Network virtualization is a demanding feature in the evolution of future Internet architectures. It enables on-demand virtualized resource provision for heterogeneous Virtual Network Requests (VNRs) from diverse end users over the underlying substrate network. However, network virtualization provides various benefits such as service separation, improved Quality of Service, security, and more prominent resource usage. It also introduces significant research challenges. One of the major such issues is allocating substrate network resources to VNR components such as virtual machines and virtual links, also named as the virtual network embedding, and it is proven to be NP-hard. To address the virtual network embedding problem, most of the existing works are ( i .) Single-objective, ( ii .) They failed to address dynamic and time-varying network states ( iii .) They neglected network-specific features. All these limitations hinder the performance of existing approaches. This work introduces an embedding framework called Intelligent Deep Reinforcement Learning (DRL) Strategy for effective virtual network embedding of an online VNRs ( InDS ). The proposed InDS uses an actor-critic model based on DRL architecture and Graph Convolutional Networks (GCNs). The GCN effectively captures dependencies between the VNRs and substrate network environment nodes by extracting both network and system-specific features. In DRL, the asynchronous advantage actor-critic agents can learn policies from these features during the training to decide which virtual machines to embed on which servers over time. The actor-critic helps in efficiently learning optimal policies in complex environments. The suggested reward function considers multiple objectives and guides the learning process effectively. Evaluation of simulation results shows the effectiveness of InDS in achieving optimal resource allocation and addressing diverse objectives, including minimizing congestion, maximizing acceptance, and revenue-to-cost ratios. The performance of InDS exhibits superiority in achieving 28% of the acceptance ratio and 45% of the revenue-to-cost ratio by effectively managing the network congestion compared to other existing baseline works.

Cloud computing aims at allowing access to large amount of computing power in a fully virtualized manner, by aggregating resources and offering a single system view. More and more businesses and individuals are attracted by the benefits of the cloud and are shifting their data and computation work to be carried out in the cloud. But since the processing of users data is done by remote computing resources and data is placed in remote storage systems, users of cloud computing has fear about the security of their data. These computation and storage are distributed in nature. To address this issue we are utilizing a framework called Cloud Information Accountability. This framework uses programmable capability of the jar file. In this framework a user data is embedded within ajar file along with the function which provides accountability for that data by automatically creating log records upon the access to the data. This feature is called “strong binding” which means both data and policies travel with the data thus providing distributed accountability which is very essential because of distributed nature of the cloud. Hence this framework provides end to end accountability for user's data at both CSP and Cloud users by providing the log records for their data and also providing the online Chat mechanism which enables the CSP and Cloud users can communicate themselves to share file which is unable to download form the cloud.