Adaptive resource allocation for Back-end Mashup applications on a heterogeneous private cloud

On-demand resource provision is one of the key features of cloud computing, allowing applications to grow or shrink on the basis of dynamic workloads. Thus far, most of the research on leveraging on-demand resource provision has focused on batch style applications for scientific computation or Web-centric applications, especially n-tier e-commerce applications. Very little research, however, has focused on leveraging the benefits of cloud computing for applications with alternative architectures. In this paper, we focus on Back-end Mashup applications that have resource-intensive back ends responsible for continuous collection and analysis of real-time data from external services or applications. We present a working prototype back-end mashup application, BuddyMonitor, that allows users of instant messaging services to monitor and analyze the online presence of their “buddies.” The prototype exploits adaptive allocation of cloud resources to scale gracefully in the presence of rapid increases in workload. We demonstrate the feasibility of the approach in an experimental evaluation with a testbed cloud and a realistic simulation of a large scale external XMPP chat service.We conclude that cloud computing with adaptive resource allocation has the potential to increase the usability, stability, and performance of large-scale back-end mashup applications.