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Cloud Computing are used to deliver services from share pool of computing resources. These resources are provided to the user using the internet. It has advantage over traditional computing services along with new challenge. These are data security, privacy, protection, access control, availability, authentication, scalability, lock-in and confidentiality. These are inherent security flaws of cloud technology due to openness, multi-tenancy, outsourcing of resources. Access control is a fundamental security mechanism but traditional access control like as mandatory, discretionary and role based access control are not suitable. These traditional existing access control model are not effective and feasible solutions for cloud. In this research work, novel access control framework is proposed that can address the security and privacy issues for cloud. The framework is based on dynamic trustworthiness of user and provides an effective and feasible access control solution for cloud. A multi layer security standard, policies and access control mechanism are provided with proposed framework. The access control is based on the trustworthiness of the user, which is demonstrated by static and dynamic trust evidence. The dynamic trustworthiness is used to reduce the possibility to perform unauthorized activities and ensures that only authorized user's access cloud resources. The prototype of the proposed framework is developed in NetLogo on Linux platform and demonstrated with test cases. The analysis of simulated results shows that proposed mechanism is highly efficient and robust under existing security threats.

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Virtualization is one of the key features of cloud computing, where the physical machines are virtually divided into several virtual machines in the cloud. The user's tasks are run on these virtual resources as per the requirements. When the user requests the services to the cloud, the user's tasks are allotted to the virtual resources depending on their needs. An efficient scheduling mechanism is required for optimizing the involved parameters. Scientific workflows deals with a large amount of data with dependency constraints and is used to simplify the applications in the diverse scientific domains. Scheduling the workflow in cloud computing is a well-known NP-hard problem. Deploying such data-and compute-intensive workflow on the cloud needs an efficient scheduling algorithm. In this paper, we have proposed a multi-objective model based hybrid algorithm (HPSOGWO), which combines the desirable characteristics of two well-known algorithms, particle swarm optimization (PSO), and grey wolf optimization (GWO). The results are analyzed under complex real-world scientific workflows such as Montage, CyberShake, Inspiral, and Sipht. We have considered the two essential parameters: total execution time and total execution cost while working in the cloud environment. The simulation results show that the proposed algorithm performs well compared to other state-of-the-art algorithms such as round-robin (RR), ant colony optimization (ACO), heterogeneous earliest time first (HEFT), and particle swarm optimization (PSO).

Cloud computing is one of the emerging technologies in computer science in which services are provided through the internet on-demand. Workflow scheduling is considered to be an NP-hard problem and has a significant issue in the cloud environment.Finding the polynomial-time solutions for workflow scheduling problem is difficult with most of the existing algorithms designed for traditional computing platforms. Some existing meta-heuristics algorithms proposed for workflow scheduling problem are stuck in the local optimal solution and fails to give the global optimal solution. In this article, a hybrid of particle swarm optimization and gray wolf optimization, named the PSO-GWO algorithm, is proposed for workflow scheduling. The proposed algorithm was tested to reduce the total executing cost (TEC) and total execution time (TET) of the dependent tasks in the cloud computing environment. The proposed algorithm takes advantage of both the standard PSO and GWO algorithms and does not stick in the local optimal solution. The experiment results show that the PSO-GWO outperformed compared with the standard PSO and GWO algorithm in TEC and TET.