Pons-Escat, L.; Feliu-Pérez, J.; Puche-Lara, J.; Huang, C.; Petit Martí, SV.; Pons Terol, J.; Gómez Requena, ME.... (2022). Effect of Hyper-Threading in Latency-Critical Multithreaded Cloud Applications and Utilization Analysis of the Major System Resources. Future Generation Computer Systems. 131:194-208. https://doi.org/10.1016/j.future.2022.01.025
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/197359
[EN] Multithreaded latency-critical applications represent an important subset of workloads running on public cloud systems. Most of these systems deploy powerful computing servers including Intel Hyper-Threading processors. ...[+]
[EN] Multithreaded latency-critical applications represent an important subset of workloads running on public cloud systems. Most of these systems deploy powerful computing servers including Intel Hyper-Threading processors. Understanding how performance is affected by the consumption of the main system resources is a major concern for cloud providers in order to devise virtualization strategies that improve the system efficiency. With this aim, this paper first characterizes the impact of QPS on tail latency, analyzing different scenarios varying the number of threads and the thread-to-core allocation (single-task and multi-task execution) policy. The characterization study reveals that the performance of some applications does not scale with the number of threads, and the performance of some others is insensitive to the Hyper-Threading technology, so they can be allocated in less physical cores and improve system utilization. Identifying these applications, however, at run-time is challenging. Despite identifying these applications at run-time is challenging, this paper shows that they can be successfully detected at run-time by analyzing the utilization trend of the major system resources. In addition to CPU, we have also studied how assigning the share of each application of other major shared system resources impacts on performance. We outline considerations cloud providers should take into account to improve performance and resource utilization.[-]
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-098156-B-C51/ES/TECNOLOGIAS INNOVADORAS DE PROCESADORES, ACELERADORES Y REDES, PARA CENTROS DE DATOS Y COMPUTACION DE ALTAS PRESTACIONES/ info:eu-repo/grantAgreement/ //FPU18%2F01948//AYUDA PREDOCTORAL FPU-PONS ESCAT. PROYECTO: GESTION EFICIENTE DE RECURSOS COMPARTIDOS EN HIGH-PERFORMANCE COMPUTING Y CLOUD COMPUTING/
Agradecimientos:
Acknowledgments This work has been supported by Huawei Cloud, and in part by Spanish Ministerio de Universidades under grant FPU18/01948, and by Spanish Ministerio de Universidades and European ERDF under grant ...[+]
Acknowledgments This work has been supported by Huawei Cloud, and in part by Spanish Ministerio de Universidades under grant FPU18/01948, and by Spanish Ministerio de Universidades and European ERDF under grant RTI2018-098156-B-C51.[-]