Migration energy aware reconfigurations of virtual network function instances in NFV architectures

Network function virtualization (NFV) is a new network architecture framework that implements network functions in software running on a pool of shared commodity servers. NFV can provide the infrastructure flexibility and agility needed to successfully compete in today's evolving communications landscape. Any service is represented by a service function chain (SFC) that is a set of VNFs to be executed according to a given order. The running of VNFs needs the instantiation of VNF instances (VNFIs) that are software modules executed on virtual machines. This paper deals with the migration problem of the VNFIs needed in the low traffic periods to turn OFF servers and consequently to save energy consumption. Though the consolidation allows for energy saving, it has also negative effects as the quality of service degradation or the energy consumption needed for moving the memories associated to the VNFI to be migrated. We focus on cold migration in which virtual machines are redundant and suspended before performing migration. We propose a migration policy that determines when and where to migrate VNFI in response to changes to SFC request intensity. The objective is to minimize the total energy consumption given by the sum of the consolidation and migration energies. We formulate the energy aware VNFI migration problem and after proving that it is NP-hard, we propose a heuristic based on the Viterbi algorithm able to determine the migration policy with low computational complexity. The results obtained by the proposed heuristic show how the introduced policy allows for a reduction of the migration energy and consequently lower total energy consumption with respect to the traditional policies. The energy saving can be on the order of 40% with respect to a policy in which migration is not performed

Optical communications and networking solutions for the support of C-RAN in a 5G environment

The widespread availability of mobile devices such as tablets and smartphones has led to fast-increasing mobile data traffic in the last few years [...

Optimizing the Cloud Resources, Bandwidth and Deployment Costs in Multi-Providers Network Function Virtualization Environment

The introduction of network function virtualization (NFV) leads to a new business model in which the Telecommunication Service Provider needs to rent cloud resources to infrastructure provider (InP) at prices as low as possible. Lowest prices can be achieved if the cloud resources can be rented in advance by allocating long-term virtual machines (VM). This is in contrast with the short-term VMs that are rented on demand and have higher costs. For this reason, we propose a proactive solution in which the cloud resource rent is planned in advance based on peak traffic knowledge. We illustrate the problem of determining the cloud resources in cloud infrastructures managed by different InPs and so as to minimize the sum of cloud resource, bandwidth and deployment costs. We formulate an integer linear problem (ILP) and due to its complexity, we introduce an efficient heuristic approach allowing for a remarkable computational complexity reduction. We compare our solution to a reactive solution in which the cloud resources are rented on demand and dimensioned according to the current traffic. Though the proposed proactive solution needs more cloud and bandwidth resources due to its peak allocation, its total resources cost may be lower than the one achieved when a reactive solution is applied. That is a consequence of the higher cost of short-term VMs. For instance, when a reactive solution is applied with traffic variation times of ten minutes, our proactive solution allows for lower total costs when the long-term VM rent is lower than the short-term VM one by 33%

Trade-off between power and bandwidth consumption in a reconfigurable xhaul network architecture

The increasing number of wireless devices, the high required traffic bandwidth, and power consumption will lead to a revolution of mobile access networks, which is not a simple evolution of traditional ones. Cloud radio access network technologies are seen as promising solution in order to deal with the heavy requirements defined for 5G mobile networks. The introduction of the common public radio interface (CPRI) technology allows for a centralization in BaseBand unit (BBU) of some access functions with advantages in terms of power consumption saving when switching off algorithms are implemented. Unfortunately, the advantages of the CPRI technology are to be paid with an increase in required bandwidth to carry the traffic between the BBU and the radio remote unit (RRU), in which only the radio functions are implemented. For this reason, a tradeoff solution between power and bandwidth consumption is proposed and evaluated. The proposed solution consists of: 1) handling the traffic generated by the users through both RRU and traditional radio base stations (RBS) and 2) carrying the traffic generated by the RRU and RBS (CPRI and Ethernet flows) with a reconfigurable network. The proposed solution is investigated under the lognormal spatial traffic distribution assumption. After proposing resource dimensioning analytical models validated by simulation, we show how the sum of the bandwidth and power consumption may be minimized with the deployment of a given percentage of RRU. For instance we show how in 5G traffic scenarios this percentage can vary from 30% to 50% according to total traffic amount handled by a switching node of the reconfigurable network

OTN/WDM Technology Application for Implementing Xhaul Architecture in C-RAN Environment

Very often reading and talking about 5G or the next generation network, we have an idea as huge as confused. We could find in many papers in literacy a lots of several issues and several facets that describe the new access network generation; from these ones, for example, we can mention an expected number of connected devices and forecast traffic that are more than 1000-fold actual values. This increasing number of wireless devices, the increasing required traffic bandwidth and the high power consumption lead to a revolution of mobile access networks, that will be not a simple evolution of traditional ones. Another point of difference with the actual network will be the presence of different classes and qualities of service necessary for providing different service types. As consequences of the intense research studies done in these years, a large number of emerging technologies that could achieve the above requirements are developed. One of these technologies is Cloud Radio Access Network (C-RAN), that is seen as promising solution in order to deal with the heavy requirements of bandwidth capacity and energy efficiency, defined for 5G mobile networks. The introduction of the Common Public Radio Interface (CPRI) technology allows for a centralization in Base Bandwidth Unit (BBU) of some access functions, with advantages in terms of power consumption saving when switching off algorithms are implemented. Unfortunately, the advantages of the CPRI technology is to be paid with an increase in bandwidth to be carried between the BBU and the Radio Remote Unit (RRU), in which only the radio functions are implemented. However, one of the most import factors, that could sign a great boost in the new generation network, will be how these technologies could work together in order to improve the whole network performance and, consequently, the possibility to have a ``network slicing", in which different service types could be managed by a carrier client. Thus, it is simple to think that one of the most important key drivers in 5G networks has to be the flexibility in managing of different radio access technologies. The OTN/WDM technology application could be important to achieve a great level of flexibility and to save resources in the 5G access network, where there will be a dense deployment of network elements like Base Station or WiFi access points. In this scenario, a trade-off solution between power and bandwidth consumption may be needed. In this thesis, it is proposed and evaluated a network solution that consists in handling and carrying the traffic generated by the RRUs and RBSs (CPRI and Ethernet flows) with a reconfigurable network based on Optical Transport Network (OTN) technology. After proposing some energy and cost-efficient OTN/WDM switch architectures and resource dimensioning analytical models, it is shown how the sum of the bandwidth and power consumption may be minimized with the deployment of a given percentage of RRU out of the total number of radio elements. It is important to note that the achievement of this results is only possible if the network has the capacity to efficiently manage a large number of base stations and, thus, it is able to exploit the gain related to the statistical multiplexing effects

Proposal and Investigation of a Convolutional and LSTM Neural Network for the Cost-Aware Resource Prediction in Softwarized Networks

Resource prediction algorithms have been recently proposed in Network Function Virtualization architectures. A prediction-based resource allocation is characterized by higher operation costs due to: (i) Resource underestimate that leads to quality of service degradation; (ii) used cloud resource over allocation when a resource overestimate occurs. To reduce such a cost, we propose a cost-aware prediction algorithm able to minimize the sum of the two cost components. The proposed prediction solution is based on a convolutional and Long Short Term Memory neural network to handle the spatial and temporal correlations of the need processing capacities. We compare in a real network and traffic scenario the proposed technique to a traditional one in which the aim is to exactly predict the needed processing capacity. We show how the proposed solution allows for cost advantages in the order of 20%

Proposal and Investigation of a Lite Time Sensitive Networking Solution for the Support of Real Time Services in Space Launcher Networks

Most launcher networks are based on proprietary buses such as MIL-STD-1553B whose low bandwidth limits the introduction of new services of suitable characteristics. Ethernet technology, because of its low cost and high performance, has been considered an excellent candidate for its use in launcher networks. The real time Ethernet solutions based on the Time Sensitive Networking (TSN) standards seem the most suitable because of its multi-vendor product characteristics. In this paper we propose a real time Ethernet solution for aerospace applications in which negligible jitter services has to be guaranteed. The proposed solution is based on the following TSN standards: IEEE 802.1AS/ASrev as synchronization protocol and 802.1Qbv-2015 for deterministic traffic scheduling. To improve both the bandwidth effective and the frame delay the solution is also based on a change in the management of the Priority Code Point field in IEEE 802.1Q standard. The optimal scheduling problem is formulated so as to minimize the makespan, defined as the time needed to deliver all of the messages of an elementary cycle. The problem has been resolved with the CPLEX solver and the proposed solution has been evaluated in terms of both delay and bandwidth effective by comparing its performance with the TTEthernet, FTTEthernet benchmark solutions. The obtained results in a real traffic scenario characterized by the set of messages of the VEGA launcher show how the proposed solution allows for the same performance of TTEthernet, i.e., the solution of proprietary and real-time Ethernet with better performance

Proposal and Investigation of a Convolutional and LSTM Neural Network for the Cost-Aware Resource Prediction in Softwarized Networks

Resource prediction algorithms have been recently proposed in Network Function Virtualization architectures. A prediction-based resource allocation is characterized by higher operation costs due to: (i) Resource underestimate that leads to quality of service degradation; (ii) used cloud resource over allocation when a resource overestimate occurs. To reduce such a cost, we propose a cost-aware prediction algorithm able to minimize the sum of the two cost components. The proposed prediction solution is based on a convolutional and Long Short Term Memory neural network to handle the spatial and temporal correlations of the need processing capacities. We compare in a real network and traffic scenario the proposed technique to a traditional one in which the aim is to exactly predict the needed processing capacity. We show how the proposed solution allows for cost advantages in the order of 20%

An approach for service function chain routing and virtual function network instance migration in network function virtualization architectures

Network function virtualization foresees the virtualization of service functions and their execution on virtual machines. Any service is represented by a service function chain (SFC) that is a set of VNFs to be executed according to a given order. The running of VNFs needs the instantiation of VNF Instances (VNFIs) that in general are software modules executed on virtual machines. The virtualization challenges include: 1) where to instantiate VNFIs; ii) how many resources to allocate to each VNFI; iii) how to route SFC requests to the appropriate VNFIs in the right sequence; and iv) when and how to migrate VNFIs in response to changes to SFC request intensity and location. We develop an approach that uses three algorithms that are used back-to-back resulting in VNFI placement, SFC routing, and VNFI migration in response to changing workload. The objective is to first minimize the rejection of SFC bandwidth and second to consolidate VNFIs in as few servers as possible so as to reduce the energy consumed. The proposed consolidation algorithm is based on a migration policy of VNFIs that considers the revenue loss due to QoS degradation that a user suffers due to information loss occurring during the migrations. The objective is to minimize the total cost given by the energy consumption and the revenue loss due to QoS degradation. We evaluate our suite of algorithms on a test network and show performance gains that can be achieved over using other alternative naive algorithms