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Figure 14 stands for the response time of 5 algorithms with the 4th case. In this case, we can se from the 1st request to the 400th request, RCBA is far away from the rests. But from the 400th onward, RCBA is stable and show its effectiveness. It slowly moves down and reach the smallest from 800th onward. This can tell us that RCBA will be stable for long term and much more request coming. We can see why it dose not fit the beginning due to the historical data storing. Very fast, RCBA can learn the trend, and it adjust for the best fit based on the historical data. In this case, we see MaxMin can perform very well, and always keep the stability during the process. With this number of requests, the non-dynamic and non-machine learning algorithms show the stable and perform not much different from each other¡¯s. But RCBA with Naïve Bayes and KMeans supports, it shows the advantages of Machine Learning and the effectiveness of it. |
IEEE/ICACT20220174 Slide.25
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