Xerxes: Distributed Load Generator for Cloud-scale Experimentation

With the growing acceptance of cloud computing as a viable computing paradigm, a number of research and real-life dynamic cloud-scale resource allocation and management systems have been developed over the last few years. An important problem facing system developers is the evaluation of such systems at scale. In this paper we present the design of a distributed load generation framework, Xerxes, that can generate appropriate resource load patterns across varying datacenter scales, thereby representing various cloud load scenarios. Toward this end, we first characterize the resource consumption of four distributed cloud applications that represent some of the most widely used classes of applications in the cloud. We then demonstrate how, using Xerxes, these patterns can be directly replayed at scale, potentially even beyond what is easily achievable through application reconfiguration. Furthermore, Xerxes allows for additional parameter manipulation and exploration of a wide range of load scenarios. Finally, we demonstrate the ability to use Xerxes with publicly available datacenter traces which can be replayed across datacenters with different configurations. Our experiments are conducted on a 700-node 2800-core private cloud datacenter, virtualized with the VMware vSphere virtualization stack. The benefits of such a microbenchmark for cloud-scale experimentation include: (i) decoupling load scaling from application logic, (ii) reslience to faults and failures, since applications tend to crash altogether when some components fail, particularly at scales, and (iii) ease of testing and the ability to understand system behavior in a variety of actual or anticipated scenarios.