MapReduce is well-applied in high performance computing for large scale data processing. However,... more MapReduce is well-applied in high performance computing for large scale data processing. However, as long as the clusters grow, handling with huge amount of intermediate data produced in the shuffle and reduce phases (middle step of Map Reduce) have impacts heavily upon the performance. With local aggregation (either combiners or in-mapper), shuffling large amounts of data can be reduced which alleviates the reduce straggler problem. The proposed modified B+ tree based indexing algorithm is applied to reduce intermediate data amount for output retrieval fast as well as scalable data storage.
Being popular for managing data dynamically in today's storage systems, fast data insertion, dele... more Being popular for managing data dynamically in today's storage systems, fast data insertion, deletion and searching are also concerned with the system's performance. Those criteria are heavily dependent on the way to handle the attributes of the algorithm used because it can determine how large as well as how much the system can hold data and throughput. B+ tree-based indexing algorithm is capable of scaling data logarithmically and so widely used in distributed file system. However, the level of the system's scalability is solely associated with the order and height of the tree. The proposed system modifies the traditional B+ Tree in the form power of 2-based for data expansion and it is designed on object-based file system.
The performance of the whole file system depends on how managing the system's components well. Co... more The performance of the whole file system depends on how managing the system's components well. Consequently, designing intelligent object will catch the fancy of most researchers. Furthermore, the intelligence of OSD (Object-based Storage Devices) depends on the object intelligence. Therefore, space allocation in OSD is strongly connected with this special object.
MapReduce is well-applied in high performance computing for large scale data processing. However,... more MapReduce is well-applied in high performance computing for large scale data processing. However, as long as the clusters grow, handling with huge amount of intermediate data produced in the shuffle and reduce phases (middle step of Map Reduce) have impacts heavily upon the performance. With local aggregation (either combiners or in-mapper), shuffling large amounts of data can be reduced which alleviates the reduce straggler problem. The proposed modified B+ tree based indexing algorithm is applied to reduce intermediate data amount for output retrieval fast as well as scalable data storage.
Being popular for managing data dynamically in today's storage systems, fast data insertion, dele... more Being popular for managing data dynamically in today's storage systems, fast data insertion, deletion and searching are also concerned with the system's performance. Those criteria are heavily dependent on the way to handle the attributes of the algorithm used because it can determine how large as well as how much the system can hold data and throughput. B+ tree-based indexing algorithm is capable of scaling data logarithmically and so widely used in distributed file system. However, the level of the system's scalability is solely associated with the order and height of the tree. The proposed system modifies the traditional B+ Tree in the form power of 2-based for data expansion and it is designed on object-based file system.
The performance of the whole file system depends on how managing the system's components well. Co... more The performance of the whole file system depends on how managing the system's components well. Consequently, designing intelligent object will catch the fancy of most researchers. Furthermore, the intelligence of OSD (Object-based Storage Devices) depends on the object intelligence. Therefore, space allocation in OSD is strongly connected with this special object.
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