CloudML based dynamic deployment configuration for scaling enterprise applications in the cloud
Name
Iurii Tverezovskyi
Abstract
In light of the popularity of cloud computing, it is really important to be free to change cloud providers when needed. However, in most cases, system configuration relies on provider services, such as load balancing or databases and this makes it much harder to change the provider. This thesis describes a CloudML based solution that is capable of deploying complex systems using different cloud providers with minimum changes including embedded load balancers. This feature allows the creation of scalable configurations that are independent from providers' load balancing services and allow any component of the system to be scalable on demand. The proposed solution also has an integrated generic LP (linear programming) model to control system scaling. We conducted a number of experiments to show that the system could be used for deploying complex systems that follow most popular workflows. The results of the experiments show that this system is capable of scaling properly to support incoming workflow regardless of chosen workflow or number of the system components.
Graduation Thesis language
English
Graduation Thesis type
Master - Software Engineering
Supervisor(s)
Satish Narayana Srirama
Defence year
2015