Architecturing Dynamic Data Race Detection as a Cloud-Based Service

A web-based service consists of layers of programs (components) in the technology stack. Analyzing program executions of these components separately allows service vendors to acquire insights into specific program behaviors or problems in these components, thereby pinpointing areas of improvement in their offering services. Many existing approaches for testing as a service take an orchestration approach that splits components under test and the analysis services into a set of distributed modules communicating through message-based approaches. In this paper, we present the first work in providing dynamic analysis as a service using a virtual machine (VM)-based approach on dynamic data race detection. Such a detection needs to track a huge number of events performed by each thread of a program execution of a service component, making such an analysis unsuitable to use message passing to transit huge numbers of events individually. In our model, we instruct VMs to perform holistic dynamic race detections on service components and only transfer the detection results to our service selection component. With such result data as the guidance, the service selection component accordingly selects VM instances to fulfill subsequent analysis requests. The experimental results show that our model is feasible.