Affiliations: [a] Department of Information Technology, Shri Andal Algar College of Engineering, Anna University, Chennai, Tamilnadu, India
| [b] Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, India
Correspondence:
[*]
Corresponding author. K. Kotteswari, Department of Information Technology, Shri Andal Algar College of Engineering, Anna University, Chennai, Tamilnadu, India. E-mail: [email protected].
Abstract: Cloud computing is an on-demand model that computes shared and dynamic resource availability in a remote or independent location. Cloud computing provides many services online to clients in a pay-as-you-go manner. Nowadays, many organizations use cloud computing techniques with the prime motive that cost can be reduced, and resources are dynamically allocated. Performance evaluation and measurement approaches for cloud computing help the cloud services consumer to evaluate their cloud system based on performance attributes. Although the researchers have proposed many techniques and approaches in this direction in past decades, none of them has attained widespread industrial benefit. This paper proposes a novel quality evaluation methodology named Stochastic Neural Net (SNN) to evaluate the cloud quality of Infrastructure as a Service (IaaS). This model deeply measures the performance by considering every activity of the IaaS system. Based on their characteristics, these works suggest key QoS factors for individual parts and activities. The individual QoS metric makes the SNN methodology acquire accurate results regarding performance measurement. The performance evaluation result can be used to improve the cloud computing system. The proposed model is compared with other standard models. The experimental comparison shows that the proposed model is more efficient than other standard models.
