Hadoop MapReduce
Submission Deadline: Dec. 30, 2015
Lead Guest Editor
Department of Computer, Islamic Azad University, Yazd, Iran
Guest Editor
Guidelines for Submission
Manuscripts can be submitted until the expiry of the deadline. Submissions must be previously unpublished and may not be under consideration elsewhere.
Papers should be formatted according to the guidelines for authors (see: http://www.sciencepublishinggroup.com/journal/guideforauthors?journalid=238). By submitting your manuscripts to the special issue, you are acknowledging that you accept the rules established for publication of manuscripts, including agreement to pay the Article Processing Charges for the manuscripts. Manuscripts should be submitted electronically through the online manuscript submission system at http://www.sciencepublishinggroup.com/login. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal and will be listed together on the special issue website.
Published Papers
The special issue currently is open for paper submission. Potential authors are humbly requested to submit an electronic copy of their complete manuscript by clicking here.
Introduction
Hadoop is a Java-based programming framework that supports the storing and processing of large data sets in a distributed computing environment and it is very much appropriate for high volume of data. it's using HDFS for data storing and using MapReduce to processing that data. MapReduce is a popular programming model to support data-intensive applications using shared-nothing clusters. the main objective of MapReduce programming model is to parallelize the job execution across multiple nodes for execution. nowadays, all focus of the researchers and companies toward to Hadoop. due this, many scheduling algorithms have been proposed in the past decades. there are three important scheduling issues in mapreduce such as locality, synchronization and fairness. The most common objective of scheduling algorithms is to minimize the completion time of a parallel application and also achieve to these issues. This special issue focuses on new scheduling algorithms of hadoop MapReduce, scheduling issues and new trends in hadoop.

Aims and Scope:

1. Hadoop
2. Hadoop MapReduce issues
3. Scheduling algorithms of MapReduce
4. Hadoop problems
5. Benefits and risks of using Hadoop
6. Hadoop And Cloud services
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