# Bouncer: A Resource-Aware Admission Control Scheme for Cloud Services
## 5 Performance Evaluation and Results
### Results and Discussion  
In virtualized environments like cloud data centers, there is a linear relationship between the average response time and the workload of a system. Bouncer resolves system overloading by dropping excessive requests. It also maintains average response time under the required range. For cloud services requesting admission into the system, have a linear relation. We compared Bouncer's average response time with an average response time of three important admission control strategies. It is clearly visible from Figure 3a that the average response time of Bouncer is effective than the other compared schemes. The curve line showing our framework performance also indicates that it resists a steady increase in average response time of request completion even after an increase in the number of job requests. We compared the error rate of all four admission control schemes on our testbed. The experiment is conducted to determine the validity of Bouncer performance with other schemes. From the results in Figure 3b, it is clear that Bouncer serves the most batch jobs with least error percentage whereas LBCP,  
Figure 3: Response time and error rate estimation. (**a**) Average response time; (**b**) error rate. MAP and SJAP having higher error rates. We believe that Bouncer has better performance in reducing error percentage because it delivers special priority to resource-hungry admission control schemes. The acquired error rate also indicates that Bouncer serves the maximum number of successful requests. The performance overheads in virtualized environments are difficult to handle. It is because large data centers are often spread across multiple geo-distributed dominions. Conventional resource isolation techniques have also failed to address these issues. Although the tested workload used in the evaluation of Bouncer is realistic, we consider a case where CPU demands of all the evaluated schemes do not have any communication overheads. We compared the CPU utilization percentage results in Figure 4a. We also calculated and compared the service monitoring overhead costs in Table 2. Results demonstrate that Bouncer incurs fewer overheads than MAP and SJAP whereas its overheads are slightly higher than LBCP. It is mainly because LBCP performs isolation functions on requests and segregates them to achieve higher results. Although this technique improves the results, it slows down the service admission in queued architectures. In contrast, Bouncer's service admission and acceptance process do not constraint system resources. It's simple yet efficient request handler and resource allocation process consumes less CPU time than MAP, SJAP, and LBCP. Our experiments for comparing CPU resource utilization demonstrate that Bouncer achieves higher throughput and acceptable response time with the compared workload. In virtualized environments, an admission control algorithm has a critical role in handling the scalability concerns of a system. If a system is scalable, it is able to handle more requests per unit time. Therefore, to compare the scalability of our model framework, we conduct a simple experiment. We generate a large number of requests and map them on our test bed's processing nodes. The aim of this experiment is to evaluate the throughput of Bouncer. We considered the monitoring results and process execution time and compared them with their respective overheads to calculate the overall system's scalability. The normalized throughput in performed experimentation is measured in requests per second unit. The results in Figure 4b demonstrate that Bouncer algorithm achieves better throughput for the compared number of nodes of a cluster. It is important to mention here that Bouncer is an SLA-aware implementation of policy-based cloud services admission.