Kun Bai

Improving readiness for enterprise migration to the cloud

Enterprises are increasingly moving their IT infrastructures to the Cloud, driven by the promise of low-cost access to ready-to-use, elastic resources. Given the heterogeneous and dynamic nature of enterprise IT environments, a rapid and accurate discovery of complex infrastructure dependencies at the application, middleware, and network level is key to a successful migration to the Cloud. Existing migration approaches typically replicate source resources and configurations on the target site, making it challenging to optimize the resource usage (for reduced cost with same or better performance) or cloud-fit configuration (no misconfiguration) after migration. The responsibility of reconfiguring the target environment after migration is often left to the users, who, as a result, fail to reap the benefits of reduced cost and improved performance in the Cloud. In this paper we propose a method that automatically computes optimized target resources and identifies configurations given discovered source properties and dependencies of machines, while prioritizing performance in the target environment. From our analysis, we could reduce service costs by 60.1%, and found four types of misconfigurations from real enterprise datasets, affecting up to 81.8% of a data centers servers.

Automated business application discovery

When planning a data center migration it is critical to discover the clients business applications and on which devices (server, storage and appliances) those applications are deployed in the infrastructure. It is also important to understand the dependencies the applications have on the infrastructure, on other applications, and in some cases on systems external to the client. Clients can only rarely provide that information in a complete and accurate manner. The usual approach then has been to obtain the information by asking the clients application and platform owners a series of questions but in most cases clients do not have the tools or skills to acquire the requested information. The lack of accurate information leads to project delays, increased cost and higher levels of risk. In this paper we present an algorithm and tools for programmatically identifying and locating business application instances in an infrastructure, based on weighted similarity metric. We discuss results from our preliminary evaluation and the correctness of the algorithm. Such automated approach to application discovery significantly helps clients to achieve their project objectives and timeline without imposing additional work on the application and platform owners.

Automation and orchestration framework for large-scale enterprise cloud migration

With the promise of low-cost access to flexible and elastic compute resources, enterprises are increasingly migrating their existing workloads to cloud environments. However, the heterogeneity and complexity of legacy IT infrastructure make it challenging to streamline processes of migration at an enterprise scale. In this paper, we present Cloud Migration Orchestrator (CMO), a framework for automation and coordination of large-scale cloud migration based 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 components, 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 further minimize workload remediation efforts. We present a taxonomy of network challenges, based on experience with migration of legacy environments and discuss how to automate and optimize network configurations. For each step of the migration process, starting from pre-migration assessment through the post-migration configuration, we 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, including a fourfold process improvement (with respect to traditional approaches) using automation and orchestration.