Revisiting Asymmetric Classifier Fuzzy Keyword Search with Performance Isolated Intertenant Storage Clusters in Public Cloud

Background/Objectives: Virtual Clusters enabled users across globe to be able to access virtual machines, transact and deploy applications. Intertenant cloud customers demand availability and performance isolation from the cloud. Methods/Statistical Analysis: Though fair allocation of resources guarantees performance isolation of intra-tenants in SaaS applications, it is still a challenging task in intertenant IaaS cloud. Findings: Existing Universal keyword classifier is revisited to suggest a new scheme named Refined Asymmetric Classifier Fuzzy Keyword Search. The Classifier Search server utilizes universal keyword classifier for the search keyword request to greatly reduce the searching time by learning the search path pattern for all the keywords in the Fuzzy keyword set. In addition to this, the cloud sevice providers of our proposed scheme utilizes Performance Isolated Intertenant Storage Cluster to provide availability and performance isolation. Our implementation outcome have shown that our proposed searching scheme minimizes searching time on the cloud storage server by utilizing the data effectively with high availability and predictable performance isolation. Applications/Improvements: This facilitates the cloud service provider to hold back the time to serve multiple customers as the searching time is absolutely diminished.