Complex multi-tier applications deployed in cloud computing environments can experience rapid changes in their workloads. To ensure market readiness of such applications, adequate resources need to be provisioned so that the applications can meet the demands of specified workload levels and at the same time ensure that service level agreements are met. Multi-tier cloud applications can have complex deployment configurations with load balancers, web servers, application servers and database servers. Complex dependencies may exist between servers in various tiers. To support provisioning and capacity planning decisions, performance testing approaches with synthetic workloads are used. Accuracy of a performance testing approach is determined by how closely the generated synthetic workloads mimic the realistic workloads. Since multi-tier applications can have varied deployment configurations and characteristic workloads, there is a need for a generic performance testing methodology that allows accurately modeling the performance of applications. We propose a methodology for performance testing of complex multi-tier applications. The workloads of multi-tier cloud applications are captured in two different models-benchmark application and workload models. An architecture model captures the deployment configurations of multi-tier applications. We propose a rapid deployment prototyping methodology that can help in choosing the best and most cost effective deployments for multi-tier applications that meet the specified performance requirements. We also describe a system bottleneck detection approach based on experimental evaluation of multi-tier applications.
REF proposes a performance evaluation approach for complex multi-tier cloud applications which captures the application workload via 3 different models: benchmark, workload, and architecture.
Performance Evaluation Approach for Multi-Tier Cloud Applications
Journal of Software Engineering and Applications
Computer Science