Estimating Effective Slowdown of Tasks in Energy-Aware Clouds

Abstract

Abstract-Consolidation consists in scheduling multiple virtual machines onto fewer servers in order to improve resource utilization and to reduce operational costs due to power consumption. However, virtualization technologies do not offer performance isolation, causing applications' slowdown. In this work, we propose a performance enforcing mechanism, composed of a slowdown estimator, and a interference-and power-aware scheduling algorithm. The slowdown estimator determines, based on noisy slowdown data samples obtained from state-of-the-art slowdown meters, if tasks will complete within their deadlines, invoking the scheduling algorithm if needed. When invoked, the scheduling algorithm builds performance and power aware virtual clusters to successfully execute the tasks. We conduct simulations injecting synthetic jobs which characteristics follow the last version of the Google Cloud tracelogs. The results indicate that our strategy can be efficiently integrated with state-of-the-art slowdown meters to fulfil contracted SLAs in real-world environments, while reducing operational costs in about 12%.

PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers

“…To address performance deviations in CPU-intensive applications, we deploy a module combining two blocks [32]: (i) Kalman Filter (KF) noise removal and (ii) the Linear Regression-based (LR) completion estimator. The simple Kalman filter [41] is a powerful estimator that has the ability to smooth noisy data and to provide reliable estimates of signals affected by indirect, inaccurate and uncertain observations (e.g., Gaussian noise).…”