A Content-wise Data Placement Policy for Improving the Performance of MapReduce-based Video Processing Applications in Cloud Computing

2020 ◽  
Author(s):  
Eihab SaatiAlsoruji
Keyword(s):  
Cloud Computing ◽  
Video Processing ◽  
Data Placement ◽  
Placement Policy

Data Placement and Query for Cloud Computing Based on MyHeawood Network

2014 ◽  
Vol 543-547 ◽  
pp. 3100-3104
Author(s):  
Xin Huang ◽  
Yu Xing Peng ◽  
Peng Fei You
Keyword(s):  
Cloud Computing ◽  
Interconnection Network ◽  
Cost Effective ◽  
Data Placement ◽  
Cloud Services ◽  
Massive Data ◽  
Massive Datasets ◽  
The Family ◽  
The Cost ◽  
Hashing Algorithm

The massive data in Data centers network will be frequently accessed massive datasets for cloud services, which will lead to some new requirements and becomes an important issue for interconnection topology and data management in cloud computing. According to the cost-effective, the paper proposes a new interconnection network MyHeawood for cloud computing. MyHeawood is constructed by small switches and servers with dual-port NIC according to recursive method. The data placement strategy in MyHeawood is a hashing algorithm based on the family of hash functions. MyHeawood uses three replicas strategy base on master copy, which is allocated in different sub layer to improve the reliability of data.

A High-Speed Railway Data Placement Strategy Based on Cloud Computing

2011 ◽  
Vol 135-136 ◽  
pp. 43-49
Author(s):  
Han Ning Wang ◽  
Wei Xiang Xu ◽  
Chao Long Jia
Keyword(s):  
Cloud Computing ◽  
High Speed ◽  
Data Access ◽  
Interval Mapping ◽  
Data Placement ◽  
Programming Algorithm ◽  
High Speed Railway ◽  
Mapping Algorithm ◽  
Data Intensive ◽  
Study Results

The application of high-speed railway data, which is an important component of China's transportation science data sharing, has embodied the typical characteristics of data-intensive computing. A reasonable and effective data placement strategy is needed to deploy and execute data-intensive applications in the cloud computing environment. Study results of current data placement approaches have been analyzed and compared in this paper. Combining the semi-definite programming algorithm with the dynamic interval mapping algorithm, a hierarchical structure data placement strategy is proposed. The semi-definite programming algorithm is suitable for the placement of files with various replications, ensuring that different replications of a file are placed on different storage devices. And the dynamic interval mapping algorithm could guarantee better self-adaptability of the data storage system. It has been proved both by theoretical analysis and experiment demonstration that a hierarchical data placement strategy could guarantee the self-adaptability, data reliability and high-speed data access for large-scale networks.

scholarly journals Data Placement in Era of Cloud Computing: a Survey, Taxonomy and Open Research Issues

2019 ◽  
Vol 20 (2) ◽  
pp. 377-398 ◽  
Author(s):  
Avinash Kaur ◽  
Pooja Gupta ◽  
Manpreet Singh ◽  
Anand Nayyar
Keyword(s):  
Cloud Computing ◽  
Workflow Management ◽  
Data Placement ◽  
Future Research ◽  
Research Directions ◽  
Research Issues ◽  
Cloud Computing Environment ◽  
Factors Affecting ◽  
Complete Survey ◽  
Future Research Directions

In cloud computing, data placement is a critical operation performed as part of workflow management and aims to find the best physical machine to place the data. It has direct impact on performance, cost and execution time of workflows. Number of data placement algorithms is designed in cloud computing environment that aimed to improve various factors affecting the workflows and their execution including the movement of data among data centers. This paper provides a complete survey and analyses of existing data placement schemes proposed in literature for cloud computing. Further, it classifies data placement schemes based on their assess capabilities and objectives. Further objectives and properties of data placement schemes are compared. Finally future research directions are provided with concluding remarks.

scholarly journals MiCADO-Edge: Towards an Application-level Orchestrator for the Cloud-to-Edge Computing Continuum

2021 ◽  
Vol 19 (4) ◽  
Author(s):  
Amjad Ullah ◽  
Huseyin Dagdeviren ◽  
Resmi C. Ariyattu ◽  
James DesLauriers ◽  
Tamas Kiss ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Analysis ◽  
Video Processing ◽  
Time Management ◽  
Realistic Case ◽  
Healthcare Data ◽  
Computing Environments ◽  
Management Policies ◽  
Run Time ◽  
Monitoring Information

AbstractAutomated deployment and run-time management of microservices-based applications in cloud computing environments is relatively well studied with several mature solutions. However, managing such applications and tasks in the cloud-to-edge continuum is far from trivial, with no robust, production-level solutions currently available. This paper presents our first attempt to extend an application-level cloud orchestration framework called MiCADO to utilise edge and fog worker nodes. The paper illustrates how MiCADO-Edge can automatically deploy complex sets of interconnected microservices in such multi-layered cloud-to-edge environments. Additionally, it shows how monitoring information can be collected from such services and how complex, user- defined run-time management policies can be enforced on application components running at any layer of the architecture. The implemented solution is demonstrated and evaluated using two realistic case studies from the areas of video processing and secure healthcare data analysis.

scholarly journals FCA-based Energy Aware-data Placement Strategy for Intensive Workflow in Cloud Computing.

2019 ◽  
Vol 159 ◽  
pp. 387-397
Author(s):  
Rihab Derouiche ◽  
Zaki Brahmi ◽  
Mohamed Mohsen Gammoudi
Keyword(s):  
Cloud Computing ◽  
Data Placement ◽  
Energy Aware
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