--The cloud storage offers users with infrastructure flexibility, faster deployment of applications and data, cost control, adaptation of cloud resources to real needs, improved productivity, etc. Inspite of these advantageous factors, there are several deterrents to the widespread adoption of cloud computing remain. Among them, security towards the correctness of the outsourced data and issues of privacy lead a major role. In order to avoid security risk for the outsourced data, we propose the dynamic audit services that enables integrity verification of untrusted and outsourced storages. An interactive proof system (IPS) with the zero knowledge property is introduced to provide public auditability without downloading raw data and protect privacy of the data. In the proposed system data owner stores the large number of data in cloud after e encrypting the data with private key and also send public key to third party auditor (TPA) for auditing purpose. TPA in clouds and it's maintained by CSP. An Authorized Application (AA), which holds a data owners secret key (sk) and manipulate the outsourced data and update the associated IHT stored in TPA. Finally Cloud users access the services through the AA. Our system also provides secure auditing while the data owner outsourcing the data in the cloud. And after performing auditing operations, security solutions are enhanced for the purpose of detecting malicious users with the help of Certificate Authority.
Lot of scientific problems in various domains from modelling sky as mosaics to understand Genome sequencing in biological applications are modelled as workflows with large number of interconnected tasks. Particle Swarm Optimization (PSO) based metaheuristics are currently used to address many optimization problems as they are simple to implement and able to produce quickly optimal or sub-optimal solutions based on learning capabilities. Even though many works are cited in the literature on workflow scheduling, most of the existing works are focused on reducing the makespan alone. Moreover, energy efficiency is considered only in few works included in the literature. Constraints about the dynamic workload allocation are not introduced in the existing systems. Moreover, the optimization techniques used in the existing systems have improved the QoS with little scalability in the cloud environment since they consider only the infrastructure as the service model.In this work a new algorithm has been proposed based on the proposal of a new Multi-Objective Optimization model called F-NSPSO using NSPSO Meta-Heuristic s. This method allows the user to choose a suitable configuration dynamically. An average of above 15% in the energy reduction for the proposed system over simple DVFS was achieved for all types of workflow applications with different dimensions. Similarly when compared to NSPSO an energy reduction of at least 10% has been observed for F-NSPSO for all three types of workflow applications. Compared to NSPSO algorithm F-NSPSO algorithm shows at least 13%, 12% and 21% improvement in average makespan for Montage, Cybershake and Epigenomics workflow applications respectively.
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