Automation and orchestration framework for large-scale enterprise cloud migration PROJECT TITLE :Automation and orchestration framework for large-scale enterprise cloud migrationABSTRACT:With the promise of low-price access to versatile and elastic compute resources, enterprises are increasingly migrating their existing workloads to cloud environments. However, the heterogeneity and complexity of legacy IT infrastructure create it difficult to streamline processes of migration at an enterprise scale. In this paper, we tend to present Cloud Migration Orchestrator (CMO), a framework for automation and coordination of huge-scale cloud migration based mostly on the IBM Business Process Management (BPM) technology with pre-migration analytics. CMO seamlessly automates complex and error-prone tasks, spanning from on-premise data center analysis, using correlations between occurrences of middleware elements, to parallel migration execution by integrating various vendor migration tools. CMO offers self-service capability with a “one-click” migration execution and provides a solution for retaining IP addresses to more minimize workload remediation efforts. We gift a taxonomy of network challenges, primarily based on expertise with migration of legacy environments and discuss how to automate and optimize network configurations. For every step of the migration process, beginning from pre-migration assessment through the post-migration configuration, we tend to discuss lessons learned from real-world deployments and demonstrate how the novel CMO framework reduces human activities through automation. Finally, we discuss efficiency of migration capabilities, as well as a fourfold process improvement (with respect to ancient approaches) using automation and orchestration. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Statistical evaluation of AC corona images in long-time scale and characterization of short-gap leader Person Re-Identification by Dual-Regularized KISS Metric Learning