2013 IEEE Sixth International Conference on Cloud Computing (2012)
Honolulu, HI, USA USA
June 24, 2012 to June 29, 2012
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CLOUD.2012.25
Cloud computing offers users the ability to access large pools of computational and storage resources on-demand without the burden of managing and maintaining their own IT assets. Today's cloud providers charge users based upon the amount of resources used or reserved, with only minimal guarantees of the quality-of-service (QoS) experienced byte users applications. As virtualization technologies proliferate among cloud providers, consolidating multiple user applications onto multi-core servers increases revenue and improves resource utilization. However, consolidation introduces performance interference between co-located workloads, which significantly impacts application QoS. A critical requirement for effective consolidation is to be able to predict the impact of application performance in the presence of interference from on-chip resources, e.g., CPU and last-level cache (LLC)/memory bandwidth sharing, to storage devices and network bandwidth contention. In this work, we propose an interference model which predicts the application QoS metric. The key distinctive feature is the consideration of time-variant inter-dependency among different levels of resource interference. We use applications from a test suite and SPECWeb2005 to illustrate the effectiveness of our model and an average prediction error of less than 8% is achieved. Furthermore, we demonstrate using the proposed interference model to optimize the cloud provider's metric (here the number of successfully executed applications) to realize better workload placement decisions and thereby maintaining the user's application QoS.
Interference, Servers, Measurement, Hidden Markov models, Quality of service, Degradation, Computational modeling, QoS-aware, Cloud computing, performance interference
Qian Zhu, Teresa Tung, "A Performance Interference Model for Managing Consolidated Workloads in QoS-Aware Clouds", 2013 IEEE Sixth International Conference on Cloud Computing, vol. 00, no. , pp. 170-179, 2012, doi:10.1109/CLOUD.2012.25