A Collective Neurodynamic Approach to Survivable Virtual Network Embedding

Although, sharing substrate resources among multiple virtual networks sustains cloud computing with many valuable benefits, it brings critical survivability issues. Single substrate resource failure can cause long service downtime and waste a lot of date from several virtual networks (VNs) [4]. Substrate resource failure becomes a part of everyday operation in today’s Internet Service Provider (ISP) networks [5].

One of the most efficient protection approaches is provisioning redundant resources for each virtual network (VN). Redundant resources enable fast reallocating affected virtual resources after substrate resource failures. Nevertheless, redundant resources increase capacity of required virtual resources, which reduces revenue and reduces acceptance ratio of cloud datacenters.

In this paper, a collective neurodynamic optimization approach has been proposed to reduce amount of required redundant resources and to optimize virtual network embedding. To guarantee virtual network restorability after substrate node failure, the proposed approach enhances virtual network by adding one virtual node and set of virtual links. Virtual networks are enhanced by applying virtual network enhancing design proposed by Guo et al. in [1]. The problem has been formulated as Mixed-Integer Linear Programming and solved by applying neural network proposed by Xia in [6]. To guarantee survivability against substrate link failure, virtual links are embedded by applying multi-path link embedding approach proposed by Khan et al. in [7].

The problem of multi-path link embedding of enhanced virtual network has been formulated as Mixed-Integer Linear Programming and has been solved by using collective neurodynamic optimization approach, which combines the ability of social thinking in Particle Swarm Optimization with the local search capability of Neural Network.

Effectiveness of the proposed approach has been evaluated by comparing its performance with other approaches. Simulation results show that the proposed model reduces required redundant resources and increases revenue.

The rest of this paper is organized as follows. Section 2 describes the related work. Section 3 briefly describes the proposed model. Section 4 experimentally demonstrates the effectiveness of the proposed model. Finally, Section 6 concludes.

Several survivable virtual network embedding (SVNE) approaches have been proposed in the last few years [1]- [4]. Guo et al. [1] have proposed survivable virtual network embedding approach. The proposed approach enhanced virtual network by adding additional virtual resources and redesigning virtual network with considering failure dependent protection technique, which provides backup substrate node for each substrate node failure scenario. Enhanced virtual network has been formulated using binary quadratic programming, and virtual network embedding has been formulated using mixed integer linear programming. Although, the proposed approach reduces amount of required substrate resources to design survivable virtual network, it increases number of required migrations after failures, which increases service down time. www.ijacsa.thesai.org A topology-aware remapping policy has been proposed by Xiao et al. [2] to deal with single substrate node failures. Based on network topology, a set of candidate backup substrate nodes has been defined for each substrate node and a set of candidate backup substrate links has been defined for each substrate link. In [8], Xiao et al. have extended the proposed policy in [2] to handle multiple nodes failures. However, the proposed policy uses all substrate nodes to accommodate incoming virtual networks. Therefore, when a substrate node failure happens, the proposed policy does not grantee that for each migrated virtual node there is a candidate backup substrate node with enough free resources to accommodate migrated virtual node.

Zhou et al. [3] have studied survivability of virtual networks against multiple physical link failures. They have formulated the problem of remapping virtual network with multiple physical link failures using mixed integer linear programming and have proposed an approach to find exact solution for the formulated problem. However, the proposed approach can deal only with small virtual networks.

Qiang et al. [9] have modeled the survivable virtual network embedding problem as an integer linear programming model and have used bee colony algorithm to find near optimal virtual network embedding solution. After substrate node failure,