ROAR: A QoS-oriented modeling framework for automated cloud resource allocation and optimization

Cloud computing offers a fast, easy and cost-effective way to configure and allocate computing resources for web applications, such as consoles for smart grid applications, medical records systems, and security management platforms. Although a diverse collection of cloud resources (e.g., servers) is available, choosing the most optimized and cost-effective set of cloud resources for a given web application and set of quality of service (QoS) goals is not a straightforward task. Optimizing cloud resource allocation is a critical task for offering web applications using a software as a service model in the cloud, where minimizing operational cost while ensuring QoS goals are met is critical to meeting customer demands and maximizing profit. Manual load testing with different sets of cloud resources, followed by comparison of test results to QoS goals is tedious and inaccurate due to the limitations of the load testing tools, challenges characterizing resource utilization, significant manual test orchestration effort, and challenges identifying resource bottlenecks. This paper introduces our work using a modeling framework ROAR (Resource Optimization, Allocation and Recommendation System) to simplify, optimize, and automate cloud resource allocation decisions to meet QoS goals for web applications, including complex multi-tier application distributed in different server groups. ROAR uses a domain-specific language to describe the configuration of the web application, the APIs to benchmark Email addresses: [email protected] (Yu Sun), [email protected] (Jules White), [email protected] (Sean Eade), [email protected] (Douglas C. Schmidt) Preprint submitted to Journal of Systems and Software October 1, 2014 and the expected QoS requirements (e.g., throughput and latency), and the resource optimization engine uses model-based analysis and code generation to automatically deploy and load test the application in multiple resource configurations in order to derive a cost-optimal resource configuration that meets the QoS goals.