Thank you for the good research paper and presentation contents.I would like to know more details about this research, I would be grateful if you could give the relevant youtube link.
Our paper proposes an improvement using application of Naïve Bayes algorithm (based on Bayes theorem on probability theory to make judgments as well as classify data based on observed and statistical data) to improve the response time of VMs in the cloud, enhance the performance of cloud and cloud infrastructure. This new proposal is named as RCBA (Response Time Classification with Naive Bayes Algorithm). We simulated this proposed RCBA algorithm with the CloudSim engine and the result has improved from 4 popular load balancing algorithms: Round-Robin, FCFS, MaxMin and MinMin.
the RCBA algorithm will use a loop to listen to all the Requests in the queue list of Requests sent to the load balancer (in this case, CloudRequests). Once this list is exhausted, it will no longer be distributed. In it, the algorithm uses the isLocated variable (logical type) to flag that the Request whether has been allocated or not. The first jump of the loop, the isLocated variable is set to false. Then, the algorithm calculates the new Response Time (predicted response time using Naïve Bayes), RT_new variable is to perform the Request in the current situation. This calculation is based on the historical data of previous requests RT1, RT2, ¡¦ RTn where n is the number of requests that have been saved in the LB. Corresponding to each machine, we use the K-Means to cluster the situation of that VM, we get the VM_Cluster variable. The algorithm considers whether the virtual machine matches the predicted RT or not, through the isFitSituation(RT_new , VM_Cluster) function. If it is satisfied, it will allocate the request under consideration to that virtual machine AllocateRequestToVM(VM_Cluster, Request), and at the same time assign the variable isLoacated = true. If no matching virtual machine is found, the loop ends. At this point, the isLocated variable is still false, and now the Request has not been allocated. Therefore, the algorithm allocates this Request to the first VM which gets the nearest means, VM = VMList.getMinFromMean(). This allocation ensures that if any requests are predicted that are not in the data of the algorithm, they are still allocated and processed for the user.
The historical data is always update after the completion of a request processing. We limit a number of requests depending on the requirement and the characteristic of the cloud users.
