Cloud computing offers end users a scalable and cost-effective way to access multi-platform data. While the Cloud Storage features endorse it, resource loss is also likely. A fault-tolerant mechanism is therefore required to achieve uninterrupted cloud service performances. The two widely used defect-tolerant mechanisms are task relocation and replication. But the replication approach leads to enormous overhead storage and computing as the number of tasks gradually increases. When a large number of defects occur, it creates more overhead storage and time complexity depending on task criticalities. An Integrated Fault Reduction Scheduling (IFRS) cloud computing model is used to resolve these problems. The probability of failure of a VM is calculated by finding the previous failures and active executions in this model. Then a fault-related adaptive recovery timer is retained, modified depending on the fault type. Experimental findings showed that IFRS reached 67% lower storage costs and 24% less response time when comparing with the current technique for sensitive tasks.

The revolution impacted by Web Service as a solution to business and enterprise application integration throws light on the significance of security provided by Web Services during Web Service Composition. Satisfying the security requirements is truly a demanding task because of the dynamic and capricious nature of the Web. Web Service Composition associates web services to create high level business process that absolutely matches and conforms appropriately to the service requestor’s needs. It involves customizing services often by locating, assimilating and deploying elementary services. Our paper proposes a policy based system for granting security during the process of web service composition. Policies defined for effective and secure composition analyze and verify the conditions under which the task of the web service is allowed or rejected. To achieve this specification, we make use of Finite State Machine model which clearly portrays the business and flow logic. Nodes in the Finite State Machine represent rules. Upon efficacious fulfillment of policies which are defined in the node access points, transition between rules is stimulated. A service composition is said to be successfully incorporated only if there is complete absence of policy violations when combining the policies of elementary services. The simulated FSM which extracts the rules and policies of the web services and correctly matches and satisfies the policy constraints defined in the access points ensure providing security for the composite web service.

Speech is an important mode of communication for people. For a long time, researchers have been working hard to develop conversational machines which will communicate with speech technology. Voice recognition is a part of a science called signal processing. Speech recognition is becoming more successful for providing user authentication. The process of user recognition is becoming more popular now a days for providing security by authenticating the users. With the rising importance of automated information processing and telecommunications, the usefulness of recognizing an individual from the features of user voice is increasing. In this paper, the three stages of speech recognition processing are defined as pre-processing, feature extraction and decoding. Speech comprehension has been significantly enhanced by using foreign languages. Automatic Speech Recognition (ASR) aims to translate text to speech. Speaker recognition is the method of recognizing an individual through his/her voice signals. The new speaker initially privileges identity for speaker authentication, and then the stated model is used for identification. The identity argument is approved when the match is above a predefined threshold. The speech used for these tasks may be either text-dependent or text-independent. The article uses Bacterial Foraging Optimization Algorithm (BFO) for accurate speech recognition through Mel Frequency Cepstral Coefficients (MFCC) model using DNN. Speech recognition efficiency is compared to that of the conventional system.

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