Technical Papers Session VI: Dynamic cloud resources allocation
Abstract/Description
Many software companies have clients that use Microsoft Azure services. Clients may have varying needs for resources, so Microsoft Azure has a very dynamic feature called elastic pool that allows resources to expand and shrink automatically on demand. However, this dynamic feature is very costly both for the clients and the software companies. Thus, there is a growing need to be able to predict the usage ahead of time on daily basis. In this paper we propose and develop an intelligent usage prediction model using the user's resource usage history. According to our research, the work done till date is limited to other specific cloud providers or private servers but none related to Microsoft Azure. The classification algorithm that we use is LSTM. However, we have also report and document results obtained by ARIMA, SVM and Bayesian Networks. The best performance is given by LSTM.
Keywords
Software companies, Dynamic feature, Elastic pool, Intelligent usage prediction model, Cloud providers, Microsoft Azure services, Dynamic cloud resource allocation
Location
Room C9 (Aman Tower, 3rd floor)
Session Theme
Technical Papers Session VI - Networks
Session Type
Parallel Technical Session
Session Chair
Dr. Syed Hyder Abbas Musavi
Start Date
17-11-2019 3:00 PM
End Date
17-11-2019 3:20 PM
Recommended Citation
Sultan, S., Asad, A., Abubakar, M., Khalid, S., Ahmed, S., & Wali, A. (2019). Technical Papers Session VI: Dynamic cloud resources allocation. International Conference on Information and Communication Technologies. Retrieved from https://ir.iba.edu.pk/icict/2019/2019/45
COinS
Technical Papers Session VI: Dynamic cloud resources allocation
Room C9 (Aman Tower, 3rd floor)
Many software companies have clients that use Microsoft Azure services. Clients may have varying needs for resources, so Microsoft Azure has a very dynamic feature called elastic pool that allows resources to expand and shrink automatically on demand. However, this dynamic feature is very costly both for the clients and the software companies. Thus, there is a growing need to be able to predict the usage ahead of time on daily basis. In this paper we propose and develop an intelligent usage prediction model using the user's resource usage history. According to our research, the work done till date is limited to other specific cloud providers or private servers but none related to Microsoft Azure. The classification algorithm that we use is LSTM. However, we have also report and document results obtained by ARIMA, SVM and Bayesian Networks. The best performance is given by LSTM.
