TY - GEN
T1 - URegM
T2 - 3rd European Symposium on Software Engineering, ESSE 2022
AU - Imran, Asif
AU - Kosar, Tevfik
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/10/27
Y1 - 2022/10/27
N2 - The low cost and rapid provisioning capabilities have made the cloud a desirable platform to launch complex scientific applications. However, resource utilization optimization is a significant challenge for cloud service providers, since the earlier focus is provided on optimizing resources for the applications that run on the cloud, with a low emphasis being provided on optimizing resource utilization of the cloud computing internal processes. Code refactoring has been associated with improving the maintenance and understanding of software code. However, analyzing the impact of the refactoring source code of the cloud and studying its impact on cloud resource usage require further analysis. In this paper, we propose a framework called Unified Regression Modelling (URegM) which predicts the impact of code smell refactoring on cloud resource usage. We test our experiments in a real-life cloud environment using a complex scientific application as a workload. Results show that URegM is capable of accurately predicting resource consumption due to code smell refactoring. This will permit cloud service providers with advanced knowledge about the impact of refactoring code smells on resource consumption, thus allowing them to plan their resource provisioning and code refactoring more effectively.
AB - The low cost and rapid provisioning capabilities have made the cloud a desirable platform to launch complex scientific applications. However, resource utilization optimization is a significant challenge for cloud service providers, since the earlier focus is provided on optimizing resources for the applications that run on the cloud, with a low emphasis being provided on optimizing resource utilization of the cloud computing internal processes. Code refactoring has been associated with improving the maintenance and understanding of software code. However, analyzing the impact of the refactoring source code of the cloud and studying its impact on cloud resource usage require further analysis. In this paper, we propose a framework called Unified Regression Modelling (URegM) which predicts the impact of code smell refactoring on cloud resource usage. We test our experiments in a real-life cloud environment using a complex scientific application as a workload. Results show that URegM is capable of accurately predicting resource consumption due to code smell refactoring. This will permit cloud service providers with advanced knowledge about the impact of refactoring code smells on resource consumption, thus allowing them to plan their resource provisioning and code refactoring more effectively.
KW - resource usage prediction
KW - scientific application in cloud
KW - unified regression modelling
UR - https://www.scopus.com/pages/publications/85148417806
U2 - 10.1145/3571697.3571705
DO - 10.1145/3571697.3571705
M3 - Conference contribution
AN - SCOPUS:85148417806
T3 - ACM International Conference Proceeding Series
SP - 56
EP - 62
BT - ESSE 2022 - 2022 3rd European Symposium on Software Engineering
PB - Association for Computing Machinery
Y2 - 27 October 2022 through 29 October 2022
ER -