Capacity planning for Web performance: metrics, models, and methods
Capacity planning for Web performance: metrics, models, and methods
Open, Closed, and Mixed Networks of Queues with Different Classes of Customers
Journal of the ACM (JACM)
The Operational Analysis of Queueing Network Models
ACM Computing Surveys (CSUR)
Performance debugging for distributed systems of black boxes
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
An analytical model for multi-tier internet services and its applications
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications
ICAC '07 Proceedings of the Fourth International Conference on Autonomic Computing
Exploiting nonstationarity for performance prediction
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Towards highly reliable enterprise network services via inference of multi-level dependencies
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
Answering what-if deployment and configuration questions with wise
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
Informed data distribution selection in a self-predicting storage system
ICAC '06 Proceedings of the 2006 IEEE International Conference on Autonomic Computing
Discovering Likely Invariants of Distributed Transaction Systems for Autonomic System Management
ICAC '06 Proceedings of the 2006 IEEE International Conference on Autonomic Computing
A query language and runtime tool for evaluating behavior of multi-tier servers
Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Modellus: Automated modeling of complex internet data center applications
ACM Transactions on the Web (TWEB)
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Modern cloud applications are complex distributed systems with tens or hundreds of interacting software components. An important management task in cloud computing platforms is to predict the impact of a certain workload or reconfiguration change on the performance of the application. Such predictions require the design of "what-if" models of the application that take as input hypothetical changes in the application's workload or environment and estimate its impact on performance. We present a workload-based what-if analysis system that uses commonly available monitoring information in large scale systems to enable the administrators to ask a variety of workload-based "what-if" queries about the system. We use a network of queues to analytically model the behavior of large distributed cloud applications. Our system automatically generates node-level queueing models and then uses model composition to build system-wide models. We employ a simple what-if query language and an intelligent query execution algorithm that employs on-the-fly model construction and a change propagation algorithm to efficiently answer queries on large scale systems. We have built a prototype and have used traces from two large production cloud bapplications from a financial institution as well as real-world synthetic applications to evaluate its what-if modeling framework. Our experimental evaluation validates the accuracy of our node-level resource usage, latency and workload models and then shows how our system enables what-if analysis in four different cloud applications.