In an era with compelling need for greater computation power, the aggregation of software system components is becoming more challenging and diverse. The new-generation scientific applications are growing hub of complex and intense computation performed on huge data set with exponential growth. With the development of parallel algorithms, design of multi-user web applications and frequent changes in software architecture, there is a bigger challenge lying in front of the research institutes and organizations. Network science is an interesting field posing extreme computation demands to sustain complex large-scale networks. Several static or dynamic network analysis have to be performed through algorithms implementing complex graph theories, statistical mechanics, data mining and visualization. Similarly, high performance computation infrastructures are imbibing multiple characters and expanding in an unprecedented way. In this age, it's mandatory for all software solutions to migrate to scalable platforms and integrate cloud enabled data center clusters for higher computation needs. So, with aggressive adoption of cloud infrastructures and resource-intensive web-applications, there is a pressing need for a dynamic middleware to bridge the gap and effectively coordinate the integrated system. Such a heterogeneous environment encourages the devising of a transparent, portable and flexible solution stack. In this project, we propose adoption of Virtual Machine aware Portable Batch System Cluster (VM-aware PBS Cluster), a self-initiating and self-regulating cluster of Virtual Machines (VM) capable of operating and scaling on any cloud infrastructure. This is an unique but simple solution for large-scale softwares to migrate to cloud infrastructures retaining the most of the application stack intact. In this project, we have also designed and implemented Cloud Integrator Framework, a dynamic implementation of cloud aware middleware for the proposed VM-aware PBS cluster. This framework regulates job distribution in an aggregate of VMs and optimizes resource consumption through on-demand VM initialization and termination. The model was integrated into CINET system, a network science application. This model has enabled CINET to mediate large-scale network analysis and simulation tasks across varied cloud platforms such as OpenStack and Amazon EC2 for its computation requirements. / Master of Science