cloud computing is fundamentally altering the expectations for how and when computing, storage, and networking resources should be allocated, managed, consumed, and allow users to utilize services globally. Due to the powerful computing and storage, high availability and security, easy accessibility and adaptability, reliable scalability and interoperability, cost and time effective cloud computing is the top, needed for current fast growing business world. A client, organization or a trade that adopting emerging cloud environment can choose a well suitable infrastructure, platform, software, and a network resource, for any business, where each one has some exclusive features and advantages. The authors first develop a comprehensive classification for describing cloud computing architecture. This classification help in survey of several existing cloud computing services developed by various projects globally such as Amazon, Google, Microsoft, Sun and Force.com and by using this survey’s results the authors identified similarities and differences of the architecture approaches of cloud computing.

scholarly journals Secure Data Aggregation Scheme for IOT Applications in Cloud

2020 ◽  
Vol 9 (4S2) ◽  
pp. 70-76
Keyword(s):  
Cloud Computing ◽  
Data Hiding ◽  
Data Aggregation ◽  
Dynamic Network ◽  
Security And Privacy ◽  
Iot Applications ◽  
P Gene ◽  
Private Data ◽  
Aggregation Scheme ◽  
And Storage

While Internet of Things (IoT) technology comprises of nodes that are self-configuring and intelligent which are interconnected in a dynamic network, utilization of shared resources has been revolutionized by the cloud computing effectively reducing the cost overheadamong the cloud users.The major concerns of IoT infrastructure are reliability, performance, security and privacy. Cloud computing is popular for its unlimited storage and processing power. Cloud computing is much more matured with the capability to resolve most of the issues in IoT technology. A suitable way to address most of the issues in IoT technology is by integrating IoTparadigm into the Cloud technology.In this regard, we propose a methodology of applying our EPAS scheme for IoT applications. In our previous work[2] , we have proposed an Enhanced Privacy preserving gene based data Aggregation Scheme (EPAS) for private data transmission and storage by utilizing Enhanced P-Gene erasable data hiding approach. Enhanced P-Gene scheme ensures secure transmission and storage of private data by relying on a data aggregation scheme fully dependent on erasable data hiding technique. In the current work we analyse the applicability of the EPAS scheme for IoT applications. Experimental results show the suitability of the proposed scheme for application involving numeric data and also demonstrates performance improvement with existing proposals for data aggregation in cloud.

Current Trends in Cloud Computing for Data Science Experiments

2021 ◽  
Vol 11 (4) ◽  
pp. 80-99
Author(s):  
Syed Imran Jami ◽  
Siraj Munir
Keyword(s):  
Cloud Computing ◽  
Resource Sharing ◽  
Data Science ◽  
Job Scheduling ◽  
Resource Scheduling ◽  
Data Intensive ◽  
Organizational Issues ◽  
Current Trends ◽  
Recent Trends ◽  
And Storage

Recent trends in data-intensive experiments require extensive computing and storage resources that are now handled using cloud resources. Industry experts and researchers use cloud-based services and resources to get analytics of their data to avoid inter-organizational issues including power overhead on local machines, cost associated with maintaining and running infrastructure, etc. This article provides detailed review of selected metrics for cloud computing according to the requirements of data science and big data that includes (1) load balancing, (2) resource scheduling, (3) resource allocation, (4) resource sharing, and (5) job scheduling. The major contribution of this review is the inclusion of these metrics collectively which is the first attempt towards evaluating the latest systems in the context of data science. The detailed analysis shows that cloud computing needs research in its association with data-intensive experiments with emphasis on the resource scheduling area.

Transporting the Cloud

2014 ◽  
pp. 135-156
Author(s):  
Claudio Estevez
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Transport Layer ◽  
Mobile Cloud ◽  
Transport Protocols ◽  
End User ◽  
Advantages And Disadvantages ◽  
Processing Power ◽  
And Storage

Cloud computing is consistently proving to be the dominant architecture of the future, and mobile technology is the catalyst. By having the processing power and storage remotely accessible, the main focus of the terminal is now related to connectivity and user-interface. The success of cloud-based applications greatly depends on the throughput experienced by the end user, which is why transport protocols play a key role in mobile cloud computing. This chapter discusses the main issues encountered in cloud networks that affect connection-oriented transport protocols. These issues include, but are not limited to, large delay connections, bandwidth variations, power consumption, and high segment loss rates. To reduce these adverse effects, a set of proposed solutions are presented; furthermore, the advantages and disadvantages are discussed. Finally, suggestions are made for future mobile cloud computing transport-layer designs that address different aspects of the network, such as transparency, congestion-intensity estimation, and quality-of-service integration.

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