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CAS-005 Load Testing Results and Performance Review

The Central Authentication Service, or CAS-005, is a widely adopted protocol for single sign-on that allows applications to authenticate users securely. Load testing of CAS-005 is crucial to understand how the service behaves under sustained traffic and high-volume authentication events. The primary goal of these tests is to determine the maximum sustainable load for a production CAS environment and to identify the conditions under which performance degradation occurs. By performing controlled trials in a stage environment, the system architects and engineers gain insight into the service’s capacity, resilience, and resource utilization, enabling them to plan deployments that can handle peak demand without failure. Load testing is not only a measure of system performance but also an assessment of how components interact, including memory management, session handling, and CPU utilization.

Deployment Architecture Overview

The CAS stage environment used for these load tests was deployed on a three-node virtual machine configuration. Each node had 3.7 GiB of real memory and was allocated two CPUs. The CPU architecture was x86_64 with both 32-bit and 64-bit operational modes, utilizing Intel Xeon E312xx processors. Each processor contained two cores per socket and one thread per core, resulting in a total of two online CPU threads per node. The virtual machines were deployed behind an Nginx proxy in an active-active-active configuration, which means that all nodes actively handle incoming requests simultaneously. This setup ensures that no single node becomes a bottleneck and that load distribution is managed efficiently. The nodes were interconnected using Hazelcast, a distributed computing framework integrated into CAS, which enabled secure sharing of ticket information and other application states. This architecture is designed to simulate a realistic production environment while allowing careful control and observation of system performance under varying loads.

Test Framework and Methodology

Locust.io was used as the primary load testing framework for these trials. Locust is a Python-based tool that allows for simulating user behavior at scale, generating authentication events against the CAS service, and observing how the system responds over time. In this setup, virtual users, referred to as locusts, were deployed to simulate interactions with the CAS service. These locusts were divided into three lifetime categories: short-lived, medium-lived, and long-lived, with a ratio of 7:2:1. Short-lived locusts persisted for approximately 60 seconds, medium-lived for 5 minutes, and long-lived for up to two hours. Each locust randomly selected credentials from a pool of nine test accounts and had a small probability of entering an incorrect password, leading to immediate termination. When a locust died, it was immediately reborn, retaining its original lifetime category but resetting session and behavioral parameters. This approach created a dynamic and varied load on the CAS service, simulating the mix of transient and persistent users typically seen in production environments. Locusts had a 25 percent chance of logging out upon their expiration, adding complexity to ticket management and memory resource demands.

Conducting the Load Trials

The load trials were conducted progressively, gradually increasing the number of simulated users to observe system behavior at different levels of stress. The first trial introduced 300 locusts with a hatch rate of 10 per second. This trial generated over 3,500 authentication events per minute, including service ticket creation and validation. Performance degradation was noted around the fifth hour of testing, and by the seventh hour, the nodes became overwhelmed, necessitating the termination of the trial. Subsequent trials followed similar patterns but varied in locust population and duration. The second trial briefly increased the locust count to 500 for a short period, demonstrating that while the nodes could handle moderate increases in load, sustained peaks quickly caused resource saturation. The third trial reduced the number of locusts to 150, producing approximately 1,700 authentication events per minute. Unlike the first two trials, this trial concluded without the nodes becoming overwhelmed, leaving open the question of whether this event rate could be sustained indefinitely. The methodology behind these trials ensured that load was applied consistently while monitoring performance indicators such as authentication event rates, session tracking, and resource utilization.

Analysis of Trial Results

Following the initial trials, it became evident that the CAS stage environment exhibited distinct performance patterns under different loads. Trial 1 demonstrated that when 300 locusts were deployed, authentication events occurred at a rate exceeding 3,500 per minute. This high rate tested the limits of the nodes, which are equipped with 3.7 GiB memory and two CPUs each. The trial revealed that the system could sustain a moderate load for several hours before experiencing performance degradation. This degradation manifested as slower response times for authentication events and increased queuing of requests. The significance of these findings lies in understanding that even in a balanced active-active-active configuration, memory and CPU resources are critical for maintaining performance. These observations provided a baseline for assessing the maximum sustainable load and highlighted the importance of session and ticket management as the number of simultaneous users increased.

Trial 2 mirrored the first trial but introduced a short-term increase in locust population to 500. The system managed this brief spike without immediate failure, but the results indicated that sustained high loads could cause rapid resource exhaustion. Observing the nodes’ response under these conditions allowed engineers to understand the thresholds at which the CAS service’s performance begins to deteriorate. By analyzing CPU usage, memory consumption, and ticket tracking efficiency, it became apparent that while the nodes could handle bursts of activity, long-term stability required careful management of authentication events and session durations. This trial underscored the importance of designing CAS deployments that can accommodate both steady-state and peak loads without compromising reliability.

Observations from Reduced Load Trials

Trial 3 reduced the number of locusts to 150, resulting in a sustained authentication event rate of approximately 1,700 events per minute. Unlike previous trials, the nodes did not become overwhelmed, indicating that the system could handle this level of traffic continuously without noticeable degradation. The implications of this trial were significant, as it suggested that a moderate user load could be maintained indefinitely, provided that memory and CPU resources were not exceeded. By monitoring Hazelcast message propagation and ticket expiration, engineers verified that the CAS service maintained accurate session states across all nodes. These findings also demonstrated the efficacy of the active-active-active deployment configuration, which allowed for balanced distribution of authentication requests and prevented any single node from becoming a bottleneck.

Trial 4 further reduced the load to 50 locusts, achieving a sustained authentication event rate of around 600 events per minute. This trial was conducted over 24 hours, confirming that the nodes could maintain consistent performance at this level indefinitely. By analyzing long-lived locust sessions, it became clear that ticket expiration and memory management were functioning as intended. The sustained event rate observed in this trial exceeded the peak usage recorded in production monitoring, which suggested that the CAS service had sufficient capacity to handle spikes in user activity without a significant impact on performance. These results provided valuable insight into the scalability of the CAS service and the thresholds at which additional nodes or resource allocation would be required to maintain desired performance levels.

Session Management and Ticket Tracking

A key factor in the CAS service’s performance is its handling of tickets and user sessions. Each locust in the trials was associated with a Ticket Granting Ticket (TGT) that allowed access to multiple service tickets. Short-lived locusts discarded their tickets quickly, while long-lived locusts retained tickets for up to two hours. The probability of locusts logging out upon expiration was set at 25 percent, meaning that the system had to maintain state for tickets that were still active even after the corresponding locust had “died.” This scenario created memory pressure, particularly during trials with high numbers of long-lived locusts. By monitoring memory usage and observing ticket expiration patterns, engineers could identify potential bottlenecks in session handling and make adjustments to configuration parameters or resource allocation. Efficient ticket management ensures that the CAS service can sustain high authentication rates while minimizing memory consumption and avoiding session leakage.

Hazelcast played a critical role in maintaining synchronization between nodes. By encrypting ticket information and distributing it across all nodes, Hazelcast allowed the CAS service to maintain a consistent view of active sessions and authentication states. This distributed architecture ensured that even under high load, no single node became a point of failure. The load trials highlighted the importance of optimizing Hazelcast configurations to handle large volumes of ticket messages efficiently. Adjustments to network settings, message batching, and memory allocation were necessary to prevent delays in ticket propagation and ensure that all nodes could respond to authentication events without significant latency. These observations informed recommendations for tuning distributed ticket management systems in production deployments.

Statistical Analysis of Authentication Events

The trials provided extensive data on authentication event rates under varying load conditions. Trial 5, which involved only 25 locusts, produced a mean event rate of 291 events per minute. Trial 6, with 10 locusts, produced an average rate of 118 events per minute. By comparing these rates to production measurements, which recorded a mean of 149 events per minute, it became evident that the CAS service operates well below its maximum sustainable load under typical conditions. The median and mode of production events were 139 and 126 events per minute, respectively, with a maximum of 494 events per minute. These values were significantly lower than the sustained rate observed in trial 4, which suggests that the CAS service has considerable capacity to absorb temporary spikes in user activity without degradation in performance.

Analysis of the standard deviation and burstiness of authentication events provided additional insights. The standard deviation of 68 events per minute indicates moderate variability, while occasional bursts of activity could reach nearly 500 events per minute. Comparing this to trial data showed that the system could handle such bursts comfortably, provided that the overall event rate remained below the thresholds identified in the trials. Understanding the statistical distribution of authentication events allows system architects to anticipate performance under different scenarios and plan resource allocation accordingly. It also emphasizes the importance of continuous monitoring and proactive adjustment of system parameters to ensure consistent service quality during peak usage periods.

Performance Implications for Production Environments

The results of the load trials have direct implications for production CAS deployments. By establishing the maximum sustainable load, engineers can design systems that provide reliable service even under heavy usage. The trials demonstrated that the CAS service could maintain high authentication rates without resource exhaustion, provided that session management and ticket tracking were optimized. The active-active-active configuration proved effective in distributing load evenly across nodes, while Hazelcast ensured consistent synchronization of session data. These architectural choices, combined with careful monitoring and tuning, allow organizations to deploy CAS in environments with varying user volumes while maintaining performance and reliability.

Long-Term Load Sustainability

Understanding the long-term behavior of CAS 5 under sustained load is critical for ensuring reliable production performance. Trials 4, 5, and 6 focused on lower locust populations to evaluate how the system handles continuous authentication events over extended periods. Trial 4, with 50 locusts, achieved a rate of approximately 600 authentication events per minute and was sustained over a full 24-hour period. The success of this trial demonstrated that the CAS service could handle moderate traffic indefinitely without experiencing performance degradation. Long-lived sessions were maintained efficiently, and ticket expiration processes functioned as expected. The nodes’ memory and CPU resources remained stable, suggesting that the architecture could accommodate routine traffic as well as temporary spikes, provided that resource utilization remained within observed thresholds.

Trial 5, with 25 locusts, established a mean authentication event rate of 291 events per minute. While this trial was shorter in duration compared to trial 4, it provided valuable insight into system performance at lower loads. Trial 6 further reduced the locust population to 10, producing an average event rate of 118 events per minute. Both trials confirmed that CAS 5 operates well below its maximum capacity during typical production conditions. By analyzing session retention, ticket expiration, and event processing times, engineers were able to determine that the system could sustain steady-state operations indefinitely. These findings suggest that the CAS service has considerable headroom to accommodate sudden increases in authentication requests without a significant impact on performance.

Session Lifecycle and Resource Implications

A key aspect of CAS load testing is evaluating the lifecycle of user sessions and the associated resource implications. In these trials, locusts were assigned varying lifetimes to simulate the diversity of user behavior in a production environment. Short-lived locusts, which persisted for roughly 60 seconds, imposed minimal memory load, as their sessions expired quickly and tickets were released. Medium-lived locusts lasted approximately five minutes, while long-lived locusts maintained sessions for up to two hours. The combination of these categories created a dynamic system load, requiring efficient management of memory, CPU cycles, and ticket tracking. The 25 percent probability that locusts would log out upon expiration introduced additional complexity, forcing the system to retain tickets for some sessions until natural expiration. This mechanism simulated real-world conditions where users may abruptly end sessions or remain connected for extended periods, placing variable demand on system resources.

Monitoring memory usage revealed that long-lived sessions, particularly those that did not log out, could contribute significantly to memory consumption. However, the deployment architecture, including three nodes in active-active-active configuration, was sufficient to distribute this load evenly. Hazelcast synchronization ensured that ticket information remained consistent across all nodes, allowing authentication events to be processed efficiently regardless of session duration. These observations highlight the importance of configuring CAS deployments to handle a mix of transient and persistent users, ensuring that long-lived sessions do not adversely affect overall performance. By understanding session lifecycle dynamics, administrators can optimize memory allocation, ticket expiration policies, and node configurations to achieve balanced and sustainable operations.

Analysis of Authentication Event Patterns

The load trials provided a wealth of data on authentication event patterns and their impact on system performance. Trial 1 and trial 2, with high locust populations, produced event rates exceeding 3,500 per minute, demonstrating the upper limits of the system under stress. These high rates tested not only CPU and memory resources but also the efficiency of ticket creation and validation processes. Observing performance degradation during these trials allowed engineers to identify bottlenecks and determine the thresholds at which additional resources or configuration adjustments were necessary. In contrast, lower load trials produced event rates ranging from 118 to 600 per minute, well below the maximum sustainable rates identified. Comparing these results to production data, which showed a mean event rate of 149 per minute with occasional bursts up to 494, confirmed that the system operates comfortably within its capacity under normal conditions.

The distribution of event types also provided important insights. Service ticket creation and validation were the most common events, accounting for the majority of load on the CAS nodes. By monitoring the duration and resource consumption of these events, engineers could assess the efficiency of the authentication pipeline. Short-lived events generated minimal overhead, while long-lived events required sustained processing and memory allocation. The balance between these event types influenced overall system performance, highlighting the need for careful consideration of user behavior when planning deployment configurations. By analyzing these patterns, system administrators can optimize locust population ratios, session lifetimes, and ticket expiration policies to reflect realistic usage scenarios and ensure consistent service quality.

Role of Distributed Ticket Management

Hazelcast, as the distributed data management component of the CAS deployment, played a central role in maintaining synchronization of ticket information across all nodes. Each authentication event generated messages containing ticket data, which were encrypted and propagated to all nodes to ensure consistency. This distributed approach prevented single-node bottlenecks and allowed the system to scale horizontally by adding additional nodes if required. During high load trials, monitoring Hazelcast performance was critical, as delayed or lost messages could result in inconsistent session states, failed authentication events, or memory leaks. Adjustments to Hazelcast configurations, such as network tuning, message batching, and memory allocation, were necessary to maintain efficiency under peak conditions. Observing how the system handled these distributed messages provided valuable insights into the resilience and scalability of CAS 5 in production environments.

The trials demonstrated that efficient distributed ticket management is essential for sustaining high authentication rates. Long-lived sessions, particularly those that did not log out, created continuous demand for ticket tracking across all nodes. By distributing this responsibility evenly, the system avoided overloading any single node and maintained consistent response times for authentication requests. This approach underscores the importance of considering both local node performance and distributed system behavior when evaluating CAS deployments. Properly tuned distributed ticket management enables the system to handle complex workloads, maintain session integrity, and provide a reliable single sign-on service for large-scale user populations.

Memory and CPU Utilization

A detailed assessment of resource utilization was conducted during all load trials to understand the impact of authentication events on the underlying hardware. CPU usage increased linearly with the number of locusts, with occasional spikes during bursts of authentication requests. Memory usage was influenced primarily by the number of active sessions and the proportion of long-lived locusts that retained tickets without logging out. Trials 1 and 2, with high locust populations, exhibited higher memory pressure, which contributed to performance degradation over time. In contrast, lower load trials showed stable memory consumption, confirming that the system could sustain moderate traffic indefinitely. These findings provide critical guidance for sizing CAS nodes, allocating memory and CPU resources, and planning for future scaling. Understanding the relationship between authentication event rates and resource consumption allows administrators to make informed decisions about infrastructure provisioning and configuration optimization.

The results also highlighted the importance of monitoring and tuning both memory and CPU usage. Excessive memory consumption due to long-lived sessions or high locust populations could lead to swapping, increased latency, or node failure. Similarly, CPU saturation during bursts of authentication events could slow down ticket processing and create bottlenecks in the authentication pipeline. By analyzing resource utilization patterns, engineers identified thresholds at which intervention was necessary, whether through horizontal scaling, tuning session management parameters, or adjusting locust distributions in testing scenarios. These insights form the foundation for robust performance management strategies that ensure reliable CAS operation under a wide range of conditions.

Consolidated Performance Insights

The cumulative results from all six trials provide a comprehensive understanding of the CAS 5 service under varying load conditions. Trials with higher locust populations established upper performance thresholds, while lower load trials highlighted the system’s ability to sustain authentication events over extended periods without degradation. Analysis of production metrics compared to trial results confirmed that the CAS service operates comfortably below its maximum capacity during normal business hours. The mean authentication rate of 149 events per minute in production is well under the sustainable rates observed in trial 4 and significantly lower than the short-term peaks produced in trials 1 and 2. These insights demonstrate that the service has adequate capacity to handle unexpected spikes, transient increases in authentication requests, and long-lived user sessions without compromising performance.

By examining the behavior of locusts across different lifetime categories, it became clear that session retention has a significant effect on resource utilization. Short-lived sessions impose minimal memory load, whereas long-lived sessions, particularly those that do not log out, require the CAS service to maintain state information for extended periods. The trials confirmed that ticket expiration processes and distributed state management via Hazelcast are capable of efficiently handling these requirements. Properly configured deployments can maintain consistent performance even with a mix of session lifetimes, ensuring a smooth user experience and reliable single sign-on functionality. The insights from these tests inform best practices for session management, ticket expiration policies, and node configuration in production environments.

Scalability Considerations

The trials underscore the importance of designing CAS deployments with scalability in mind. The active-active-active node configuration proved effective for load distribution, but understanding the thresholds for CPU and memory utilization is essential for planning horizontal scaling. High-load trials indicated that while the system can tolerate short-term spikes in authentication events, sustained loads above certain thresholds will eventually overwhelm nodes. This observation highlights the need for careful monitoring and the potential addition of nodes or resources in production deployments to maintain performance under future growth. Administrators should consider the balance between node count, session lifetime policies, and network throughput to ensure that the system scales efficiently without introducing latency or instability.

Distributed ticket management is also a critical component of scalable deployments. Hazelcast enables the CAS service to synchronize session and ticket data across all nodes, allowing consistent performance under load. Proper configuration of distributed message propagation, encryption, and memory allocation is necessary to prevent bottlenecks during peak activity. Trials demonstrated that the system could maintain consistent authentication event processing rates across multiple nodes while managing a mix of short-lived, medium-lived, and long-lived sessions. These findings emphasize that scalability is not merely about adding nodes but also about optimizing the interaction between distributed state management, session retention, and authentication processing.

Implications for Resource Management

Resource management is central to achieving sustainable performance for the CAS service. CPU utilization and memory consumption are closely tied to the number and behavior of active sessions. Trials showed that memory pressure increases with the proportion of long-lived sessions that do not log out, and CPU usage spikes during bursts of authentication requests. Understanding these dynamics allows administrators to allocate resources appropriately, configure ticket expiration policies, and monitor system health proactively. Efficient resource management ensures that the CAS service can sustain authentication events indefinitely under typical load conditions while providing a buffer to handle temporary spikes. By combining careful monitoring with proactive adjustments to node configurations, administrators can maintain optimal performance without over-provisioning resources.

Analysis of production data further validates the effectiveness of resource management strategies. Despite occasional bursts reaching 494 events per minute, the system remained well within sustainable thresholds identified in trials. This observation confirms that the CAS service is resilient to fluctuations in load, provided that session management, ticket handling, and node resource allocation are configured correctly. Continuous monitoring of CPU, memory, and ticket processing metrics enables administrators to detect early signs of resource contention and take corrective actions before performance is impacted. These strategies form the foundation of reliable CAS operations, ensuring consistent authentication performance for users in real-world production environments.

Reliability Under Sustained Load

The trials highlight the CAS service’s reliability under sustained load conditions. Trial 4, with 50 locusts generating approximately 600 authentication events per minute over 24 hours, demonstrated that the system could maintain consistent performance without degradation. Even when accounting for long-lived sessions and incomplete logouts, nodes successfully processed authentication events while maintaining accurate ticket states across the distributed environment. Lower load trials confirmed that the system could operate indefinitely at rates corresponding to normal production conditions. These results indicate that the CAS deployment is not only capable of handling routine traffic but also resilient enough to accommodate temporary spikes, ensuring uninterrupted authentication services for users.

Session lifecycle management, particularly the handling of long-lived sessions, contributes significantly to reliability. By maintaining ticket states until expiration and efficiently propagating changes across nodes via Hazelcast, the CAS service ensures that authentication requests are processed consistently. This approach reduces the risk of session conflicts, memory leaks, or lost tickets, which could otherwise compromise reliability. The trials demonstrated that even under mixed session lifetimes and varying logout behaviors, the system maintained integrity and performance, reinforcing confidence in the CAS deployment’s resilience under real-world conditions.

Recommendations for Production Deployment

Based on the insights gained from the load trials, several recommendations emerge for production deployment. First, deploying nodes in an active-active-active configuration provides effective load distribution and redundancy. Second, session and ticket management should be optimized, particularly by balancing short-lived and long-lived sessions to manage memory usage effectively. Third, monitoring resource utilization, including CPU and memory metrics, is essential for detecting potential bottlenecks and adjusting system parameters proactively. Finally, distributed ticket management systems, such as Hazelcast, must be properly configured to handle message propagation, encryption, and memory allocation efficiently. Following these guidelines ensures that CAS deployments remain reliable, scalable, and capable of handling both normal and peak authentication loads.

Regular performance monitoring and periodic load testing are also recommended. Production environments can experience unexpected increases in traffic due to user growth, seasonal peaks, or external system interactions. By repeating controlled load tests similar to those described in these trials, administrators can validate that the system continues to operate within safe performance thresholds. Adjustments to node configurations, session lifetime policies, and resource allocation can then be implemented based on empirical evidence. This iterative approach ensures continuous optimization, sustained reliability, and confidence that the CAS service can accommodate evolving operational demands.

Conclusion

The CAS 5 load tests conducted on the stage environment provide a detailed understanding of system performance, scalability, and reliability. High-load trials established the upper limits of authentication event processing, while lower load and long-duration trials demonstrated the system’s ability to sustain continuous traffic without degradation. Key findings include the importance of efficient session and ticket management, the effectiveness of distributed ticket synchronization via Hazelcast, and the critical role of node resource allocation in maintaining performance. Analysis of production data confirmed that CAS operates well below its maximum sustainable load, providing a buffer to handle temporary spikes and ensuring a reliable authentication service. By following best practices in deployment architecture, resource management, and session handling, administrators can maintain a robust CAS environment capable of supporting diverse user populations and varying operational conditions.

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