Improving the Performance of Secure Cloud Infrastructure with Machine Learning Techniques

Security is one of the key concerns of the cloud user community. Most important ask from cloud users is to provide quality of services to manage Security end-to-end. The quality of Services (QoS) for securing cloud images were proposed in the earlier papers [2][3]. However, the key concern that still remains is how to balance performance and security. In this paper, a new and an intelligent model of QoS is being proposed which determines the files to be decrypted, without decrypting the entire file list in the secure wallet. Smart QoS is proposed as an extension to the security method proposed and presented in CCEM 2015[5] to improve the performance of Secure Cloud. The Smart QoS is capable of addressing some of the security concerns of cloud user community ensuring security and performance as well. Machine learning techniques have been used to design and develop the Smart Quality of Services. Solutions to ensure end-to-end security in cloud environments were proposed by us in the earlier paper [5]. In this paper, we have extended the security method proposed in [5] to ensure security and performance. Proposed Model is experimented in HPE Helion Cloud on two real time scenarios and results are attached to this paper.