## 5 Performance Evaluation
### Results and Discussion
#### 5.3.1 Impacts of a Varying Number of Incoming Requests  
The impacts of varying the number of incoming requests of the incoming service requests on the performance of our proposed algorithms are manifested in Figure 4.  
The graphs of Figure 4b illustrate that for all the approaches, the average waiting time of the requests will increase as the number of service requests increases. However, for the genetic-based approach (QoSM), it is quite high since the algorithm itself contributes to the waiting time of the requests. On the other hand, to adjust the resources for incoming requests in the news vendor game theory-based approach (NvG), some additional waiting time is observed by each request. Although our proposed approaches schedule the request in priority basis, it also ensures higher probability of the deferred requests to be scheduled in the subsequent scheduling intervals. Due to this consideration,  
Figure 4: Impacts of varying number of arrived requests. (**a**) Percentage of requests satisfying QoS; (**b**) Average waiting time; (**c**) Quality-of-Experience of users; (**d**) Service provider profit. FFP: First Fit Profit; FFS: First Fit Satisfaction.  
average waiting of the requests in this approach is less compared to the existing works. Moreover, as in our proposed FFP algorithm, minimum QoS requirement is maintained, and waiting time is also reduced. As the FFS algorithm focuses on maximizing the time gap between the maximum allowable delay to get a response and summation of waiting time and processing time, among our proposed QEPRA, FFS, and FFP algorithms, FFS provides the best result in terms of reducing the average waiting time, as shown in Figure 4b.  
The performance results of the proposed system in terms of Quality-of-Experience (QoE) are depicted in the graphs of Figure 4c. Our approaches execute the requests according to the priority of the requests which is based on their requirements. As a result, most of the requests get their results much before their deadline, which is eventually reflected in their QoE. As the FFS algorithm is especially designed for QoE maximization, it outperforms the other proposed algorithms as well as the existing works, as illustrated in Figure 4c.  
A comparative study of the service provider profit for a varying number of arrived requests is provided in Figure 4d. In our approaches, on a scheduling interval, when an arrived request with higher scheduling priority executes in the system, the corresponding price of the service is also maximized. Our proposed FFP algorithm targets the maximization of service provider profit by reducing the resource rental cost. The per-unit profit gain also remains increasing as the number of incoming requests increases. QEPRA makes a tradeoff between profit and QoE; on the other hand, in the FFS algorithm, minimum per-unit profit is maintained and so this algorithm provides a better result than the others, except FFP. On the other hand, in the news vendor game-based approach, the scope of maximizing profit will be in its bargaining phase when the total service request is larger than the system capacity. As a result, the profit gain remains static for a certain number of requests in NvG. As QoSM does not address the service provider profit, the average per-unit profit gain always remains constant for the execution of an increasing number of requests.