Towards AI for Cloud Services Reliability Using Combined Metrics

Tek Raj Chhetri
With the emergence of cloud computing and the Quality of Services (QoS), Compute Power, Performance, and Scalability it offers, the paradigm of computing has shifted towards the cloud. Due to attractiveness cloud offers, today, more and more businesses, research, and individuals are adopting cloud services. Even with the maturity of the cloud, reliability is still a concern. The reason being the constant occurrence of failure causes financial loss as well as a negative impact on its users as it directly affects QoS. Further, the scale and heterogeneity make it more prone to failure, highlighting the necessity for a robust solution to maintain the attractiveness and prevent financial loss. By predicting failure before it could happen, we can improve the reliability. Artificial Intelligence, now, has made significant progress, finding itself a place in all possible areas. In our study we present artificial intelligence with a combined metrics approach to improve the failure prediction. An experiment conducted with data recorded from more than 100 cloud servers shows significant improvement in failure prediction with high prediction accuracy, precision, and recall compared to state of the art studies.
Graduation Thesis language
Graduation Thesis type
Master - Computer Science
Satish Narayana Srirama, Dr. Chinmaya Dehury, Artjom Lind
Defence year