Cloud-Internet of Things computing paradigms call for novel and efficient method-ologies to decide where to place application services in continuity with Continuous Integration/Continuous Deployment pipelines and infrastructure monitoring. In this article, we present Continuous Adaptation (CA), a new DevOps practice for (1) detect-ing runtime changes in the application requirements or the infrastructure that, due to their change in resource consumption or their effects on the Quality of Service (QoS), can affect the validity and dependability of the current application placement, and for (2) locally intervening on them by suggesting new placements that ensure all (func-tional and non-functional) application requirements are met. We assess a prototype of CA, ConDADO, and analyze its performance over a motivating use case. Con -DADO adapts the application placement to environmental changes through the use of continuous reasoning, reducing the size of the problem to be solved to optimize its performance. The evaluation shows that ConDADO is able to obtain nearly optimal QoS up to 4.2x faster than alternative techniques, also minimizing the cost of service migration.

Herrera, J.L., Berrocal, J., Forti, S., Brogi, A., Murillo, J.M. (2023). Continuous QoS-aware adaptation of Cloud-IoT application placements. COMPUTING, 105(9), 2037-2059 [10.1007/s00607-023-01153-1].

Continuous QoS-aware adaptation of Cloud-IoT application placements

Herrera, Juan Luis;
2023

Abstract

Cloud-Internet of Things computing paradigms call for novel and efficient method-ologies to decide where to place application services in continuity with Continuous Integration/Continuous Deployment pipelines and infrastructure monitoring. In this article, we present Continuous Adaptation (CA), a new DevOps practice for (1) detect-ing runtime changes in the application requirements or the infrastructure that, due to their change in resource consumption or their effects on the Quality of Service (QoS), can affect the validity and dependability of the current application placement, and for (2) locally intervening on them by suggesting new placements that ensure all (func-tional and non-functional) application requirements are met. We assess a prototype of CA, ConDADO, and analyze its performance over a motivating use case. Con -DADO adapts the application placement to environmental changes through the use of continuous reasoning, reducing the size of the problem to be solved to optimize its performance. The evaluation shows that ConDADO is able to obtain nearly optimal QoS up to 4.2x faster than alternative techniques, also minimizing the cost of service migration.
2023
Herrera, J.L., Berrocal, J., Forti, S., Brogi, A., Murillo, J.M. (2023). Continuous QoS-aware adaptation of Cloud-IoT application placements. COMPUTING, 105(9), 2037-2059 [10.1007/s00607-023-01153-1].
Herrera, Juan Luis; Berrocal, Javier; Forti, Stefano; Brogi, Antonio; Murillo, Juan M.
File in questo prodotto:
File Dimensione Formato  
s00607-023-01153-1-1.pdf

accesso aperto

Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione 1.24 MB
Formato Adobe PDF
1.24 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/959538
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
  • ???jsp.display-item.citation.isi??? 5
social impact