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As multimedia applications demand on ISPs rise, effective network management is essential. This paper introduces the Network Management Game (NMG), a simulation framework designed to train network administrators in a risk-free, game-based environment. By replacing traditional lengthy apprenticeships with interactive scenarios, we evaluated user performance through experiments, demonstrating significant improvements in skill acquisition and throughput optimization. Results indicate that targeted training using NMG leads to a 16% increase in performance, outperforming standard algorithms in dynamic situations.
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Network Management Game EnginArslan, Murat Yuksel, Mehmet H. Gunes LANMAN 2011 North Carolina
Outline • Motivation • Related Work • Network Management Game (NMG) Framework • User Experiments & Results • Conclusion
Motivation • High demand for multimedia applications (VoIP, IPTV, teleconferencing, Youtube) • ISPs have to meet customer demand Service Level Agreement (SLA) • Network management and automated configuration of large-scale networks is a crucial issue for ISPs • ISPs generally trust experienced administrators to manage network and for better Traffic Engineering
Training Network Administrators • Network administrator training is a long-term process • Exposing inexperienced administrators to the network is too risky • Current practice to train is apprenticeship Can we train the network administrators using a game-like environment rather than months of years of apprenticeship?
Related Work • Training by virtualized game-like environment • Pilot training • Investor training • Commander training • Compeauet al. : End-user training and learning Deborah Compeau, Lorne Olfman, MaungSei, and Jane Webster. 1995. End-user training and learning. Commun. ACM 38, 7 • Chatham et al. : Games for training Ralph E. Chatham. 2007. Games for training. Commun. ACM 50, 7 (July 2007), 36-43. • Network administrator programs:Cisco Certification
Change link weight Framework Network Configuration Display traffic 1 3 7 2 6 Graphical User Interface Traffic traces Simulation Engine (NS-2) 4 5 Calculate new routes Block diagram of Network Management Game (NMG) components.
Network Simulator (NS-2) NS-2 System Configuration Output • No real time interactivity Run simulation See the results • Necessitates adequate level of TCL scripting • Not designed for training purpose
Simulator-GUI Interaction • Concurrencyis challenging • Run the simulation engine for a time period then animate in GUI before the engine continues • Slowdown animator – chose this approach • GUI-Engine interaction is achieved via TCP port • Animator opens a socket to send simulation traces • GUI opens a socket to send commands Sample Message: $ns $n1 $n2 2 set weight of link between n1 and n2 to 2
User Goal • Increase Overall Throughputby manipulating link weights within a given time period B 1Mbps 1Mb/s 1Mb/s E A C D 3Mb/s 3Mbps 3Mb/s 4Mb/s
User Goal VIDEO
User Experiments We conducted 2 user experiments • Training without Mastery • No specific skills targeted • No success level obligated • Training with Mastery • Two skills are targeted to train • Success level obligated Introduction| Related Work | NMG Framework |User Experiments| Conclusion
Training without Mastery • 5 training scenarios • For every scenario, user has fixed 3-5 minutes to maximize overall throughput • 8 users attended • Took around 45 minutes for each user • User performance evaluated for failure and no failure cases
User Experiment Failure scenarios No failure scenarios Tutorial 6 7 1 2 3 4 5 • 6’ 7’ Before Training Training After Training
No Failure Case P-test value :0.0002 Before Training After Training 16% increase
Failure Case Users outperform heuristic solutions P-test value: 0.27 After Training Before Training 2.2% increase
Training with Mastery • Two skills are targeted • High bandwidth path selection • Decoupling of flows • 7 training scenarios 7 levels • Success level is obligated to advance next level • 5 users attended • Took 2-3 hours on average per user Introduction| Related Work | NMG Framework |User Experiments| Conclusion
Training with Mastery Tutorial 8 1 2 3 4 5 • 6 • 7 • 8’ Before Training Training After Training Introduction| Related Work | NMG Framework |User Experiments| Conclusion
Results of Training with Mastery P-test value: 0.00001 Introduction| Related Work | NMG Framework |User Experiments| Conclusion
Conclusion • Performance of a person in network management can be improved via our tool • 16% improvement first user experiment • 13%- 21% improvement second user experiment • People outperform heuristic algorithms in case of dynamism in network • Targeting skills and designing specific scenarios for skills lead better training • Success level of second user training Introduction| Related Work | NMG Framework | User Experiments|Conclusion
Future Work • Extend for large scale networks • Extend quantity and quality of test cases • Using different metrics in addition to throughput such as delay or loss • Improve for investment based simulations (what-if scenario) • Simulate multiple link failure (disastrous scenario)
Thank you! For offline questions: enginars@buffalo.edu
Related Work • Ye et al. :Large-scale network parameter configuration using an on-line simulation framework Tao Ye, Hema T. Kaur, ShivkumarKalyanaraman, and Murat Yuksel. 2008. Large-scale network parameter configuration using an on-line simulation framework. IEEE/ACM Trans. Netw • Gonen et al. :Trans-Algorithmic search for automated network management and configuration B. Gonen, etal. Probabilistic Trans-Algorithmic search for automated network management and configuration. In IEEE International Workshop on Management of Emerging Networks and Services (IEEE MENS 2010 • Wang et al. :IGP weight setting in multimedia ip networks R. D. D. Wang, G. Li, “Igp weight setting in multimedia ipnetworks,”inIEEE Infocom Mini’07, 2007.
