Identifying and Estimating Technical Debt for Service Composition in SaaS Cloud

A composite service in multi-tenant SaaS cloud would inevitably operate under dynamic changes on the workload from the tenants, and thus it is not uncommon for the composition to encounter under-utilization and over-utilization on the component services. However, both of those cases could be good or bad: the former implies that although there is under-utilization, the pay-off afterwards are more significant; the latter, in contrast, refers to the over-utilization that leads to trivial pay-off, or nothing at all. Such a notion perfectly matches with the Technical Debt (TD) metaphor in Software Engineering. As a result, it is necessary to identify the root causes of the debts and where the debt can be manifested in the service composition, which, in turn, would offer great helps on the decision making process of service composition. In this paper, we propose a novel approach for identifying the technical debt in service composition under SaaS cloud. The approach combines time series forecasting and a newly proposed technical debt model to estimate the future debt and utility in the service composition. Through a real world case study, we demonstrate that our approach can successfully identify both the good and bad debts, while producing satisfactory accuracy on estimating the technical debt in the service composition under SaaS cloud.