ecms_neu_mini.png

Digital Library

of the European Council for Modelling and Simulation

 

Title:

ANN-Based Secure Task Scheduling In Computational Clouds

Authors:

Jacek Tchorzewski, Ana Respicio, Joanna Kolodziej

Published in:

 

 

 

(2018). ECMS 2018 Proceedings Edited by: Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen European Council for Modeling and Simulation. doi: 10.7148/2018-0005

 

ISSN: 2522-2422 (ONLINE)

ISSN: 2522-2414 (PRINT)

ISSN: 2522-2430 (CD-ROM)

 

32nd European Conference on Modelling and Simulation,

Wilhelmshaven, Germany, May 22nd – May 265h, 2018

 

 

Citation format:

Jacek Tchorzewski, Ana Respicio, Joanna Kolodziej (2018). ANN-Based Secure Task Scheduling In Computational Clouds, ECMS 2018 Proceedings Edited by: Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen European Council for Modeling and Simulation. doi: 10.7148/2018-0468

DOI:

https://doi.org/10.7148/2018-0468

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 com-plex environment due to the complexity of the system architecture, the huge number of services, and the re-quired 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 sup-port decisions regarding security and tasks scheduling in cloud services, which aims at automating these pro-cesses. This system comprises two different kinds of Artificial 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 de-mands. Tasks are then scheduled on VMs optimizing the makespan and ensuring that their security requirements are fulfilled.

The paper also describes tests assessing the best con-figurations 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.

 

Full text: