CloudXplor: a tool for configuration planning in clouds based on empirical data
Proceedings of the 2010 ACM Symposium on Applied Computing
Proceedings of the 2010 ACM Symposium on Applied Computing
Quantitative system evaluation with Java modeling tools
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
Automated control for elastic n-tier workloads based on empirical modeling
Proceedings of the 8th ACM international conference on Autonomic computing
Distributed workload and response time management for web applications
Proceedings of the 7th International Conference on Network and Services Management
Web workload generation challenges - an empirical investigation
Software—Practice & Experience
Experiences of using a hybrid cloud to construct an environmental virtual observatory
Proceedings of the 3rd International Workshop on Cloud Data and Platforms
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In many areas such as e-commerce, mission-critical N-tier applications have grown increasingly complex. They are characterized by non-stationary workloads (e.g., peak load several times the sustained load) and complex dependencies among the component servers. We have studied N-tier applications through a large number of experiments using the RUBiS and RUBBoS benchmarks. We apply statistical methods such as kernel density estimation, adaptive filtering, and change detection through multiple-model hypothesis tests to analyze more than 200GB of recorded data. Beyond the usual single-bottlenecks, we have observed more intricate bottleneck phenomena. For instance, in several configurations all system components show average resource utilization significantly below saturation, but overall throughput is limited despite addition of more resources. More concretely, our analysis shows experimental evidence of multi-bottleneck cases with low average resource utilization where several resources saturate alternatively, indicating a clear lack of independence in their utilization. Our data corroborates the increasing awareness of the need for more sophisticated analytical performance models to describe N-tier applications that do not rely on independent resource utilization assumptions. We also present a preliminary taxonomy of multi-bottlenecks found in our experimentally observed data.