A two-phase virtual machine placement policy for data-intensive applications in cloud

2021 ◽  
Vol 180 ◽  
pp. 103025
Author(s):  
Samaneh Sadegh ◽  
Kamran Zamanifar ◽  
Piotr Kasprzak ◽  
Ramin Yahyapour
Keyword(s):  
Virtual Machine ◽  
Virtual Machine Placement ◽  
Two Phase ◽  
Data Intensive ◽  
Placement Policy ◽  
Data Intensive Applications

Efficient Querying Distributed Big-XML Data using MapReduce

2016 ◽  
Vol 8 (3) ◽  
pp. 70-79 ◽  
Author(s):  
Song Kunfang ◽  
Hongwei Lu
Keyword(s):  
Data Storage ◽  
Two Phase ◽  
Xml Data ◽  
Data Intensive ◽  
Labeling Algorithm ◽  
Efficiency And Effectiveness ◽  
Hierarchical Index ◽  
Parallel Queries ◽  
Computing Framework ◽  
Data Intensive Applications

MapReduce is a widely adopted computing framework for data-intensive applications running on clusters. This paper proposed an approach to exploit data parallelisms in XML processing using MapReduce in Hadoop. The authors' solution seamlessly integrates data storage, labeling, indexing, and parallel queries to process a massive amount of XML data. Specifically, the authors introduce an SDN labeling algorithm and a distributed hierarchical index using DHTs. More importantly, an advanced two-phase MapReduce solution are designed that is able to efficiently address the issues of labeling, indexing, and query processing on big XML data. The experimental results show the efficiency and effectiveness of the proposed parallel XML data approach using Hadoop.

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