An Adaptive Namespace Management for Ultra Large Storage Systems

Abstract

Existing distributed storage frameworks for the most part neglect to offer a sufficient ability for the semantic questions. Since the genuine esteem or worth of information intensely relies upon how proficiently semantic pursuit can be done on the information in (near- ) real-time, vast parts of information wind up with their qualities being lost or essentially diminished because of the information staleness. With a specific end goal to completely assess the framework execution, we actualize all segments and functionalities of FAST in a model framework. The model framework is utilized to assess a utilization instance of close constant information examination of computerized pictures. We gather a major and genuine picture set that comprises of more than 60 million pictures (more than 200 TB storage limit) taken of a best traveler spot amid an occasion. Utilizing this genuine picture dataset as a contextual investigation, we assess the execution of FAST of finding missing youngsters from the picture dataset and contrast it and the cutting edge plans. The contextual investigation assessment exhibits the proficiency and adequacy of FAST in the execution changes and vitality reserve funds