Self-Tuning Resource Demand Estimation
Self-Tuning Resource Demand Estimation
复制标题
DOI：
10.1109/icac.2017.19
复制
发表时间：
2017-07
期刊：
影响因子：
--
通讯作者：
Johannes Grohmann;N. Herbst;Simon Spinner;Samuel Kounev
中科院分区：
文献类型：
--
作者：
Johannes Grohmann;N. Herbst;Simon Spinner;Samuel Kounev
在受监视的工作负载组合中，资源处理传入请求所需的平均时间是随机性能模型的一个关键参数。直接测量这些资源需求通常是不可行的，因为仪器开销会在生产环境中造成测量干扰和扰动。因此，文献中提出了许多统计估计方法（例如，基于优化、回归或卡尔曼滤波器），每种方法都有不同的强度和运行时开销。大多数方法提供参数，以便自定义影响估计质量和所需计算时间的估计器的行为。然而，它们的配置通常需要详尽的测试，因为默认参数通常不能提供最佳性能。在本文中，我们提出了一种基于离散优化的自调优方法，可用于自动调整资源需求估计方法的参数，使其适合特定的应用场景，从而提高其准确性。我们在具有不同负载级别和工作负载类数量的代表性数据集上应用并比较了不同的技术。我们表明，我们选择的参数调优方法可以自动提高某些估计器的估计质量，最高可达25%。
The average time a resource needs to process incoming requests in a monitored workload mix is a key parameter of stochastic performance models. Direct measurement of these resource demands is usually infeasible due to instrumentation overheads causing measurement interferences and perturbation in production environments.Thus, a number of statistical estimation approaches (e.g., based on optimization, regression or Kalman filters) have been proposed in the literature each coming with different strengths and run-time overheads. Most approaches offer parameters in order to customize the behavior of the estimator influencing the estimation quality and the required computation time. However, their configuration usually requires exhaustive testing, as default parameters normally do not provide optimal performance.In this paper, we propose a self-tuning approach based on discrete optimization that can be used to automatically tune the parameters of resource demand estimation methods, tailoring them to the specific application scenario and thus improving their accuracy. We apply and compare different techniques on a representative data set with varying load levels and number of workload classes. We show that our selected approach for parameter tuning can automatically improve the estimation quality of certain estimators by up to 25%.