Understanding, modelling, and improving the performance of web applications in multicore virtualised environments

  • Authors:
  • Xi Chen;Chin Pang Ho;Rasha Osman;Peter G. Harrison;William J. Knottenbelt

  • Affiliations:
  • Imperial College London, London, United Kingdom;Imperial College London, London, United Kingdom;Imperial College London, London, United Kingdom;Imperial College London, London, United Kingdom;Imperial College London, London, United Kingdom

  • Venue:
  • Proceedings of the 5th ACM/SPEC international conference on Performance engineering
  • Year:
  • 2014

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Abstract

As the computing industry enters the Cloud era, multicore architectures and virtualisation technologies are replacing traditional IT infrastructures. However, the complex relationship between applications and system resources in multicore virtualised environments is not well understood. Workloads such as web services and on-line financial applications have the requirement of high performance but benchmark analysis suggests that these applications do not optimally benefit from a higher number of cores. In this paper, we try to understand the scalability behaviour of network/CPU intensive applications running on multicore architectures. We begin by benchmarking the Petstore web application, noting the systematic imbalance that arises with respect to per-core workload. Having identified the reason for this phenomenon, we propose a queueing model which, when appropriately parametrised, reflects the trend in our benchmark results for up to 8 cores. Key to our approach is providing a fine-grained model which incorporates the idiosyncrasies of the operating system and the multiple CPU cores. Analysis of the model suggests a straightforward way to mitigate the observed bottleneck, which can be practically realised by the deployment of multiple virtual NICs within our VM. Next we make blind predictions to forecast performance with multiple virtual NICs. The validation results show that the model is able to predict the expected performance with relative errors ranging between 8 and 26 per cent.