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VM auto-scaling methods for high throughput computing on hybrid infrastructure

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Abstract

Cloud computing provides on-demand resource provisioning and scalable resources dynamically for the efficient use of computing resources. Scientific applications recently need a very large number of loosely coupled tasks to be handled efficiently. In response, current computing environments often consist of heterogeneous resources such as cloud computing. To effectively use cloud resources, auto-scaling methods that consider diverse metrics such as CPU utilization and costs of resource usage have been studied widely. However it still remains a challenge to automatically and timely allocate resources such that deadline violation and application types are considered. In this paper, we propose auto-scaling methods that consider specific conditions such as application types, task dependency, user-defined deadlines and data transfer times within a hybrid computing infrastructure. Our hybrid computing infrastructure consists of local cluster and cloud resources using HTCaaS. We observe noticeable improvements in performance when our auto-scaling methods for bag-of-tasks and workflow applications is applied.

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Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2013R1A1A3007866).

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Authors and Affiliations

  1. Department of Computer Science, Sookmyung Women’s University, Seoul, 140-742, Korea

    Jieun Choi, Younsun Ahn & Yoonhee Kim

  2. National Institute of Supercomputing and Networking, KISTI, Daejeon, 305-806, Korea

    Seoyoung Kim

  3. School of Computer Science & Engineering, Soongsil University, Seoul, 156-743, Korea

    Jaeyoung Choi

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  1. Jieun Choi
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  2. Younsun Ahn
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  4. Yoonhee Kim
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Correspondence to Yoonhee Kim.

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Choi, J., Ahn, Y., Kim, S. et al. VM auto-scaling methods for high throughput computing on hybrid infrastructure. Cluster Comput 18, 1063–1073 (2015). https://doi.org/10.1007/s10586-015-0462-8

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  • DOI: https://doi.org/10.1007/s10586-015-0462-8

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