Map Reduce & Hadoop Scheduling Supported by EnFuzion

Axceleon, a leader in high performance cloud computing solutions for render farms, computational grids and cloud environments announces its new capability to run map reduce tasks.

Axceleon, a leader in high performance cloud computing solutions for render farms, computational grids and cloud environments announced today that's its EnFuzion product now supports optimized job scheduling on Map Reduce and Hadoop clusters.

MapReduce is a framework for processing extremely large datasets for certain kinds of problems that can be parallelized and typically run on a large number of computers or a cluster. MapReduce can work on unstructured data, a file system for example, or structured data, such as a database. MapReduce allows for distributed processing of the map and reduction operations and can sort a petabyte of data in only a few hours on a large cluster. MapReduce from Google, and Hadoop are used by many companies to provide internet data and sentiment based news seach capability, Google, Yahoo and Skygrid for example.

"EnFuzion has always had the capability to manage and control MapReduce and Hadoop clusters and now this is becoming more and more important to our customer base. This technology and capability has always existed in EnFuzion and we have done work recently to optimize the scheduling processes to maximize throughput and minimize response times", said Mary Keogh CEO of Axceleon.

EnFuzion can drive and manage one million jobs per minute and this performance makes it a good fit for this type of activity where it's best to keep the tasks small for better response times", commented Mary Keogh.


To learn more about EnFuzion please contact Axceleon at [email protected] or visit our website http://www.axceleon.com.

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Tags: Business, Cloud Computing, green technology, hadoop, high-performance, New, USA


About Axceleon Inc

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Meg Duffy
Press Contact, Axceleon Inc
Axceleon Inc
101 Main St, Suite 275 Huntington Beach, CA
Huntington Beach, CA 92648
United States