2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) (2017)
Atlanta, Georgia, USA
June 5, 2017 to June 8, 2017
In multi-tier cloud service systems, performance evaluation relies on numerous experiments in order to collect key metrics such as resources usage. The approach may result in highly time-consuming in practice. In this paper, we propose an automated framework for performance tracking, data management and analysis to minimize human intervention in multi-tier cloud service systems. The framework support fine-grained analysis of the mixed workloads through the Discrete-time Markov-modulated Poisson process (DMMPP). A general multi-tier application is theoretically formulated as a queueing network to evaluate the performance. The effectiveness of the model has been validated through extensive experiments conducted in the RUBiS benchmark system.
Steady-state, Analytical models, Servers, Queueing analysis, Throughput, Time factors, Performance evaluation
X. Zhao, J. Huang, L. Liu, S. Liu, C. Pu and L. Cui, "Automated Performance Evaluation for Multi-tier Cloud Service Systems Subject to Mixed Workloads," 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Atlanta, Georgia, USA, 2017, pp. 2630-2631.