When average is not average: large response time fluctuations in n-tier systems
Proceedings of the 9th international conference on Autonomic computing
Two levels autonomic resource management in virtualized IaaS
Future Generation Computer Systems
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Good performance and efficiency, in terms of high quality of service and resource utilization for example, are important goals in a cloud environment. Through extensive measurements of an n-tier application benchmark (RUBBoS), we show that overall system performance is surprisingly sensitive to appropriate allocation of soft resources (e.g., server thread pool size). Inappropriate soft resource allocation can quickly degrade overall application performance significantly. Concretely, both under-allocation and over-allocation of thread pool can lead to bottlenecks in other resources because of non-trivial dependencies. We have observed some non-obvious phenomena due to these correlated bottlenecks. For instance, the number of threads in the Apache web server can limit the total useful throughput, causing the CPU utilization of the C-JDBC clustering middleware to decrease as the workload increases. We provide a practical iterative solution approach to this challenge through an algorithmic combination of operational queuing laws and measurement data. Our results show that soft resource allocation plays a central role in the performance scalability of complex systems such as n-tier applications in cloud environments.