CMFog: Proactive Content Migration Using Markov Chain and MADM in Fog Computing

Author(s)

Araujo, Marcelo C. ; Sousa, Bruno ; Curado, Marilia ; Bittencourt, Luiz F. ; IEEE

• Document type: Journal article
• Source: 2020 IEEE/ACM 13TH INTERNATIONAL CONFERENCE ON UTILITY AND CLOUD COMPUTING (UCC 2020); v. N/A, p. 10-pg., 2020-01-01.

Abstract

The popularization of mobile devices has led to the emergence of new demands that the centralized infrastructure of the Cloud has not been able to meet. In this scenario Fog Computing emerges, which migrates part of the computational resources to the edge and offers low latency access to devices connected to the network. Nowadays, many applications have a high level of interactivity and are highly sensitive to latency, thus requiring strategies that allow data migration to follow users' mobility and ensure the QoS (Quality of Service) requirements. In this context, CMFog (Content Migration Fog) is proposed, a proactive migration strategy for virtual machines in the Fog that uses the MADM (Multiple Attribute Decision Making) approach to decide when and where the virtual machine should be migrated. The Markov Chain method is used to predict mobility and to allow migration decisions to be made proactively. The achieved results with CMFog demonstrate a reduction up to 50% in the average latency when compared with the reactive approach used as baseline. (AU)

• FAPESP's process: 18/23064-8 - Mobility in urban computing: characterization, modeling and applications (MOBILIS)
• Grantee: Antonio Alfredo Ferreira Loureiro
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 15/24494-8 - Communications and processing of big data in cloud and fog computing
• Grantee: Nelson Luis Saldanha da Fonseca
• Support Opportunities: Research Projects - Thematic Grants