2026-04-17T06:30:06Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/1864592026-01-22T04:33:24Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc Baig, Shuja-ur-Rehman author Iqbal, Waheed author Berral García, Josep Lluís author Carrera Pérez, David author 2020-03 Accurate prediction of data center resource utilization is required for capacity planning, job scheduling, energy saving, workload placement, and load balancing to utilize the resources efficiently. However, accurately predicting those resources is challenging due to dynamic workloads, heterogeneous infrastructures, and multi-tenant co-hosted applications. Existing prediction methods use fixed size observation windows which cannot produce accurate results because of not being adaptively adjusted to capture local trends in the most recent data. Therefore, those methods train on large fixed sliding windows using an irrelevant large number of observations yielding to inaccurate estimations or fall for inaccuracy due to degradation of estimations with short windows on quick changing trends. In this paper we propose a deep learning-based adaptive window size selection method, dynamically limiting the sliding window size to capture the trend for the latest resource utilization, then build an estimation model for each trend period. We evaluate the proposed method against multiple baseline and state-of-the-art methods, using real data-center workload data sets. The experimental evaluation shows that the proposed solution outperforms those state-of-the-art approaches and yields 16 to 54% improved prediction accuracy compared to the baseline methods. This work is partially supported by the European ResearchCouncil (ERC) under the EU Horizon 2020 programme(GA 639595), the Spanish Ministry of Economy, Industry andCompetitiveness (TIN2015-65316-P and IJCI2016-27485), theGeneralitat de Catalunya, Spain (2014-SGR-1051) and Universityof the Punjab, Pakistan. The statements made herein are solelythe responsibility of the authors. Peer Reviewed Postprint (published version) Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors Cloud computing Data processing service centers Machine learning Resource allocation Sliding windows Adaptive observation window Time series Resource estimation Data center Computació en núvol Centres informàtics Aprenentatge automàtic Assignació de recursos Adaptive sliding windows for improved estimation of data center resource utilization