In this paper, we propose an energy efficient virtual network embedding (EEVNE) approach for cloud computing networks, where power savings are introduced by consolidating resources in the network and data centers. We model our approach in an IP over WDM network using mixed integer linear programming (MILP). The performance of the EEVNE approach is compared with two approaches from the literature: the bandwidth cost approach (CostVNE) and the energy aware approach (VNE-EA). The CostVNE approach optimizes the use of available bandwidth, while the VNE-EA approach minimizes the power consumption by reducing the number of activated nodes and links without taking into account the granular power consumption of the data centers and the different network devices.
The results show that the EEVNE model achieves a maximum power saving of 60% (average 20%) compared to the CostVNE model under an energy inefficient data center power profile. We develop a heuristic, real-time energy optimized VNE (REOViNE), with power savings approaching those of the EEVNE model. We also compare the different approaches adopting energy efficient data center power profile. Furthermore, we study the impact of delay and node location constraints on the energy efficiency of virtual network embedding. We also show how VNE can impact the design of optimally located data centers for minimal power consumption in cloud networks. Finally, we examine the power savings and spectral efficiency benefits that VNE offers in optical orthogonal division multiplexing networks. Energy Efficient Virtual Network Embedding for Cloud Networks
Existing methods of disaster-resilient optical datacenter networks through integer linear programming (ILP) and heuristics addressed content placement, routing, and protection of network and content for geographically distributed cloud services delivered by optical networks models and heuristics are developed to minimize delay and power consumption of clouds over IP/WDM networks. The authors of exploited anycast routing by intelligently selecting destinations and routes for users traffic served by clouds over optical networks, as opposed to unicast traffic, while switching off unused network elements. A unified, online, and weighted routing and scheduling algorithm is presented in for a typical optical cloud infrastructure considering the energy consumption of the network and IT resources.
In the authors provided an optimization-based framework, where the objective functions range from minimizing the energy and bandwidth cost to minimizing the total carbon footprint subject to QoS constraints. Their model decides where to build a data center, how many servers are needed in each datacenter and how to route requests. In we built a MILP model to study the energy efficiency of public cloud for content delivery over non-bypass IP/WDM core networks. The model optimizes clouds external factors including the location of the cloud in the IP/WDM network and whether the cloud should be centralized or distributed and cloud internal capability factors including the number of servers, internal LAN switches, routers, and amount of storage required in each cloud.
We developed a MILP model which attempts to minimize the bandwidth cost of embedding a VNR. In the virtual network embedding energy aware (VNE-EA) model minimized the energy consumption by imposing the notion that the power consumption is minimized by switching off substrate links and nodes. The authors also assume that the power saved in switching off a substrate link is the same as the power saved by switching off a substrate node.
In the authors assumed that the power consumption in the network is insensitive to the number of ports used. They also seek to minimize the number of active working nodes and links. Botero and Hesselbach have proposed a model for energy efficiency using load balancing and have also developed a dynamic heuristic that reconfigured the embedding for energy efficiency once it is performed. They have implemented and evaluated their MILP models and heuristic algorithms using the ALEVIN Framework. The ALEVIN Framework is a good tool for developing, comparing and analyzing VNE algorithms.
The performance of the EEVNE approach is compared with two approaches from the literature: the bandwidth cost approach (CostVNE) and the energy aware approach (VNE-EA). The CostVNE approach optimizes the use of available bandwidth, while the VNE-EA approach minimizes the power consumption by reducing the number of activated nodes and links without taking into account the granular power consumption of the data centers and the different network devices.
The results show that the EEVNE model achieves a maximum power saving of 60% (average 20%) compared to the CostVNE model under energy inefficient data center power profile. We develop a heuristic, real-time energy optimized VNE (REOViNE), with power savings approaching those of the EEVNE model.