Description

MapReduce has become ubiquitous for processing large data volume jobs. In existing system, the Hadoop The weighted Round Robin scheduling allocate weight to each queue then scheduling tasks of different sub queue according to weight. The development of ontologies involves continuous but relatively small modifications. Even after a number of changes, ontology and its previous version usually share most of their axioms. Unfortunately, when ontology evolves, current reasoners do not take advantage of the similarities between the ontology and its previous version. That is, when reasoning over the latest version of ontology, current reasoners do not reuse existing results already obtained for the previous one and repeat the whole reasoning process. Even after a number of changes, ontology and its previous version usually share most of their axioms. Unfortunately, when ontology evolves, current reasoners do not take advantage of the similarities between the ontology and its previous version. That is, when reasoning over the latest version of ontology, current reasoners do not reuse existing results already obtained for the previous one and repeat the whole reasoning process. In this project the system proposed a method to improve the efficiency of the map reduce scheduling algorithms. Then the SAMR mapreduce scheduling technique is being developed which uses the historical information and find the slow nodes and launches backup tasks. The historical information is stored in each nodes in XML format.