Orchestrated Multi-Cloud Application Deployment in OpenStack with TOSCA

2017 ◽  
Author(s):  
Giuseppe Tricomi ◽  
Alfonso Panarello ◽  
Giovanni Merlino ◽  
Francesco Longo ◽  
Dario Bruneo ◽  
...  
Keyword(s):  
Cloud Application ◽  
Application Deployment ◽  
Multi Cloud

Aeolus: Mastering the Complexity of Cloud Application Deployment

2013 ◽  
pp. 1-3 ◽  
Author(s):  
Michel Catan ◽  
Roberto Di Cosmo ◽  
Antoine Eiche ◽  
Tudor A. Lascu ◽  
Michel Lienhardt ◽  
...  
Keyword(s):  
Cloud Application ◽  
Application Deployment

scholarly journals On the modeling of optimal and automatized cloud application deployment

2019 ◽  
Vol 107 ◽  
pp. 108-135
Author(s):  
Stijn de Gouw ◽  
Jacopo Mauro ◽  
Gianluigi Zavattaro
Keyword(s):  
Cloud Application ◽  
Application Deployment

Event Pattern Discovery in Multi-Cloud Service-Based Applications

2015 ◽  
Vol 5 (4) ◽  
pp. 78-103 ◽  
Author(s):  
Chrysostomos Zeginis ◽  
Kyriakos Kritikos ◽  
Dimitris Plexousakis
Keyword(s):  
Traffic Management ◽  
Service Oriented Architecture ◽  
Cloud Service ◽  
Service Level ◽  
Critical Event ◽  
Service Oriented ◽  
Application Deployment ◽  
And Performance ◽  
Multiple Clouds ◽  
Multi Cloud

The adoption of Cloud computing in the Service Oriented Architecture (SOA) world is continuously increasing. However, as developers try to optimize their application deployment cost and performance, they may also deploy application parts redundantly on different VMs. In such heterogeneous and distributed environments, it is important to have a clear view of the system's state and its components' interrelationships. This paper aims at proposing a novel monitoring and adaptation framework for Service-based Applications (SBAs) deployed on multiple Clouds. The main functionality of this framework is the discovery of critical event patterns within monitoring event streams, leading to specific Service Level Objective (SLO) violations. Furthermore, two main meta-models are proposed for describing the SBA's components and their dependencies, and the supported adaptation actions in a specific context respectively. The proposed approach is empirically evaluated based on a real-world traffic management application.

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