The seamless conveying of information's considering internet as a virtual space, connecting all users from the world is cloud computing. In simpler terms it is a means of storing and accessing information over internet irrespective of place and time. The cloud proceeds with the process of storing, retrieving and allowing access, on demand as a paid service. As the benefits of cloud is attractive, the number of users adopting to the cloud also increases, so the workflow management becomes tedious and more challenging in the cloud. The decision to provide with an enhanced work flow management requires proper scheduling recollecting value of the information. So the paper proposes with an efficient method of managing the work flow considering the value of the information by categorizing between the much value and non-value information's and framing the algorithm that functions as scheduler using the parallel implementation in natural process of genetic algorithm (GA) with secured frame work for the information's of high value. The proposed work shows an overwhelmed performance than the conventional techniques in the time taken for the execution and its total cost. The performance of the scheduler is validated in the WORKFLOWSIM on the grounds of time of execution and total cost.

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The work-flow is emerging as the promising paradigm for the different computing infrastructures of distributed nature. It is applied widely in various scientific fields and simple job execution. To manage with the enormous flow of data in the work-flow, the requirement of huge infrastructure and the execution in the reasonable time. The work -flow adapts to the cloud environment. Though cloud environment provides with the large-scale infrastructure and consistency and efficiency on comparing to the system operated over non-cloud, a proper scheduling is entailed for the execution of tasks satisfying the specified constraints with the necessary security measure that has to be provided as the cloud does not provide with the counter measures for the security threats. More over as the real world tasks are most probably heterogeneous requiring virtual machines of different instance of series. So the paper proffers a cost-effective algorithm based on the multi-populated genetic algorithm (Multi-populated GA) implanted with particle swarm optimization (PSO) for the heterogeneous networks so as to minimize the cost of the execution and meet out the QOS constraints, the results serve as the guiding principle for the security level improvement for the (sensitive) critical tasks. The performance evaluation of the scheduling using the workflowsim is performed to evidence the maximum reduction in the cost of the execution and the QOS constraints met using the proposed system.

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Grid is a distributed computing architecture that integrates a large number of data and computing resources into a single virtual data management system. A Computational Grid is a natural extension of the former cluster computer where large computing tasks have to be computed at distributed computing resources. A safe registration and communication is essential in Computational Grid networks. This paper reports a secure tunnelling protocol integrated frame work, which enhances the quality of Point-to-Point Tunnelling Protocol (PPTP), Layer Two Tunnelling Protocol (L2TP) and Internet Security Protocol (IPSec). The proposed model used an encryption scheme such as Data Encryption Standard (DES) algorithm. The new packet offers a secure communication in the grid network without any time delays.