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Samir Youcef

Researcher at French Institute for Research in Computer Science and Automation

Publications -  5
Citations -  19

Samir Youcef is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Software as a service & Quality of service. The author has an hindex of 2, co-authored 5 publications receiving 17 citations.

Papers
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Journal ArticleDOI

Bounding models families for performance evaluation in composite Web services

TL;DR: This work proposes methods to automatically derive from the original model a family of bounding models for the composite Web response time, allowing to find the appropriate trade-off between accuracy of the bounds and the computational complexity.
Proceedings ArticleDOI

Paving the Way towards Semi-automatic Design-Time Business Process Model Obfuscation

TL;DR: This paper proposes a design-time approach for transforming a BP model into BP fragments so that these BP fragments externalized in a multi-cloud context do not allow a cloud resource provider to understand a critical fragment of the company.
Proceedings ArticleDOI

Business Process Compositions Preserving k-Soundness Property

TL;DR: A set of compositional rules that allow to build complex workflows from single ones while preserving k-soundness property under elementary conditions is proposed and for the free-choice Petri net class it is shown that the soundness property verification can be efficiently checked.
Book ChapterDOI

A Methodology for Tenant Migration in Legacy Shared-Table Multi-tenant Applications

TL;DR: In this article, the authors present a solution for scaling in or out of SaaS applications through the migration of a tenant's data to new application and database instances, which requires no change to the application and incurs no service downtime for non-migrated tenants.

Optimisation of business process tenant distribution in the Cloud with a genetic algorithm

TL;DR: This paper presents a cost optimization model and a heuristic based on genetic algorithms to adjust resource allocation to the need of a set of customers with varying BPM task throughput and shows the gain of this method compared to previous approaches.