Cloud computing is a successful model for hosting webfacing applications that are accessed by their users as services. While clouds currently offer Service Level Agreements (SLAs) containing guarantees of availability, they do not make performance guarantees for deployed applications. In this work we present Cerebro -a system for establishing statistical guarantees of application response time in cloud settings. Cerebro combines off-line static analysis of application control structure with on-line cloud performance monitoring and statistical forecasting to predict bounds on the response time of web-facing application programming interfaces (APIs). Because Cerebro does not require application instrumentation or per-application cloud benchmarking, it does not impose any runtime overhead, and is suitable for use at cloud scales. Also, because the bounds are statistical, they are appropriate for use as the basis for SLAs between cloud-hosted applications and their users. We investigate the correctness of Cerebro predictions, the tightness of their bounds, and the duration over which the bounds persist in both Google App Engine and AppScale (public and private cloud platforms respectively). We also detail the effectiveness of our SLA prediction methodology compared to other performance bound estimation methods based on simple statistical analysis.
Cerebro REF predicts bounds on the response time performance of web APIs exported by applications hosted in a PaaS cloud.
Response time service level agreements for cloud-hosted web applications
SoCC '15
Computer Science