Violation Detection in Service Level Agreement to Ensure the Privacy in Cloud Community using Chicken Spider Monkey Optimization-Based Deep Belief Network

Abstract

In the cloud service system, the expectation between the customer and the cloud provider for the delivered services like, responsibilities and penalties of the violation arise in the cloud parties are specifically designed in a document named as Service Level Agreement (SLA). However, violation in the Service Level Agreement is a major challenge in the research community of SaaS cloud providers. Hence, an effective violation detection method named Chicken Spider Monkey Optimization-based Deep Belief Network (ChicSMO-based DBN) is proposed in this research to perform the violation detection mechanism in the cloud environment. The proposed Chicken Spider Monkey Optimization-based Deep Belief Network effectively computes the scores, like User Privacy Score (UPS), Service Level Agreement score (SLA), and Cloud Service Provider score (CSP) based on the fitness value. The spider monkeys forage the food by evaluating the distance from the food, and update their position towards the food source. The feature scores are computed by updating the position of local and global leader group members, and perform the violation detection process using the Deep Belief Network classifier. The performance enhancement attained by the proposed algorithm in terms of the evaluation metrics, like accuracy, coverage rate, and F-value which acquire with the values of 0.862, 0.944, and 0.889 with the transaction data of 1000, respectively. It increases the reliability to address the QoS guarantee issues and increase the confidence level of the prediction model.