SDA-CLOUD: A Multi-VM Architecture for Adaptive Dynamic Data Race Detection

Abstract

A concrete service consists of a number of program components, each of which is integrated to the service at either design time or runtime. In testing a concrete service, testers should validate the correctness of each of its components under diverse service consumption scenarios. Analyzing the program executions of these components under different configurations allows developers to compare and pinpoint issues therein. There is surprisingly little work in bridging this gap. In this paper, to the best of our knowledge, we propose the first work in designing dynamic analysis-as-a-service using a multi-virtual machine (multi-VM) approach to dynamic data race detection. Almost all existing work on dynamic data race detection focuses on improving detection precision, efficiency, or coverage of thread interleaving scenarios on the same but single compiled concurrent program component. Our model continually selects VM instances, each hosting a different compiled version of the same program component and running a state-of-the-art detector to detect data races. As such, our model innovatively takes existing race detectors as building blocks and operates at a higher level of abstraction. We have evaluated our proposal through an experiment. The experiment reveals that the multi-VM approach is feasible in monitoring multiple compiled versions and can detect different races both in amount and in detection probability. Under a limited execution budget constraint, the multi-VM approach is also significantly more effective in detecting races than approaches that use single compiled versions only. Some races hidden deeply in one compiled version have been found to be significantly more detectable in some other compiled versions of the same service component.