Variations in Performance and Scalability When Migrating n-Tier Applications to Different Clouds

TLDR

The growing popularity of cloud computing drives industry and research to address many new and challenging questions. The study evaluates performance and scalability of migrating an n‑tier application from a datacenter to an IaaS cloud and proposes practical solutions to identified bottlenecks. Using the RUBBoS macro‑benchmark, the authors compared performance and scalability across Amazon EC2, Open Cirrus, and Emulab, and explored alternative approaches to mitigate bottlenecks. The best configuration in Emulab can become the worst in EC2, with high context‑switch and network‑driver overhead identified as system‑level bottlenecks confirmed by micro‑benchmark experiments.

Abstract

The increasing popularity of computing clouds continues to drive both industry and research to provide answers to a large variety of new and challenging questions. We aim to answer some of these questions by evaluating performance and scalability when an n-tier application is migrated from a traditional datacenter environment to an IaaS cloud. We used a representative n-tier macro-benchmark (RUBBoS) and compared its performance and scalability in three different test beds: Amazon EC2, Open Cirrus (an open scientific research cloud), and Emulab (academic research test bed). Interestingly, we found that the best-performing configuration in Emulab can become the worst-performing configuration in EC2. Subsequently, we identified the bottleneck components, high context switch overhead and network driver processing overhead, to be at the system level. These overhead problems were confirmed at a finer granularity through micro-benchmark experiments that measure component performance directly. We describe concrete alternative approaches as practical solutions for resolving these problems.