The Descartes Modeling Language for Self-Aware Performance and Resource Management

Samuel Kounev, University of Würzburg, Würzburg, Germany
Associate Editor: Zhen Ming (Jack) Jiang, York University, Toronto, Canada 

Modern software systems have increasingly distributed architectures composed of loosely-coupled services that are typically deployed on virtualized infrastructures. Such system architectures provide increased flexibility by abstracting from the physical infrastructure, which can be leveraged to improve system efficiency. However, these benefits come at the cost of higher system complexity and dynamics. The inherent semantic gap between application-level metrics, on the one hand, and resource allocations at the physical and virtual layers, on the other hand, significantly increase the complexity of managing end-to-end application performance.

To address this challenge, techniques for online performance prediction are needed. Such techniques should make it possible to continuously predict at runtime: a) changes in the application workloads [3], b) the effect of such changes on the system performance, and c) the expected impact of system adaptation actions [1]. Online performance prediction can be leveraged to design systems that proactively adapt to changing operating conditions, thus enabling what we refer to as self-aware performance and resource management [4, 7]. Existing approaches to performance and resource management in the research community are mostly based on coarse-grained performance models that typically abstract systems and applications at a high-level (e.g., [2, 5, 8]). Such models do not explicitly model the software architecture and execution environment, distinguishing performance-relevant behavior at the virtualization level vs. at the level of applications hosted inside the running VMs. Thus, their online prediction capabilities are limited and do not support complex scenarios such as predicting how changes in application workloads propagate through the layers and tiers of the system architecture down to the physical resource layer, or predicting the effect on the response times of different services, if a VM in a given application tier is to be replicated or migrated to another host, possibly of a different type.

To enable online performance prediction in scenarios such as the above, architecture-level modeling techniques are needed, specifically designed for use in online settings. The Descartes Modeling Language (DML) provides such a language for performance and resource management of modern dynamic IT systems and infrastructures. DML is designed to serve as a basis for self-aware systems management during operation, ensuring that system performance requirements are continuously satisfied while infrastructure resources are utilized as efficiently as possible. DML provides appropriate modeling abstractions to describe the resource landscape, the application architecture, the adaptation space, and the adaptation processes of a software system and its IT infrastructure [1, 4, 6]. An overview of the different constituent parts of DML and how they can be leveraged to enable online performance prediction and proactive model-based system adaptation can be found in [6]. A set of related tools and libraries are available from the DML website at: http://descartes.tools/dml.

References

[1]  F. Brosig, N. Huber, and S. Kounev. Architecture-Level Software Performance Abstractions for Online Performance Prediction. Elsevier Science of Computer Programming Journal (SciCo), Vol. 90, Part B:71–92, 2014.

[2] I. Cunha, J Almeida, V. Almeida, and M. Santos. Self-Adaptive Capacity Management for Multi-Tier Virtualized Environments. In IFIP/IEEE Int. Symposium on Integrated Network Management, pages 129–138, 2007.

[3] N. Herbst, N. Huber, S. Kounev, and E. Amrehn. Self-Adaptive Workload Classification and Forecasting for Proactive Resource Provisioning. Concurrency and Computation - Practice and Experience, John Wiley and Sons, 26(12):2053–2078, 2014.

[4] N. Huber, A. van Hoorn, A. Koziolek, F. Brosig, and S. Kounev. Modeling Run-Time Adaptation at the System Architecture Level in Dynamic Service-Oriented Environments. Service Oriented Computing and Applications Journal, 8(1):73–89, 2014.

[5] G. Jung, M.A. Hiltunen, K.R. Joshi, R.D. Schlichting, and C. Pu. Mistral: Dynamically Managing Power, Performance, and Adaptation Cost in Cloud Infrastructures. In IEEE Int. Conf. on Distributed Computing Systems, pages 62 –73, 2010.

[6] S. Kounev, N. Huber, F. Brosig, and X. Zhu. Model-Based Approach to Designing Self-Aware IT Systems and Infrastructures. IEEE Computer Magazine, 2016, IEEE. To appear. http://se2.informatik.uni-wuerzburg.de/pa/uploads/papers/paper-926.pdf

[7] S. Kounev, X. Zhu, J. O. Kephart, and M. Kwiatkowska, editors. Model-driven Algorithms and Architectures for Self-Aware Computing Systems. Dagstuhl Reports. Dagstuhl, Germany, January 2015. http://drops.dagstuhl.de/opus/volltexte/2015/5038/

[8] Qi Zhang, Ludmila Cherkasova, and Evgenia Smirni. A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications. In Proceedings of the 4th International Conference on Autonomic Computing, 2007.

If you like this article, you might also enjoy reading:

• A. Avritzer, J. P. Ros and E. J. Weyuker, "Reliability testing of rule-based systems," IEEE Software, vol. 13, no. 5, pp. 76-82, Sep 1996.
• E. Dimitrov, A. Schmietendorf, R. Dumke, "UML-Based Performance Engineering Possibilities and Techniques”, IEEE Software, vol. 19, no. 1, pp. 74-83, Jan-Feb, 2002.
   
• J. Happe, H. Koziolek and R. Reussner, "Facilitating Performance Predictions Using Software Components," in IEEE Software, vol. 28, no. 3, pp. 27-33, May-June 2011.