Dynamic provisioning in multi-tenant service clouds

Cloud-based systems promise an on-demand service provisioning system along with a “pay-as-you-use” policy. In the case of multi-tenant systems this would mean dynamic creation of a tenant by integrating existing cloud-based services on the fly. Presently, dynamic creation of a tenant is handled by building the required components from scratch. Although multi-tenant systems help providers save cost by allocating multiple tenants to the same instance of an application, they incur huge reconfiguration costs. Cost and time spent on these reconfiguration activities can be reduced by re-constructing tenants from existing tenant configurations supported by service providers. Multi-tenant cloud-based systems also lack the facility of allowing clients to specify their requirements. Giving clients the flexibility to specify requirements helps them avoid spending an excessive amount of time and effort looking through a list of services, many of which might not be relevant to them. Moreover, dynamic provisioning in the cloud requires an integrated solution across the technology stack (software, platform and infrastructure) combining functional, non-functional and resource allocation requirements. Existing research works in the area of web service matching, although numerous, still fall short, since they usually consider each requirement type in isolation and cannot provide an integrated solution. To that end, in this paper we investigate the features needed for dynamic service provisioning on the cloud. We propose a novel User Interface-Tenant Selector-Customizer (UTC) model and approach, which enables cloud-based services to be systematically modeled and provisioned as variants of existing service tenants in the cloud. Our approach considers functional, non-functional and resource allocation requirements, which are explicitly specified by the client via the user interface component of the model. To the best of our knowledge, ours is the first such integrated approach. We illustrate our ideas using a realistic running example, and also present a proof-of-concept prototype built using IBM’s Rational Software Architect modeling tool. We also present experimental results demonstrating the applicability of our matching algorithm. Our results show significant reduction in matching time with the help of an elimination process that reduces the search space needed for performing matching.