Virtualized Network Service Topology Exploration Using Nepal

ABSTRACT

Modern communication networks are large, dynamic, and complex. To deploy, maintain, and troubleshoot such networks, it is essential to understand how network elements such as servers, switches, virtual machines, and virtual network functions are connected to one another, and to be able to discover communication paths between them. For network maintenance applications such as troubleshooting and service quality management it is also essential to understand how connections change over time, and be able to pose time-travel queries to retrieve information about past network states. With the industry-wide move to SDNs and virtualized network functions [13], maintaining these inventory databases becomes a critical issue.

We represent a communication network inventory as a graph where the nodes are network entities and edges represent relationships between them, e.g. hosted-on, communicates-with, and so on. We have found that querying such a graph for e.g., troubleshooting, using a typical graph query language is too cumbersome for network analysts.

In this demonstration we present Nepal -- a network path query language which is designed to effectively retrieve desired paths from a network graph. Nepal treats paths as first-class citizens of the language, which achieves closure under composition while maintaining simplicity. The Nepal schema system allows the complexities of items in the inventory database to be abstracted away when desired, and yet provide strongly-typed access. We demonstrate how Nepal path queries can simplify the extraction of information from a dynamic inventory of a multi-layer network and can be used for troubleshooting.

References

1. S. Abiteboul, D. Quass, J. McHugh, J. Widom, and J. L. Wiener. The Lorel query language for semistructured data. International journal on digital libraries, 1(1):68--88, 1997.
2. R. Angles and C. Gutierrez. Survey of graph database models. ACM Comput. Surv., 40(1):1:1--1:39, 2008.
3. P. Buneman, M. Fernandez, and D. Suciu. UnQL: A query language and algebra for semistructured data based on structural recursion. The VLDB Journal, 9(1):76--110, Mar. 2000.
4. A. Dries, S. Nijssen, and L. De Raedt. A query language for analyzing networks. In Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM '09, pages 485--494, 2009.
5. J. Fan, A.G.S. Raj, J.M. Patel. The Case Against Specialized Graph Analytics Engines, CIDR 2015.
6. Y. Kanza and Y. Sagiv. Flexible queries over semistructured data. In Proceedings of the Twentieth ACM Symposium on Principles of Database Systems, PODS '01, pages 40--51, 2001.
7. H. Jagadish, L. V. Lakshmanan, D. Srivastava, and K. Thompson. TAX: A tree algebra for XML. In G. Ghelli and G. Grahne, editors, Database Programming Languages, volume 2397 of Lecture Notes in Computer Science, pages 149--164. Springer Berlin Heidelberg, 2002.
8. H. V. Jagadish, S. Al-Khalifa, A. Chapman, L. V. S. Lakshmanan, A. Nierman, S. Paparizos, J. M. Patel, D. Srivastava, N. Wiwatwattana, Y. Wu, and C. Yu. TIMBER:
9. A native XML database. The VLDB Journal, 11(4):274--291, Dec. 2002.
10. T. Johnson, Y. Kanza, L.V.S. Lakshmanan, V. Shkapenyuk. Nepal: A Path Query Language for Time-Travel Queries over Communication-Network Inventories. Proc. Network Data Analytics Workshop, 2016.
11. K. Kirkpatrick. Software-defined networking. Communications of the ACM, 56(9):16--19, 2013.
12. U. Leser. A query language for biological networks. Bioinformatics, 21(2):33--39, Jan. 2005.
13. Rice, C. ECOMP -- the engine behind our software-centric network. AT&T, 2016. http://about.att.com/innovationblog/031716ecomp
14. M. Sintek and S. Decker. TRIPLE - a query, inference, and transformation language for the semantic web. In The Semantic Web-ISWC 2002, pages 364--378. Springer, 2002.
15. R. Snodgrass, I. Ahn. Temporal Databases. IEEE Computer 19(9), 1986.
16. D. Srivastava, L. Golab, R. Greer, T. Johnson, J. Seidel, V. Shkapenyuk, O. Spatscheck, and J. Yates. Enabling real time data analysis. PVLDB, 3(1):1--2, 2010.
17. O. van Rest, S. Hong, J. Kim, X. Meng, and H. Chafi. PGQL: a property graph query language. In Proceedings of the Fourth International Workshop on Graph Data Management Experiences and Systems, p. 7. ACM, 2016.
18. P. T. Wood. Query languages for graph databases. SIGMOD Rec., 41(1):50--60, Apr. 2012.

Index Terms

• Information systems

  • Data management systems

    • Database design and models

      • Data model extensions

        • Temporal data

      • Graph-based database models

    • Database management system engines

      • Database query processing

• Networks

  • Network services

    • Network management