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Automated QoS-oriented cloud resource optimization using containers

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Abstract

Optimizing the deployment of software in a cloud environment is one approach for maximizing system Quality-of-Service (QoS) and minimizing total cost. A traditional challenge to this optimization is the large amount of benchmarking required to optimize even simplistic cloud systems. This paper introduces \(\hbox {C}^2\)RAM, an new approach to enable rapid, optimized deployment of software onto a cloud environment by substantially reducing the number of benchmarks required. \(\hbox {C}^2\)RAM continues to perform some benchmarking, and therefore its predictions of application QoS metrics, such as throughput and latency, are very accurate. Our results show a maximum difference of 1.06 % between \(\hbox {C}^2\)RAM predicted QoS and empirically measured QoS. Moreover, \(\hbox {C}^2\)RAM can be provided with QoS requirements for each software in the system, and will ensure that each requirement is met before presenting a deployment plan.

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Authors and Affiliations

  1. Vanderbilt University, 1025 16th Ave. S., Nashville, TN, 37212, USA

    Jules White, Bo Li & Michael Walker

  2. Virginia Tech, 1185 Perry St., Blacksburg, VA, 24061, USA

    Hamilton Turner

  3. California Polytechnic State University, 3801 W Temple Ave, Pomona, CA, 91768, USA

    Yu Sun

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Sun, Y., White, J., Li, B. et al. Automated QoS-oriented cloud resource optimization using containers. Autom Softw Eng 24, 101–137 (2017). https://doi.org/10.1007/s10515-016-0191-0

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