Cloud services help individuals and organization to use data that are managed by third parties or another person at remote locations. With the increase in the development of cloud computing environment, the security has become the major concern that has been raised more consistently in order to move data and applications to the cloud as individuals do not trust the third party cloud computing providers with their private and most sensitive data and information. This paper presents, the migration of virtual machine to improve the security in cloud computing. Virtual machine (VM) is an emulation of a particular computer system. In cloud computing, virtual machine migration is a useful tool for migrating operating system instances across multiple physical machines. It is used to load balancing, fault management, low-level system maintenance and reduce energy consumption. Virtual machine (VM) migration is a powerful management technique that gives data center operators the ability to adapt the placement of VMs in order to better satisfy performance objectives, improve resource utilization and communication locality, achieve fault tolerance, reduce energy consumption, and facilitate system maintenance activities. In the migration based security approach, proposed the placement of VMs can make enormous difference in terms of security levels. On the bases of survivability analysis of VMs and Discrete Time Markov Chain (DTMC) analysis, we design an algorithm that generates a secure placement arrangement that the guest VMs can moves before succeeds the attack.

In the Environment of Big-Data analytics in world-wide, the cloud web-services were deployed in internet and Intranet domains. Moreover cloud computing possess the privileges and acquired rapid development, faces the trust complexities, privacy concepts and security issues which allows to implement the QoS measures in the optimization techniques in the web services selection. The study focusses on selection of component services and employing the efficient algorithm with end to end Quality of measures. The Data diversification and the service characteristics would decline the accuracy level of the measures. In this study a novel Qos measure web-services algorithm implemented the weight attributes and the subjective attributes. This study employs the novel hybrid-optimization algorithm in gaining the privileges of the search randomised-attributes and the implementation of IWO-invasive-weed algorithm. This study also focusses on the Calculation of Quality-of-service measures on the weights of the web-services attributes. Many researches have placed the Implementation of nature inspired concept for the optimization complexities in Big Data and thus employing Eagle-Perching Algorithm in the efficiency enhancement of cloud web-services. The evolution of BES- Bald Eagle-Search were utilized as the nature inspired approach would drive as the efficient technique for optimisation issues which imitates the bald-eagles behaviour. The results have been demonstrated the comparison of the performance metrics with the existing approaches to evaluate the proposed methodology.

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