Autoscaling systems provide means to automatically change the resources allocated to a software system according to the incoming workload and its actual needs. Public cloud providers offer a variety of autoscaling solutions, ranging from those based on user-written rules to more sophisticated ones. Originally, these solutions were conceived to manage clusters of virtual machines, while more recently, they have also been employed in the operation of containers. This paper analyses the autoscaling solutions provided by three major cloud providers, namely Amazon Web Services, Google Cloud Platform, and Microsoft Azure, and compares them against two solutions we develop based on control theory (ScaleX) and queuing theory (QN-CTRL). We evaluate the different approaches using both an in-house simulation engine and cloud deployments by feeding them with various synthetic and real-world workloads. Our extensive evaluation collects both simulation results and real measurements by which we can assess that both scaleX and QN-CTRL outperform industrial techniques in most cases when considering the trade-offs between the service-level-agreement (SLA) violations and the optimal usage of resources.
Autoscaling Solutions for Cloud Applications under Dynamic Workloads
Pinciroli, Riccardo;Trubiani, Catia;
2024-01-01
Abstract
Autoscaling systems provide means to automatically change the resources allocated to a software system according to the incoming workload and its actual needs. Public cloud providers offer a variety of autoscaling solutions, ranging from those based on user-written rules to more sophisticated ones. Originally, these solutions were conceived to manage clusters of virtual machines, while more recently, they have also been employed in the operation of containers. This paper analyses the autoscaling solutions provided by three major cloud providers, namely Amazon Web Services, Google Cloud Platform, and Microsoft Azure, and compares them against two solutions we develop based on control theory (ScaleX) and queuing theory (QN-CTRL). We evaluate the different approaches using both an in-house simulation engine and cloud deployments by feeding them with various synthetic and real-world workloads. Our extensive evaluation collects both simulation results and real measurements by which we can assess that both scaleX and QN-CTRL outperform industrial techniques in most cases when considering the trade-offs between the service-level-agreement (SLA) violations and the optimal usage of resources.File | Dimensione | Formato | |
---|---|---|---|
2024_IEEETransServComput_EarlyAccess_Quattrocchi.pdf
accesso aperto
Tipologia:
Versione Editoriale (PDF)
Licenza:
Accesso gratuito
Dimensione
2.23 MB
Formato
Adobe PDF
|
2.23 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.