Deploying applications on hybrid clouds with computational artifacts distributed over public backends and private edges involve several constraints. Designing such deployment requires application architects to solve several challenges, spanning over hard regulatory policy constraints as well as business policy constraints such as enablement of privacy by on-prem processing of data to the extent the business wants, backend support of privacy enabling technologies (PET), sustainability in terms of green energy utilization, latency sensitivity of the application. In this paper, we propose to optimize hybrid cloud application architectures, while taking all those factors into consideration, and empirically demonstrate the effectiveness of our approach. To the best of our knowledge, this work is the first of its kind.

We have recently witnessed tremendous success of Machine Learning (ML) in practical applications. Computer vision, speech recognition and language translation have all seen a near human level performance. We expect, in the near future, most business applications will have some form of ML. However, testing such applications is extremely challenging and would be very expensive if we follow today's methodologies. In this work, we present an articulation of the challenges in testing ML based applications. We then present our solution approach, based on the concept of Metamorphic Testing, which aims to identify implementation bugs in ML based image classifiers. We have developed metamorphic relations for an application based on Support Vector Machine and a Deep Learning based application. Empirical validation showed that our approach was able to catch 71% of the implementation bugs in the ML applications.