The cloud computing is an Internet-based computing emerging as a new architecture which aims to give reliable, customizable and QoS guaranteed dynamic environment for end-users. As multi-tenancy is one of the key features of cloud computing where service providers and users have scalable and economic benefits on same cloud platforms. In cloud computing environment the execution process requires resource management due to the processing capability is high to the resource ratio. The aim of the system is to handle resource management by executing scientific workflows. The locating and assigning of free resources is handled through the Cloud-based Workflow Scheduling Algorithm (CWSA) policy. The simulation results shows that the scheduling algorithm improves the performance of scientific workflows and helps in minimization of workflow completion time, tardiness, execution cost and use of idle resources of cloud using simulator Workflowsim.

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Abstract. Policy-based access control is a technology that achieves separation of concerns through evaluating an externalized policy at each access attempt. While this approach has been well-established for requestresponse applications, it is not supported for database queries of datadriven applications, especially for attribute-based policies. In particular, search operations for such applications involve poor scalability with regard to the data set size for this approach, because they are influenced by dynamic runtime conditions. This paper proposes a scalable application-level middleware solution that performs runtime injection of the appropriate rules into the original search query, so that the result set of the search includes only items to which the subject is entitled. Our evaluation shows that our method scales far better than current state of practice approach that supports policy-based access control.

Applications are increasingly operating on large data sets. This trend creates problems for access control, which in principle restricts the actions that subjects can perform on any item in that data set. Performance issues therefore emerge, typically for operations on entire data sets. Emerging access control models such as attribute-based access control do meet their limitations in this context. Worse, few solutions exist that addresses performance problems while supporting separation of concerns. In this paper, we present a first approach towards addressing this challenge. We propose a middleware architecture that performs policy transformations and query rewriting for externalized policies to optimize the access control process on the data set. We argue that this offers a promising approach for reducing the policy evaluation overhead for access control on large data sets.