Issue No. 03 - July-Sept. (2015 vol. 3)
Ioan Petri , School of Computer Science & Informatics, Cardiff University, Cardif, Wales, United Kingdom
Javier Diaz-Montes , Cloud and Autonomic Computing Center, Rutgers University, New Brunswick, NJ
Mengsong Zou , Cloud and Autonomic Computing Center, Rutgers University, New Brunswick, NJ
Tom Beach , School of Engineering, Cardiff University, Cardif, Wales, United Kingdom
Omer Rana , School of Computer Science & Informatics, Cardiff University, Cardif, Wales, United Kingdom
Manish Parashar , Cloud and Autonomic Computing Center, Rutgers University, New Brunswick, NJ
Multi-cloud systems have enabled resource and service providers to co-exist in a market where the relationship between clients and services depends on the nature of an application and can be subject to a variety of different Quality of Service (QoS) constraints. Deciding whether a cloud provider should host (or finds it profitable to host) a service in the long-term would be influenced by parameters such as the service price, the QoS guarantees required by customers, the deployment cost (taking into account both cost of resource provisioning and operational expenditure, e.g. energy costs) and the constraints over which these guarantees should be met. In a federated cloud system users can combine specialist capabilities offered by a limited number of providers, at particular cost bands—such as availability of specialist co-processors and software libraries. In addition, federation also enables applications to be scaled on-demand and restricts lock in to the capabilities of a particular provider. We devise a market model to support federated clouds and investigate its efficiency in two real application scenarios:(i)
energy optimisation in built environments and (ii) cancer image processing both requiring significant computational resources to execute simulations. We describe and evaluate the establishment of such an application based federation and identify a cost-decision based mechanism to determine when tasks should be outsourced to external sites in the federation. The following contributions are provided: (i) understanding the criteria for accessing sites within a federated cloud dynamically, taking into account factors such as performance, cost, user perceived value, and specific application requirements; (ii) developing and deploying a cost based federated cloud framework for supporting real applications over three federated sites at Cardiff (UK), Rutgers and Indiana (USA), (iii) a performance analysis of the application scenarios to determine how task submission could be supported across these three sites, subject to particular revenue targets.
Computational modeling, Cloud computing, Data models, Biological system modeling, Buildings, Cancer, Optimization
I. Petri, J. Diaz-Montes, M. Zou, T. Beach, O. Rana and M. Parashar, "Market Models for Federated Clouds," in IEEE Transactions on Cloud Computing, vol. 3, no. 3, pp. 398-410, 2015.