Cloud computing is an emerging paradigm that provides hardware, platform and software resources as services over the internet in a pay-as-you-go model. It is being increasingly used for hosting and executing service-based business processes. These business processes are exposed to dynamic evolution during their life-cycle due to the highly dynamic evolution of cloud environments. The main adopted technique is to couple cloud computing with autonomic management in order to build autonomic computing systems. Almost all the existing approaches on autonomic computing have been focused on modeling and implementing autonomic mechanisms without paying any attention to the optimization of the autonomic management cost. Therefore, in this paper, we propose a novel approach based on binary linear program for determining the optimal allocation of cloud resources to manage a service-based business process which guarantees the specific requirements of customers and minimizes the management monetary cost. Then, to validate our approach under realistic conditions and inputs, we extend the CloudSim simulator to model and simulate the behaviour of business processes and their management in a cloud environment. Experiments conducted on two real datasets highlight the effectiveness of our approach.
Network security policy automation enables enterprise security teams to keep pace with increasingly dynamic changes in on-premises and public/hybrid cloud environments. This chapter discusses the most common use cases for policy automation in the enterprise, and new automation methodologies to address them by taking the reader step-by-step through sample use cases. It also looks into how emerging automation solutions are using big data, artificial intelligence, and machine learning technologies to further accelerate network security policy automation and improve application and network security in the process.
Nowadays cloud environments are used by many business service sectors like healthcare, retail marketing, banking, and many business fields. At the same time, the usage of Internet of Things (IoT) devices in different sectors also increasing tremendously. So, there is a general problem for securing any business service in enterprise cloud environments restricting by only authorized devices. We are proposing cryptographic techniques with the help of a token-based framework by enabling a secure handshake between consuming applications and the source business service which aims to authorize the target end consumers of the respective business service. The proposed work aims to achieve the desired secure handshake so that any consuming application or device requests the desired business service with a secret token and an input combination. The source business service creates a secure token using any latest robust cryptographic algorithm on the above input combination and returns the token to the consuming application. The consuming application requests to the source business service, it must pass the above token which if validated then only would receive the required data. Hence, in this paper, we propose the delegation of the authorization task to the end consumers, who are responsible to fetch the security tokens and use them in their application lifecycle.
Over the past years, Cloud computing has become one of the most influential information technologies to combat computing needs because of its unprecedented advantages. In spite of all the social and economic benefits it provides, it has its own fair share of issues. These include privacy, security, virtualization, storage, and trust. The underlying issues of privacy, security, and trust are the major barriers to the adoption of cloud by individuals and organizations as a whole. Trust has been the least looked into since it includes both subjective and objective characteristics. There is a lack of review on trust models in this research domain. This paper focuses on getting insight into the nomenclature of trust, its classifications, trust dimensions and throws an insight into various trust models that exist in the current knowledge stack. Also, various trust evaluation measures are highlighted in this work. We also draw a comparative analysis of various trust evaluation models and metrics to better understand the notion of trust in cloud environments. Furthermore, this work brings into light some of the gaps and areas that need to be tackled toward solving the trust issues in cloud environments so as to provide a trustworthy cloud ecosystem. Lastly, we proposed a Machine Learning backed Rich model based solution for trust verification in Cloud Computing. We proposed an approach for verifying whether the right software is running for the correct services in a trusted manner by analyzing features generated from the output cloud processed data. The proposed scheme can be utilized for verifying the cloud trust in delivering services as expected that can be perceived as an initiative towards trust evaluation in cloud services employing Machine learning techniques. The experimental results prove that the proposed method verifies the service utilized with an accuracy of 99%.