Modeling and pricing cloud service elasticity for geographically distributed applications

Cloud service providers (CSP) strive to effectively provision their cloud resources to ensure that their hosted distributed applications meet their performance guarantees. However, accurately provisioning the inter-data centers network resources remains a challenging problem due to the cloud hosted applications´ workload fluctuation. In this paper, we propose a novel approach that enables a CSP to offer Elasticity-as-a-Service (EaaS) for inter-data centers communication in order to guarantee the performance of distributed cloud applications. The contributions of the proposed work are two fold; first, we develop an efficient approach that enables the CSP to estimate and reserve the pool of network resources needed to fulfill the demands imposed by the network workload fluctuations of applications subscribing to this service. The approach allows the CSP to offer communication EaaS at differentiated levels based on the degree of bandwidth-sensitivity of the distributed cloud applications. In order to capture the inter-data centers network activity of hosted applications, we model their workloads using Markovian modeling. The second contribution is a novel dynamic pricing mechanism for network EaaS offerings that can be employed by the CSP to maximize the expected long-term revenue, and to regulate network elastic demands. Performance evaluation results demonstrate the efficiency of our proposed approach, the higher accuracy of our prediction method, and the increase in the CSPs net profit.