1. Evaluation of Distributed Security Systems Server Modules Peak Workload Sergey Panasenkoserg@panasenko.ru, www.panasenko.ru ANCUD Ltd.www.ancud.ru

2. Distributed security systems Dedicated servers manage and monitor client modules, which may perform various security functions, e. g.: access control modules; access control modules; anti-virus software; anti-virus software; firewalls; firewalls; virtual private network modules; virtual private network modules; intrusion detection/prevention sensors; intrusion detection/prevention sensors; identification and authentication modules. identification and authentication modules. 2

3. Example #1: distributed access control system 3

4. Typical workload profile Typical workload/time dependence graph of a database server: [Oracle database performance tuning guide. 10g release 2, http://download.oracle.com, 2008.] 4

5. Evaluation of Server Modules Peak Workload Average and peak workload of distributed access control systems server modules should be evaluated before their deployment. This allows to determine an amount of required resources and to provide clustering. Also, this helps to balance the server modules workload if needed. Let us try to determine peak workload on distributed security systems server modules. 5

6. Peak workload of distributed access control systems where: – a number of access control boards; each of them authenticates a user times a day in average; N C – a number of access control boards; each of them authenticates a user N A times a day in average; – an average number of time units required to process one authentication request; T A – an average number of time units required to process one authentication request; – average resources of the server needed while authentication request processing per time unit; P A – average resources of the server needed while authentication request processing per time unit; – the number of time units per a day. T T – the number of time units per a day. 6 Average server workload per time unit:

7. Peak workload of distributed access control systems where: – the length of peak workload periods in a day in aggregate; T P – the length of peak workload periods in a day in aggregate; – a coefficient, which is defined as a ratio of a number of user authentication requests during peak workload periods to a total number of such requests per a day. C P – a coefficient, which is defined as a ratio of a number of user authentication requests during peak workload periods to a total number of such requests per a day. 7 Average server workload in peak periods:

8. Generalization on any distributed security system where: – an average amount of -type operations for each client module during a day; N n – an average amount of n -type operations for each client module during a day; – an average amount of time units required to perform one -type operation; T n – an average amount of time units required to perform one n -type operation; – average resources of the server needed while processing one -type operation per time unit. P n – average resources of the server needed while processing one n -type operation per time unit. 8 Average server workload per time unit:

9. Example #2: multi-purpose distributed system 9

10. Multi-purpose distributed system server workload profile 10

11. Peak workload of multi-purpose distributed systems where: – represents a ratio of a number of -type operations during the time period with length of time units to a total number of -type operations per a day. C n,T – represents a ratio of a number of n -type operations during the time period T with length of T P,T time units to a total number of n -type operations per a day. 11 Average server workload for -type operations processing during the peak time period : Average server workload for n -type operations processing during the peak time period T :

12. Peak workload of multi-purpose distributed systems 12 Average server workload for all types of operations during the same time period : Average server workload for all types of operations during the same time period T :

13. Conclusion 1.Peak workload periods may be predicted and evaluated early during the development of distributed security systems server modules. 2.Such evaluation allows to determine the sufficient level of server resources before system deployment. 3.An alternative way is to make server workload as uniform as possible to avoid distinct periods of peak workload. 13

14. Thank you! Sergey Panasenkoserg@panasenko.ru, www.panasenko.ru ANCUD Ltd.www.ancud.ru