Szczegóły publikacji
Opis bibliograficzny
ANN-based secure task scheduling in computational clouds / Jacek TCHÓRZEWSKI, Ana Respício, Joanna Kołodziej // W: ECMS 2018 : 32nd European Conference on Modelling and Simulation : May 22nd–25th 2018, Wilhelmshaven, Germany : proceedings / ed. by Lars Nolle, [et al.]. — Germany : ECMS, cop. 2018. — (Proceedings (European Council for Modelling and Simulation) ; ISSN 2522-2414). — ISBN: 978-0-9932440-6-3; e-ISBN: 978-0-9932440-7-0. — S. 468–474. — Bibliogr. s. 474, Abstr. — Toż na płycie CD. — J. Tchórzewski – dod. afiliacja: Cracow University of Technology, Cracow, Poland
Autorzy (3)
- AGHTchórzewski Jacek
- Respício Ana
- Kołodziej Joanna
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 116977 |
|---|---|
| Data dodania do BaDAP | 2018-10-01 |
| Rok publikacji | 2018 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Czasopismo/seria | Proceedings (European Council for Modelling and Simulation) |
Abstract
Assuring the security of services in Computational Clouds (CC) is one of the most critical factors in cloud computing. However, it can complicate an already complex environment due to the complexity of the system architecture, the huge number of services, and the required storage management. In real systems, some security parameters of CC are manually set, which can be very time-consuming and requires security expertise. This paper proposes an intelligent system to support decisions regarding security and tasks scheduling in cloud services, which aims at automating these processes. This system comprises two dierent kinds of Articial Neural Networks (ANN) and an evolutionary algorithm, and has as main goal sorting tasks incoming into CC according to their security demands. Trust levels of virtual machines (VMs) in the environment are automatically set to meet the tasks security demands. Tasks are then scheduled on VMs optimizing the makespan and ensuring that their security requirements are fullled. The paper also describes tests assessing the best congurations for the system components, using randomly generated batches of tasks. Results are presented and discussed. The proposed system may be used by CC service providers and CC consumers using Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) Cloud Computing models.