(untitled) (International Journal of Engineering and Computer Science, 2017)
As the sizes of IT infrastructure continue to grow, cloud computing is a natural extension of virtualization technologies that enable scalable management of virtual machines over a plethora of physically connected systems. Cloud computing provides on-demand access to computational resources which together with pay-per use business models, enable application providers seamlessly scaling their services. Cloud computing infrastructures allow creating a variable number of virtual machine instances depending on the application demands. However, even when large-scale applications are deployed over pay-per-use cloud high-performance infrastructures, cost-effective scalability is not achieved because idle processes and resources (CPU, memory) are unused but charged to application providers. Over and under provisioning of cloud resources are still unsolved issues. Here we try to present the data compression techniques to squeezing data that illustrates to reduce the size which is about deploy in cloud servers. These compression techniques are much reliable when we need to manage the large amount of data, especially useful for industries like which maintain huge data warehouses and big educational universities for reducing the cost. This work attempts to establish formal measurements for under and over provisioning of virtualized resources in cloud infrastructures, specifically for SaaS(software as a service) platform deployments and proposes a resource allocation model to deploy SaaS applications over cloud computing platforms by taking into account their multitenancy, thus creating a costeffective scalable environments. As a result the aim of this paper is two-folded; firstly to evaluate cloud security by compressing data which contains encrypted format to ensure sufficient security, Requirements and secondly to present a viable solution that creates a cost-effective cloud environment for large-scale systems