Automated QoS-oriented Cloud Resource Optimization Using Containers

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.