Prediction‐based survivable virtual network mapping against disaster failures. (22nd June 2016)

• Record Type: Journal Article
• Authors: Wang, Ying; Liu, Xiao; Qiu, Xuesong; Li, Wenjing
• Other Names: Meirosu Catalin guestEditor.; Pentikousis Kostas guestEditor.; Kind Mario guestEditor.; Gonzalez Prieto Alberto guestEditor.
• Is Part Of: International journal of network management. Volume 26:Number 5(2016:Sep./Oct.)
• Page Start: 336
• Page End: 354
• Publication Date: 2016-06-22
• Subjects: Computer networks -- Management -- Periodicals; 004.6
• DOI: 10.1002/nem.1939
• Languages: English
• ISSNs: 1055-7148

Abstract

Summary: Survivable virtual network mapping (SVNM) guarantees that the mapped virtual network works normally against substrate failures. Most of the existing solutions are mainly focusing on single node or single link failure. A long‐standing challenge in SVNM is to reduce the capacity loss of network when substrate failures happen. Because some regions are frequently attacked by disasters, the disaster failures should be paid attention to. In this paper, we re‐consider the existing work on the SVNM and explore the feasible solution of SVNM against disaster failures. We first design the disaster failure model with the knowledge of risk assessment. Then we formulate the problem with the mixed integer programming. Two heuristic algorithms based on the prediction mechanism are proposed. Simulations show that our algorithms increase the average acceptance ratio and reduce the risk of capacity loss in the initial mapping phase compared with previous algorithms. Copyright © 2016 John Wiley & Sons, Ltd. Abstract : The feasible solutions of SVNM against disaster failures are explored. We first design disaster failure model with the knowledge of risk assessment. Then we formulate the problem with the mixed integer programming. Two heuristic algorithms (called minimum fault risk prior selection and asymmetric parallel flow allocation) based on the prediction mechanism are proposed, which use the greedy strategy and the path‐splitting mechanism to reduce the capacity loss and increase the average acceptance ratio in disaster failure scenario.