Search-Based Stress Testing the Elastic Resource Provisioning for Cloud-Based Applications.

One of the main benefits of cloud computing is to enable customers to deploy their applications on a cloud infrastructure that provisions resources (e.g., memory) to these applications on as-needed basis. Unfortunately, certain workloads can cause customers to pay for resources that are provisioned to, but not fully used by their applications, and as a result their performances then deteriorate beyond some acceptable thresholds and the benefits of cloud computing may be significantly reduced or even completely obliterated. We propose a novel approach to automatically discover these workloads to stress test elastic resource provisioning for cloud-based applications. We experimented with four non-trivial applications on the Microsoft Azure cloud to determine how effectively and efficiently our approach explores a very large space of the workload parameters' values. The results show that our approach discovers the first irregular workload faster in the search space of over 10 40 input combinations compared to the random approach, and it discovers more irregular workloads that result in much higher costs and performance degradations for applications in the cloud.