The Need of an Optimal QoS Repository and Assessment Framework in Forming a Trusted Relationship in Cloud: A Systematic Review

© 2017 IEEE. Due to the cost-effectiveness and scalable features of the cloud the demand of its services is increasing every next day. Quality of Service (QOS) is one of the crucial factor in forming a viable Service Level Agreement (SLA) between a consumer and the provider that enable them to establish and maintain a trusted relationship with each other. SLA identifies and depicts the service requirements of the user and the level of service promised by provider. Availability of enormous service solutions is troublesome for cloud users in selecting the right service provider both in terms of price and the degree of promised services. On the other end a service provider need a centralized and reliable QoS repository and assessment framework that help them in offering an optimal amount of marginal resources to requested consumer. Although there are number of existing literatures that assist the interaction parties to achieve their desired goal in some way, however, there are still many gaps that need to be filled for establishing and maintaining a trusted relationship between them. In this paper we tried to identify all those gaps that is necessary for a trusted relationship between a service provider and service consumer. The aim of this research is to present an overview of the existing literature and compare them based on different criteria such as QoS integration, QoS repository, QoS filtering, trusted relationship and an SLA

Comparing time series with machine learning-based prediction approaches for violation management in cloud SLAs

© 2018 In cloud computing, service level agreements (SLAs) are legal agreements between a service provider and consumer that contain a list of obligations and commitments which need to be satisfied by both parties during the transaction. From a service provider's perspective, a violation of such a commitment leads to penalties in terms of money and reputation and thus has to be effectively managed. In the literature, this problem has been studied under the domain of cloud service management. One aspect required to manage cloud services after the formation of SLAs is to predict the future Quality of Service (QoS) of cloud parameters to ascertain if they lead to violations. Various approaches in the literature perform this task using different prediction approaches however none of them study the accuracy of each. However, it is important to do this as the results of each prediction approach vary according to the pattern of the input data and selecting an incorrect choice of a prediction algorithm could lead to service violation and penalties. In this paper, we test and report the accuracy of time series and machine learning-based prediction approaches. In each category, we test many different techniques and rank them according to their order of accuracy in predicting future QoS. Our analysis helps the cloud service provider to choose an appropriate prediction approach (whether time series or machine learning based) and further to utilize the best method depending on input data patterns to obtain an accurate prediction result and better manage their SLAs to avoid violation penalties