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

• ROAR transparently converts QoS goals to a set of optimized cloud resources.
• ROAR implements a fully automatic endtoend resource optimization process.
• ROAR supports generic web applications including multitier distributed architecture.
• ROAR supports multiple cloud platforms such as AWS and GCE.
• ROAR generates complete resource deployment template for the specific cloud platform.

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 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.