In this chapter analyses the overall system capacity and scalability which is affected by added traffic introduced by more users who are trying to access the system provided services. This could happen in a mission critical communication system during natural disaster or large scale attack, where the system accessibility could be affected due to the sudden increase of number of users. The need for a more detailed study of other SIP and IMS KPIs is vital to have a better understanding of the overall system performance which will enable us to take it a step further toward system performance enhancement and optimization to avoid single point of failure of the system.
TopIntroduction
IP Multimedia Subsystems (IMS) (Technical Specification Group Services and System Aspects, 2006) and Session Initiation Protocol (SIP) (Rosenberg, et al., 2002) performance play major role in multimedia communication networks by altering the Key Performance Indicators (KPIs) related to the Quality of Experience (QoE) metrics of the end-to-end service. Registration Request Delay (RRD) is one of the SIP KPIs that also has impact over both IMS KPIs and end user QoE. Therefore, it is crucial to have performance evaluation of both SIP and IMS based on RRD metric to give indication of the overall system capacity and scalability potentials.
In this Chapter analyse the overall system capacity and scalability which is affected by added traffic introduced by more users who are trying to access the system provided services. This could happen in a mission critical communication system during natural disaster or large scale attack, where the system accessibility could be affected due to the sudden increase of number of users.
It was also found that the system ability to process the registration requests per unit time is increasing exponentially to a limit with the linear increase of the number of users. After this limit, the number of processed requests will start to decrease and will eventually degrade and lead to system failure. The simulation results shows that the system was able to handle a maximum of 7400 registrations per second which could happen during nationwide disaster with users trying to access the Mission Critical System (MCS).
The need for a more detailed study of other SIP and IMS KPIs is vital to have a better understanding of the overall system performance which will enable us to take it a step further toward system performance enhancement and optimization to avoid single point of failure of the system.
The research methodology that was presented by John W. Creswell (Creswell, 2003) was followed for both the qualitative and quantitative approaches to set the broad lines foe all measurements and simulations. The methodology to decide the qualitative values that need to be investigated can be summarized as follows:
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Determine the challenges that need to be investigated within the scope of the study. As presented in the previous section, it was found that the project embed several challenges and the focus of the project will be in the signalling domain especially between the end user and the core network in addition to the signalling interface between the core network and IMS.
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Determine the benchmark for what is considered accepted SIP performance and decide on the metrics that will be measured to judge and compare the performance of the suggested setup.
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Decide the appropriate simulation tools to get the results from multiple sources to have the appropriate comparison criteria based on the selected tool.
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Determine the key factors that affect the SIP signalling in addition to multimedia services operation in LTE and IMS that affect the overall QoS for the Mission Critical system.
The Quantitative Methodology to get the needed quantitative measures can be summarized as follows:
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Develop a test-bed for the IMS to test the performance of the system. Then decide the performance metrics that need to be collected to be compared with other implementations and scenarios. The details of the experiment will be presented in later sections.
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Develop a virtual machine to generate a virtual clients and running IMS to compare the performance of the system with the running test-bed., then again compare the results with the test-bed outcomes.
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Develop a simulation project for both LTE and IMS over OPNET to investigate the performance of the system and compare it with the test-bed implementation and decide the benchmark that need to be followed for the different performance metrics.
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Determine the variables and parameters in OPNET models and scenarios that need to be changed and manipulated to evaluate the performance of the overall system.