Energy-Aware Virtual Network Embedding

Abstract

Network virtualization has caught the attention of many researchers in recent years. It facilitates the process of creating several virtual networks over a single physical network. Despite this advantage, however, network virtualization suffers from the problem of mapping virtual links and nodes to physical network in most efficient way. This problem is called virtual network embedding ("VNE"). Many researches have been proposed in an attempt to solve this problem, which have many optimization aspects, such as improving embedding strategies in a way that preserves energy, reducing embedding cost and increasing embedding revenue. Moreover, some researchers have extended their algorithms to be more compatible with the distributed clouds instead of a single infrastructure provider ("ISP"). This paper proposes energy aware particle swarm optimization algorithm for distributed clouds. This algorithm aims to partition each virtual network request ("VNR") to sub-graphs, using the Heavy Clique Matching technique ("HCM") to generate a coarsened graph. Each coarsened node in the coarsened graph is assigned to a suitable data center ("DC"). Inside each DC, a modified particle swarm optimization algorithm is initiated to find the near optimal solution for the VNE problem. The proposed algorithm was tested and evaluated against existing algorithms using extensive simulations, which shows that the proposed algorithm outperforms other algorithms.

Toward efficient dynamic virtual network embedding strategy for cloud networks

“…There are many studies to find out the efficient strategy to maximize the benefit for InPs including subgraph isomorphism, 12 path splitting and migration, 9 topology-aware node ranking 13 or other heuristic algorithms, and meta-heuristic algorithms. [14][15][16] Generally, the existing studies can be divided into two primary categories: online approaches and offline approaches.…”

PaCoVNE: Power Consumption Aware Coordinated VNE with Delay Constraints

“…The heuristic considers a set of embedded VNRs as input to perform an energy efficient relocation of resources, without impacting the acceptance ratio. [10] proposed to maximize the accepted VNRs while minimizing the energy cost of the whole system. They followed two observations, first embed VN nodes on SN nodes that has lowest electricity price, second embed VN nodes on an already active SN as much as possible, then put other nodes that has no load into sleeping mode.…”

Reliable design for virtual network requests with location constraints in edge-of-things computing

“…Network virtualization allows multiple heterogeneous virtual networks (VNs) to share the same physical network in edge-of-things computing [12][13][14][15][16]. Due to the increasing popularity of edge-ofthings computing, a great deal of research has been conducted on network virtualization and virtual network mapping technology [17][18][19][20][21][22][23][24].…”