A bio‐inspired tool for managing resilience in enterprise networks with embedded intelligent formulation. Issue 1 (7th June 2017)

• Record Type: Journal Article
• Authors: Habib, Sami J.; Marimuthu, Paulvanna N.
• Other Names: Rocha Álvaro guestEditor.; Lima Stanley guestEditor.
• Is Part Of: Expert systems. Volume 35:Issue 1(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-06-07
• Subjects: bio‐inspired algorithms -- enterprise evolution -- iIntelligence formulations -- resilience -- workload uncertainty; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12208
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: The bio‐organisms are resilient in nature, where resilience is viewed as a sign of intelligence for understanding and reacting to the unforeseen changes in order to retain their normal functionalities. The resilient behavior within the livings is developed through an evolutionary cycle. Here, we want to provide an enterprise network (EN) with a resilient behavior by embedding intelligent formulations to guide EN through uncertain workload, which is not considered during early design stage. We formulated the resilient management problem within EN as an optimization problem, with an objective function is to maximize the resilience. We developed a bio‐inspired tool comprising of Genetic Algorithm (GA), Molecular Assembly (MA) and Ant Colony Optimization (ACO). Our experimental results on an EN of 100 nodes, with a heavy backbone traffic of 7.6 TB demonstrated that GA gains the maximum resilience of 50%, whereas MA gains 46%, ACO gains 31.54%, and the original EN had 20% resilience. However, with light workload, MA and GA performed closely with a maximum resilience of 64% and 62%, respectively, and ACO showed a moderate resilience with a maximum of 32%.