With the advent of big data applications, which tend to have longer execution time, choosing the right cloud VM has significant performance and economic implications. For example, in our large-scale empirical study of 107 different workloads on three popular big data systems, we found that a wrong choice can lead to a 20 times slowdown or an increase in cost by 10 times. Bayesian optimization is a technique for optimizing expensive (black-box) functions. Previous work has only used instance-level information (such as core counts and memory size) which is not sufficient to represent the search space. In this work, we discover that this may lead to the fragility problem-either incurs high search cost or finds only the sub-optimal solution. The central insight of this paper is to use low-level performance information to augment the process of Bayesian Optimization. Our novel low-level augmented Bayesian Optimization is rarely worse than current practices and often performs much better (in 46 of 107 cases). Further, it significantly reduces the search cost in nearly half of our case studies. Based on this work, we conclude that it is often insufficient to use general-purpose off-the-shelf methods for configuring cloud instances without augmenting those methods with essential systems knowledge such as CPU utilization, working memory size and I/O wait time.
A poor choice, for example, can lead to a 20 time slowdown or a 10 times increase in total cost REF .
Arrow: Low-Level Augmented Bayesian Optimization for Finding the Best Cloud VM
2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS)
2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS)
Computer Science