Self-adaptive Decision Making for the Management of Component-Based Applications

Belhaj, Nabila; Belaïd, Djamel; Mukhtar, Hamid

doi:10.1007/978-3-319-69462-7_36

Cited by 3 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [5], the authors presented an approach for improving the decision making process of a MAPE-K loop in order to self-adapt a component-based application. The authors equipped the analyzer component with sophisticated learning blocks, where the decision problem of the analyzer component is modeled as a Markov Decision Process with a finite set of states and actions.…”

Section: Related Workmentioning

confidence: 99%

Optimal Autonomic Management of Service-Based Business Processes in the Cloud

Hadded

Hamrouni

2022

Preprint

View full text Add to dashboard Cite

Cloud computing is an emerging paradigm that provides hardware, platform and software resources as services over the internet in a pay-as-you-go model. It is being increasingly used for hosting and executing service-based business processes. These business processes are exposed to dynamic evolution during their life-cycle due to the highly dynamic evolution of cloud environments. The main adopted technique is to couple cloud computing with autonomic management in order to build autonomic computing systems. Almost all the existing approaches on autonomic computing have been focused on modeling and implementing autonomic mechanisms without paying any attention to the optimization of the autonomic management cost. Therefore, in this paper, we propose a novel approach based on binary linear program for determining the optimal allocation of cloud resources to manage a service-based business process which guarantees the specific requirements of customers and minimizes the management monetary cost. Then, to validate our approach under realistic conditions and inputs, we extend the CloudSim simulator to model and simulate the behaviour of business processes and their management in a cloud environment. Experiments conducted on two real datasets highlight the effectiveness of our approach.

show abstract

Section: Related Workmentioning

confidence: 99%