Cloud Virtual Network Embedding: Profit, Power and Acceptance

In this paper, we investigate maximizing the profit achieved by infrastructure providers (InPs) from embedding virtual network requests (VNRs) in IP/WDM core networks with clouds. We develop a mixed integer linear programming (MILP) model to study the impact of maximizing the profit on the power consumption and acceptance of VNRs. The results show that higher acceptance rates do not necessarily lead to higher profit due to the high cost associated with accepting some of the requests. The results also show that minimum power consumption can be achieved while maintaining the maximum profit

Energy Efficient Cloud Networks

Cloud computing is expected to be a major factor that will dominate the future Internet service model. This paper summarizes our work on energy efficiency for cloud networks. We develop a framework for studying the energy efficiency of four cloud services in IP over WDM networks: cloud content delivery, storage as a service (StaaS), and virtual machines (VMS) placement for processing applications and infrastructure as a service (IaaS).Our approach is based on the co-optimization of both external network related factors such as whether to geographically centralize or distribute the clouds, the influence of users’ demand distribution, content popularity, access frequency and renewable energy availability and internal capability factors such as the number of servers, switches and routers as well as the amount of storage demanded in each cloud. Our investigation of the different energy efficient approaches is backed with Mixed Integer Linear Programming (MILP) models and real time heuristic

Green virtual network embedding in optical OFDM cloud networks

Network virtualization has been identified as the mainstay of the current and future success of cloud computing networks. In this work, we study Virtual Network Embedding (VNE) over Optical Orthogonal Frequency Division Multiplexing (O-OFDM) networks as a means of allocating resources in a cloud computing network environment. We investigate two approaches to embed virtual networks in IP over O-OFDM networks: power minimized O-OFDM networks and spectrum minimized O-OFDM networks. The results show that the virtual network embedding in both power and spectrum minimized IP over O-OFDM networks outperform VNE in a 100 Gb/s IP over WDM network with average power savings of 63% and 17%, respectively

Energy Efficiency Measures for Future Core Networks

We summarize the various techniques developed by the GreenTouch consortium over the past 5 years to minimize core network power consumption. Adopting GreenTouch techniques can potentially improve the energy efficiency by 316x in a 2020 reference network compared to the state of the art in 2010

Energy Efficient Virtual Network Embedding for Cloud Networks

Network virtualization is widely considered to be one of the main paradigms for the future Internet architecture as it provides a number of advantages including scalability, on demand allocation of network resources, and the promise of efficient use of network resources. 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 an 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

Virtual Network Embedding Employing Renewable Energy Sources

Environmental sustainability in high capacity networks and cloud data centers has become one of the hottest research subjects. In this paper, we investigate the effective use of renewable energy and hence resource allocation in core networks with clouds as a means of reducing the carbon footprint. We develop a Green Virtual Network Embedding (GVNE) framework for minimizing the use of non-renewable energy through intelligent provisioning of bandwidth and cloud data center resources. The problem is modeled as a mixed integer linear program (MILP). The results show that it is better to instantiate virtual machines in cloud data centers that have access to abundant renewable energy even at the expense of traversing several links across the network. The GVNE model reduces the overall CO2 emissions by up to 32% for the network considering solar power availability and data center locations

Energy Efficient Network Function Virtualization in 5G Networks

A Number of merits could be brought by network function virtualization (NFV) such as scalability, on demand allocation of resources, and the efficient utilization of network resources. In this paper, we introduce a framework for designing an energy efficient architecture for 5G mobile network function virtualization. In the proposed architecture, the main functionalities of the mobile core network which include the packet gateway (P-GW), serving gateway (S-GW), mobility management entity (MME), policy control and charging role function, and the home subscriber server (HSS) functions are virtualized and provisioned on demand. We also virtualize the functions of the base band unit (BBU) of the evolved node B (eNB) and offload them from the mobile radio side. We leverage the capabilities of gigabit passive optical networks (GPON) as the radio access technology to connect the remote radio head (RRH) to new virtualized BBUs. We consider the IP/WDM backbone network and the GPON based access network as the hosts of virtual machines (VMs) where network functions will be implemented. Two cases were investigated; in the first case, we considered virtualization in the IP/WDM network only (since the core network is typically the location that supports virtualization) and in the second case we considered virtualization in both the IP/WDM and GPON access network. Our results indicate that we can achieve energy savings of 22% on average with virtualization in both the IP/WDM network and GPON access network compared to the case where virtualization is only done in the IP/WDM network

An Efficient Communication Aware Heuristic for Multiple Cloud Application Placement

International audienceTo deploy a distributed application on the cloud, cost, resource and communication constraints have to be considered to select the most suitable Virtual Machines (VMs), from private and public cloud providers. This process becomes very complex in large scale scenarios and, as this problem is NP-Hard, its automation must take scalability into consideration. In this work, we propose a heuristic able to calculate initial placements for distributed component-based applications on possibly multiple clouds with the objective of minimizing VM renting costs while satisfying applications' resource and communication constraints. We evaluate the heuristic performance and determine its limitations by comparing it to other placement approaches, namely exact algorithms and meta-heuristics. We show that the proposed heuristic is able to compute a good solution much faster than them

GreenTouch GreenMeter Core Network Energy-Efficiency Improvement Measures and Optimization

In this paper, we discuss energy-efficiency improvements in core networks obtained as a result of work carried out by the GreenTouch consortium over a five-year period. A number of techniques that yield substantial energy savings in core networks were introduced, including (i) the use of improved network components with lower power consumption, (ii) putting idle components into sleep mode, (iii) optically bypassing intermediate routers, (iv) the use of mixed line rates, (v) placing resources for protection into a low power state when idle, (vi) optimization of the network physical topology, and (vii) the optimization of distributed clouds for content distribution and network equipment virtualization. These techniques are recommended as the main energy-efficiency improvement measures for 2020 core networks. A mixed integer linear programming optimization model combining all the aforementioned techniques was built to minimize energy consumption in the core network. We consider group 1 nations’ traffic and place this traffic on a US continental network represented by the AT&T network topology. The projections of the 2020 equipment power consumption are based on two scenarios: a business as usual (BAU) scenario and a GreenTouch (GT) (i.e., BAU GT) scenario. The results show that the 2020 BAU scenario improves the network energy efficiency by a factor of 4.23× compared with the 2010 network as a result of the reduction in the network equipment power consumption. Considering the 2020 BAU + GT network, the network equipment improvements alone reduce network power by a factor of 20× compared with the 2010 network. Including of all the BAU+GT energy-efficiency techniques yields a total energy efficiency improvement of 315× . We have also implemented an experimental demonstration that illustrates the feasibility of energy-efficient content distribution in IP/WDM networks