VNF-AAPC : accelerator-aware VNF placement and chaining

In recent years, telecom operators have been migrating towards network architectures based on Network Function Virtualization in order to reduce their high Capital Expenditure (CAPEX) and Operational Expenditure (OPEX). However, virtualization of some network functions is accompanied by a significant degradation of Virtual Network Function (VNF) performance in terms of their throughput or energy consumption. To address these challenges, use of hardware-accelerators, e.g. FPGAs, GPUs, to offload CPU-intensive operations from performance-critical VNFs has been proposed. Allocation of NFV infrastructure (NFVi) resources for VNF placement and chaining (VNF-PC) has been a major area of research recently. A variety of resources allocation models have been proposed to achieve various operator's objectives i.e. minimizing CAPEX, OPEX, latency, etc. However, the VNF-PC resource allocation problem for the case when NFVi incorporates hardware-accelerators remains unaddressed. Ignoring hardware-accelerators in NFVi while performing resource allocation for VNF-chains can nullify the advantages resulting from the use of hardware-accelerators. Therefore, accurate models and techniques for the accelerator-aware VNF-PC (VNF-AAPC) are needed in order to achieve the overall efficient utilization of all NFVi resources including hardware-accelerators. This paper investigates the problem of VNF-AAPC, i.e., how to allocate usual NFVi resources along-with hardware-accelerators to VNF-chains in a cost-efficient manner. Particularly, we propose two methods to tackle the VNF-AAPC problem. The first approach is based on Integer Linear Programming (ILP) which jointly optimizes VNF placement, chaining and accelerator allocation while concurring to all NFVi constraints. The second approach is a heuristic-based method that addresses the scalability issue of the ILP approach. The heuristic addresses the VNF-AAPC problem by following a two-step algorithm. The experimental evaluations indicate that incorporating accelerator-awareness in VNF-PC strategies can help operators to achieve additional cost-savings from the efficient allocation of hardware-accelerator resources

Algorithms for advance bandwidth reservation in media production networks

Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results

Server resource dimensioning and routing of service function chain in NFV network architectures

The Network Function Virtualization (NFV) technology aims at virtualizing the network service with the execution of the single service components in Virtual Machines activated on Commercial-off-the-shelf (COTS) servers. Any service is represented by the 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 (VNFI) that in general are software components executed on Virtual Machines. In this paper we cope with the routing and resource dimensioning problem in NFV architectures. We formulate the optimization problem and due to its NP-hard complexity, heuristics are proposed for both cases of offline and online traffic demand. We show how the heuristics works correctly by guaranteeing a uniform occupancy of the server processing capacity and the network link bandwidth. A consolidation algorithm for the power consumption minimization is also proposed. The application of the consolidation algorithm allows for a high power consumption saving that however is to be paid with an increase in SFC blocking probability

Distributed Processing in FPGA Accelerated Cloud

Motivated by the need of cost reduction, better energy efficiency and agile update and deployment of new services, telecommunication industry is moving towards virtualization, which lead to Network Function Virtualization (NFV) standard. NFV leverages cloud technologies to deploy network functions that are traditionally implemented using dedicated proprietary hardware. Still, the performance provided by current cloud infrastructure does not fulfill the requirements for demanding NFV's use cases. Thus, hardware acceleration should be deployed. The hardware programmability of FPGAs allows them to adapt well to many type of workloads, placing them as good candidates to be used as hardware accelerators in virtualized environments. In this thesis, the CRUN framework is proposed to provide FPGA as hardware accelerator resources in cloud, abstracting the integration complexity while enabling sharable and scalable use of such devices. CRUN architecture allow user's acceleration hardware to be accessed locally and through the datacenter's network. The latter provide flexible connectivity by following the Software-defined Networking (SDN) principles. The architecture enables the same sharable FPGA to be used simultaneously as a co-processor, a network accelerator or as a distributed accelerator in a scalable scenario over several FPGAs. In its current development state, CRUN was leveraged for inference of a machine learning application composed of a fully connected neural network. The main performance target was to achieve ultra-low latency, less than 40μs, for each inference at software level. Only CRUN fulfilled the requirement among the analyzed alternatives, where the architecture is capable of providing latency in the 30μs range in average. For context, high-end General-Purpose Processor (GPP) and Graphics Processing Unit (GPU) provided latency values of 798μs and 1 897μs respectively for the same application

Infrastructure sharing of 5G mobile core networks on an SDN/NFV platform

When looking towards the deployment of 5G network architectures, mobile network operators will continue to face many challenges. The number of customers is approaching maximum market penetration, the number of devices per customer is increasing, and the number of non-human operated devices estimated to approach towards the tens of billions, network operators have a formidable task ahead of them. The proliferation of cloud computing techniques has created a multitude of applications for network services deployments, and at the forefront is the adoption of Software-Defined Networking (SDN) and Network Functions Virtualisation (NFV). Mobile network operators (MNO) have the opportunity to leverage these technologies so that they can enable the delivery of traditional networking functionality in cloud environments. The benefit of this is reductions seen in the capital and operational expenditures of network infrastructure. When going for NFV, how a Virtualised Network Function (VNF) is designed, implemented, and placed over physical infrastructure can play a vital role on the performance metrics achieved by the network function. Not paying careful attention to this aspect could lead to the drastically reduced performance of network functions thus defeating the purpose of going for virtualisation solutions. The success of mobile network operators in the 5G arena will depend heavily on their ability to shift from their old operational models and embrace new technologies, design principles and innovation in both the business and technical aspects of the environment. The primary goal of this thesis is to design, implement and evaluate the viability of data centre and cloud network infrastructure sharing use case. More specifically, the core question addressed by this thesis is how virtualisation of network functions in a shared infrastructure environment can be achieved without adverse performance degradation. 5G should be operational with high penetration beyond the year 2020 with data traffic rates increasing exponentially and the number of connected devices expected to surpass tens of billions. Requirements for 5G mobile networks include higher flexibility, scalability, cost effectiveness and energy efficiency. Towards these goals, Software Defined Networking (SDN) and Network Functions Virtualisation have been adopted in recent proposals for future mobile networks architectures because they are considered critical technologies for 5G. A Shared Infrastructure Management Framework was designed and implemented for this purpose. This framework was further enhanced for performance optimisation of network functions and underlying physical infrastructure. The objective achieved was the identification of requirements for the design and development of an experimental testbed for future 5G mobile networks. This testbed deploys high performance virtualised network functions (VNFs) while catering for the infrastructure sharing use case of multiple network operators. The management and orchestration of the VNFs allow for automation, scalability, fault recovery, and security to be evaluated. The testbed developed is readily re-creatable and based on open-source software

View on 5G Architecture: Version 1.0

The current white paper focuses on the produced results after one year research mainly from 16 projects working on the abovementioned domains. During several months, representatives from these projects have worked together to identify the key findings of their projects and capture the commonalities and also the different approaches and trends. Also they have worked to determine the challenges that remain to be overcome so as to meet the 5G requirements. The goal of 5G Architecture Working Group is to use the results captured in this white paper to assist the participating projects achieve a common reference framework. The work of this working group will continue during the following year so as to capture the latest results to be produced by the projects and further elaborate this reference framework. The 5G networks will be built around people and things and will natively meet the requirements of three groups of use cases: • Massive broadband (xMBB) that delivers gigabytes of bandwidth on demand • Massive machine-type communication (mMTC) that connects billions of sensors and machines • Critical machine-type communication (uMTC) that allows immediate feedback with high reliability and enables for example remote control over robots and autonomous driving. The demand for mobile broadband will continue to increase in the next years, largely driven by the need to deliver ultra-high definition video. However, 5G networks will also be the platform enabling growth in many industries, ranging from the IT industry to the automotive, manufacturing industries entertainment, etc. 5G will enable new applications like for example autonomous driving, remote control of robots and tactile applications, but these also bring a lot of challenges to the network. Some of these are related to provide low latency in the order of few milliseconds and high reliability compared to fixed lines. But the biggest challenge for 5G networks will be that the services to cater for a diverse set of services and their requirements. To achieve this, the goal for 5G networks will be to improve the flexibility in the architecture. The white paper is organized as follows. In section 2 we discuss the key business and technical requirements that drive the evolution of 4G networks into the 5G. In section 3 we provide the key points of the overall 5G architecture where as in section 4 we elaborate on the functional architecture. Different issues related to the physical deployment in the access, metro and core networks of the 5G network are discussed in section 5 while in section 6 we present software network enablers that are expected to play a significant role in the future networks. Section 7 presents potential impacts on standardization and section 8 concludes the white paper

Distribution of Low Latency Machine Learning Algorithm

Mobile networks are evolving towards centralization and cloudification while bringing computing power to the edge, opening its scope to a new range of applications. Ultra-low latency is one of the requirements of such applications in the next generation of mobile networks (5G), where deep learning is expected to play a big role. Hence, to enable the usage of deep learning solutions on the edge cloud, ultra-low latency inference must be investigated. The study presented here relies on the usage of an in-house framework (CRUN) that enables the distribution of acceleration on data center environment. The objective of this thesis is to leverage the best solution for the inference of a machine learning algorithm for an anomaly detection application using neural networks in the edge cloud context. To evaluate the obtained results with CRUN a comparison work is also carried out. Five inference solutions were compared using CPU, GPU and FPGA. The results show a superior performance in terms of latency for all CRUN experiments, that basically comprehends three cases. The first one utilizing the RTL anomaly detection neural network as a baseline solution, the second using the same baseline code but unrolling the biggest layer for obtaining reduced latency and the third by distributing the neural network in two FPGAs. The requirements for this solution were to obtain latency between 20 μs to 40 μs for inference time and at least 20000 inferences per second. These goals were categorically fulfilled for all CRUN experiments, providing 30 μs latency in average, while the second best solution provided 272 μs