Inference of Control Structures in Adaptive Networked Systems

A networked system is made up of three layers of abstraction: an infrastructure sub-system that realizes the physical & logical resources, a service-support system that maps the infrastructure components into usable objects, and an adaptation support system that decides on suitable allocation of resources to various applications to attain a desired QoS behavior. The system layers are under purview of different administrative regimes, interacting via inter-layer service interfaces (such as IaaS/SaaS layers in a cloud-hosted service). In this setting, the service-support system has multiple stake-holders: such as QoS adaptation agents, resource-aware end-users, QoS auditors, and reconfiguration managers. These stake-holders often need to dynamically infer the control structures employed in the service-support system (e.g., the topological placement of content caching sites in a CDN). Obtaining an accurate system tomography in the face of changing external environment conditions is critical to the realization of diverse stake-holder goals. The paper outlines the strategies and mechanisms to infer the system-internals for specific needs. Topology inferencing in a rate-adaptive video multicast network of content caching/rendering sites (e.g., NetFlix) is also described.

[1]  Klara Nahrstedt,et al.  A control-based middleware framework for quality-of-service adaptations , 1999, IEEE J. Sel. Areas Commun..

[2]  Donald F. Towsley,et al.  Multicast topology inference from measured end-to-end loss , 2002, IEEE Trans. Inf. Theory.

[3]  Peng Liu,et al.  Incentive-based modeling and inference of attacker intent, objectives, and strategies , 2003, CCS '03.

[4]  Alex Glikson,et al.  SLA-aware resource over-commit in an IaaS cloud , 2012, 2012 8th international conference on network and service management (cnsm) and 2012 workshop on systems virtualiztion management (svm).

[5]  Jean-Chrysostome Bolot,et al.  Characterizing End-to-End Packet Delay and Loss in the Internet , 1993, J. High Speed Networks.

[6]  Kaliappa Nadar Ravindran.  Model-Based Engineering for Certification of Complex Adaptive Network Systems , 2012, 2012 32nd International Conference on Distributed Computing Systems Workshops.

[7]  Jian Ni,et al.  Network Routing Topology Inference from End-to-End Measurements , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[8]  Karl Johan Åström,et al.  BOOK REVIEW SYSTEM IDENTIFICATION , 1994, Econometric Theory.

[9]  Kaliappa Nadar Ravindran.  Multi-source cooperative adaptation for QoE-aware video multicast rate-control , 2013, Proceedings of the 9th International Conference on Network and Service Management (CNSM 2013).

[10]  Monica Palmirani,et al.  Research challenges in legal-rule and QoS-aware cloud service brokerage , 2018, Future Gener. Comput. Syst..

[11]  Matti A. Hiltunen,et al.  Cholla: A Framework for Composing and Coordinating Adaptations in Networked Systems , 2009, IEEE Transactions on Computers.

[12]  Jun Wu,et al.  Distributed adaptation algorithms for rate-controlled video multicast over shared infrastructure networks , 2010, 2010 Second International Conference on COMmunication Systems and NETworks (COMSNETS 2010).

[13]  Mohamed Mohamed,et al.  Security SLA Based Monitoring in Clouds , 2017, 2017 IEEE International Conference on Edge Computing (EDGE).

[14]  Joseph Gualdoni,et al.  Three-Step Approach to QoS Maintenance in Cloud Computing Using a Third-Party Auditor , 2017 .