Castelldefels Project: Simulating the Computer System that Gives Support to the Virtual Campus of the Open University of Catalonia

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1 22nd EUROPEAN CONFERENCE ON OPERATIONAL RESEARCH Prague, July 8 11, 2007 Castelldefels Project: Simulating the Computer System that Gives Support to the Virtual Campus of the Open University of Catalonia Angel A. Juan Dep. of Applied Mathematics I - EPSEB Technical University of Catalonia (Spain) angel.alejandro.juan@upc.edu Javier Faulin Dep. of Statistics and Operations Research Public University of Navarra (Spain) javier.faulin@unavarra.es Miguel A. Arquimbau Joan M. Marquès Computer Science Studies Open University of Catalonia (Spain) {marquimbau, jmarquesp}@uoc.edu

2 1. INTRODUCTION Computer networks have become extremely important in our day-to-day life. Most companies and institutions depend on the appropriate functioning of their computer networks In order to analyze computer networks performance, both analytical and simulation methods can be used Advantages of discrete-event simulation methods (DES) over analytical ones: DES allows to model in detail the dynamic nature of real computer networks DES allows to obtain information about the network internal behavior (not only about its performance) DES allows to test several designs by performing what-if analysis

3 2. THE OPEN UNIVERSITY OF CATALONIA (UOC) The Open University of Catalonia (UOC) is a completely on-line university located at Barcelona, Spain UOC Virtual Campus offers on-line services to students, faculty and administrative staff (about 37,000 users, distributed among different Latin-American countries) The computer architecture that supports all these services must ensure: Speed, reliability, availability, safety and scalability An efficient global performance Unfortunately, it is not possible to perform what-if analysis using the real system and, therefore, modeling & simulation is the natural choice to develop performance studies on this system

4 3. THE CASTELLDEFELS RESEARCH PROJECT Castelldefels is the name of the computer system that gives support to the UOC Virtual Campus Main objective of the project: to improve the system performance levels by performing what-ifanalysis over hardware and software components and by selecting the optimal values for system configuration parameters (e.g.: network topology, # and type of hardware devices, queuing and balancing policies, services & protocols to implement, etc.) The computer system is complex & dynamic Long-term project (updates). At current stage, we focus on: the load balance mechanisms (two dedicated hardware devices perform this task) the session persistence requirements (when a user introduces its login and password and gets access to the intranet, all information associated to her is loaded from the database into a front-

5 4. DESCRIPTION OF THE CASTELLDEFELS SYSTEM The network system that gives support to the UOC intranet is composed by routers, firewalls, load balancers, web accelerators, switches and dedicated servers How does it work? A user types the University URL in a web browser The user petition crosses through the frontier routers and firewalls and reaches the load balancer, which sets up a session with one of the 3 portal servers. A portal page is loaded into the user browser Once the user has introduced her login and password (and this has been validated against the database server), she enters the intranet (her request has been balanced and it has finally arrived to one of the 25 available front-end servers)

6 5. OPNET SIMULATION SOFTWARE OPNET is a simulation software specially designed to model & simulate computer networks. OPNET Modeler, the main module, provides a Virtual Network Environment that allows to model and simulate the behavior of an entire computer or telecommunication network (WAN, LAN, WI-FI, P2P, etc.) Why did we use OPNET?: it seemed to be the most widely tested, used and documented software in the network simulation area a free license for academic and research use was available from the software developer OPNET Modeler offers advanced tools for model design, simulation, data mining and analysis

7 6. MODELING THE CASTELLDEFELS SYSTEM WAN level First stage of the project: we reduced somewhat the model size of the Campus network, assuming that it has less servers, users and devices than the real system has Main components of this model: Applications (HTTP, FTP, ) User profiles (one general profile with support for all considered applications) Servers (6 HTTP-FTP front-ends and 2 servers) Load balancer with failures and repairs (balancer policies: Round-Robin for HTTP-FTP, and # of connections for e- mail) Firewall and Internet Router User network (up to 250 concurrent clients) LAN level

8 7. SIMULATION RESULTS: EXPERIMENT 1: Basic Model Objective: to construct a first model using the advanced Opnet Modeler Five different scenarios concerning up to four front-end servers were balanced during ten simulated hours, with 50 workstations requesting HTTP services; the scenarios were: (a) just one server, (b, c, and d) 3 servers using different balancing policies (no balanced, random and round-robin), and (e) 4 servers using a round-robin balancing algorithm Results: as expected, applying a balancing policy significantly improves performance: more traffic is allowed and response-time is decreased no significant difference among system performances when applying different balancing policies Balancing more traffic allowed No significant difference among balancing policies

9 8. SIMULATION RESULTS: EXPERIMENT 2: Firewalls Objective: to verify the firewall correct behavior by studying system performance depending on its activation status (on/off) Four front-end servers (3 HTTP and 1 SMTP) were balanced during ten simulated hours, with one activated firewall performing between the router and the balancer (the firewall was set up so that it filtered all FTP traffic) Results: as expected, no FTP traffic is observed while the firewall is up; other services are not affected by the firewall configuration firewall performance is verified (we can use firewall policies to filter traffic associated to some services and see how it affects global performance) With FTP filtering Without FTP filtering

10 9. SIMULATION RESULTS: EXPERIMENT 3: Failure & Repair Objective: to verify that the balancer is monitoring the servers, detecting when a server is down (and, therefore, re-directing traffic to other servers) and when it is up again (therefore, considering it again when re-distributing input traffic) Four front-ends servers (3 HTTP and 1 SMTP) were balanced during ten simulated hours, considering controlled server-failure and serverrepair events on server 1 Results: as expected, server 1 performs fine during the first 2 hours (the failure has been set at this target-time and established to last during the next 6 hours) as expected, after 6 hours of inactivity, server 1 gets back to work and the balancer adds it again to the list of available servers Balancer is monitoring servers

11 10. SIMULATION RESULTS: EXPERIMENT 4: Balancing Policies Objective: to compare four different balancing policies: random, round-robin, # of connections, and server load Four different scenarios concerning seven frontends (5 HTTP/FTP and 2 SMTP) were balanced during twenty simulated hours; the scenarios were based on the applied balancing algorithm: (a) random, (b) round-robin, (c) # of connections, and (d) minimum server load Results: as results obtained with Opnet IT Guru, round-robin tends to be the most efficient when distributing the load among the servers when using the # of connections as a balancing policy, load tends to stabilize at different levels depending on the server Round-robin (equally distrib.) # of connections (not equally distrib.)

12 11. SIMULATION RESULTS: EXPERIMENT 5: Server CPU utilization Objective: to compare server CPU utilization for two different WAN configurations Two different scenarios concerning seven frontends (5 HTTP/FTP and 2 SMTP) were balanced during 1 simulated hour; two different WAN models were compared, one based on the use of LANs to model users and other based on the use of individual nodes Results: In both scenarios, total IP traffic received by the servers were the same In the LANs based scenario, server CPU utilization was about 25% (coherent with real system rates) while in the case of the scenario based on individual nodes, CPU utilization was about 2% ( this scenario does not mimic the real system! It is not validated) Total IP traffic CPU utilization

13 12. ACADEMIC APPLICATIONS It is expensive to set up a physical networking lab for university students. Moreover, such labs would have significant limitations when dealing with different possible scenarios (only some LANs could be considered). Therefore, use of discrete-event simulation is not only interesting in the professional arena but also in the academic one Visit the project website for more information and new developments:

14 13. CONCLUSSIONS When analyzing computer networks, simulation techniques offer advantages over analytical ones: the opportunity of creating models which faithfully mimics the real network the possibility of obtaining additional information about its internal functioning We have used OPNET to develop a prototype model of the computer system that gives support to the UOC intranet; the model allows us to start experimenting with some what-if scenarios More work is being developed to mimic the real system characteristics, so that derived results will help managers to take strategic decisions regarding the system. At this initial stage, though, some interesting results have been already obtained Finally, we have discussed how discrete-event simulation software can add value to the network engineers training process DISCRETE EVENT SIMULATION CASTELLEDEFELS PROJECT RESEARCH TEACHING COMPUTER NETWORKS ANALYSIS

15 22nd EUROPEAN CONFERENCE ON OPERATIONAL RESEARCH Prague, July 8 11, 2007 Castelldefels Project: Simulating the Computer System that Gives Support to the Virtual Campus of the Open University of Catalonia Thank you! Angel A. Juan Dep. of Applied Mathematics I - EPSEB Technical University of Catalonia (Spain) angel.alejandro.juan@upc.edu Javier Faulin Dep. of Statistics and Operations Research Public University of Navarra (Spain) javier.faulin@unavarra.es Miguel A. Arquimbau Joan M. Marquès Computer Science Studies Open University of Catalonia (Spain) {marquimbau, jmarquesp}@uoc.edu