149 research outputs found

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

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    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

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

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    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

    Server Resource Dimensioning and Routing of Service Function Chain in NFV Network Architectures

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    Effectiveness of segment routing technology in reducing the bandwidth and cloud resources provisioning times in network function virtualization architectures

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    Network Function Virtualization is a new technology allowing for a elastic cloud and bandwidth resource allocation. The technology requires an orchestrator whose role is the service and resource orchestration. It receives service requests, each one characterized by a Service Function Chain, which is a set of service functions to be executed according to a given order. It implements an algorithm for deciding where both to allocate the cloud and bandwidth resources and to route the SFCs. In a traditional orchestration algorithm, the orchestrator has a detailed knowledge of the cloud and network infrastructures and that can lead to high computational complexity of the SFC Routing and Cloud and Bandwidth resource Allocation (SRCBA) algorithm. In this paper, we propose and evaluate the effectiveness of a scalable orchestration architecture inherited by the one proposed within the European Telecommunications Standards Institute (ETSI) and based on the functional separation of an NFV orchestrator in Resource Orchestrator (RO) and Network Service Orchestrator (NSO). Each cloud domain is equipped with an RO whose task is to provide a simple and abstract representation of the cloud infrastructure. These representations are notified of the NSO that can apply a simplified and less complex SRCBA algorithm. In addition, we show how the segment routing technology can help to simplify the SFC routing by means of an effective addressing of the service functions. The scalable orchestration solution has been investigated and compared to the one of a traditional orchestrator in some network scenarios and varying the number of cloud domains. We have verified that the execution time of the SRCBA algorithm can be drastically reduced without degrading the performance in terms of cloud and bandwidth resource costs

    IT and Multi-layer Online Resource Allocation and Offline Planning in Metropolitan Networks

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    Metropolitan networks are undergoing a major technological breakthrough leveraging the capabilities of software-defined networking (SDN) and network function virtualization (NFV). NFV permits the deployment of virtualized network functions (VNFs) on commodity hardware appliances which can be combined with SDN flexibility and programmability of the network infrastructure. SDN/NFV-enabled networks require decision-making in two time scales: short-term online resource allocation and mid-to-long term offline planning. In this paper, we first tackle the dimensioning of SDN/NFV-enabled metropolitan networks paying special attention to the role that latency plays in the capacity planning. We focus on a specific use-case: the metropolitan network that covers the Murcia - Alicante Spanish regions. Then, we propose a latency-aware multilayer service-chain allocation (LA-ML-SCA) algorithm to explore a range of maximum latency requirements and their impact on the resources for dimensioning the metropolitan network. We observe that design costs increase for low latency requirements as more data center facilities need to be spread to get closer to the network edge, reducing the economies of scale on the IT infrastructure. Subsequently, we review our recent joint computation of multi-site VNF placement and multilayer resource allocation in the deployment of a network service in a metro network. Specifically, a set of subroutines contained in LA-ML-SCA are experimentally validated in a network optimization-as-a-service architecture that assists an Open-Source MANO instance, virtual infrastructure managers and WAN controllers in a metro network test-bed.Grant numbers : Go2Edge - Engineering Future Edge Computing Networks, Systems and Services.@ 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

    Network slicing cost allocation model

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    Within the upcoming fifth generation (5G) mobile networks, a lot of emerging technologies, such as Software Defined Network (SDN), Network Function Virtualization (NFV) and network slicing are proposed in order to leverage more flexibility, agility and cost-efficient deployment. These new networking paradigms are shaping not only the network architectures but will also affect the market structure and business case of the stakeholders involved. Due to its capability of splitting the physical network infrastructure into several isolated logical sub-networks, network slicing opens the network resources to vertical segments aiming at providing customized and more efficient end-to-end (E2E) services. While many standardization efforts within the 3GPP body have been made regarding the system architectural and functional features for the implementation of network slicing in 5G networks, techno-economic analysis of this concept is still at a very incipient stage. This paper initiates this techno-economic work by proposing a model that allocates the network cost to the different deployed slices, which can then later be used to price the different E2E services. This allocation is made from a network infrastructure provider perspective. To feed the proposed model with the required inputs, a resource allocation algorithm together with a 5G network function (NF) dimensioning model are also proposed. Results of the different models as well as the cost saving on the core network part resulting from the use of NFV are discussed as well

    Techno-economic analysis of software-defined telecommunications networks

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    Performance Modeling of Softwarized Network Services Based on Queuing Theory with Experimental Validation

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    Network Functions Virtualization facilitates the automation of the scaling of softwarized network services (SNSs). However, the realization of such a scenario requires a way to determine the needed amount of resources so that the SNSs performance requisites are met for a given workload. This problem is known as resource dimensioning, and it can be efficiently tackled by performance modeling. In this vein, this paper describes an analytical model based on an open queuing network of G/G/m queues to evaluate the response time of SNSs. We validate our model experimentally for a virtualized Mobility Management Entity (vMME) with a three-tiered architecture running on a testbed that resembles a typical data center virtualization environment. We detail the description of our experimental setup and procedures. We solve our resulting queueing network by using the Queueing Networks Analyzer (QNA), Jackson’s networks, and Mean Value Analysis methodologies, and compare them in terms of estimation error. Results show that, for medium and high workloads, the QNA method achieves less than half of error compared to the standard techniques. For low workloads, the three methods produce an error lower than 10%. Finally, we show the usefulness of the model for performing the dynamic provisioning of the vMME experimentally.This work has been partially funded by the H2020 research and innovation project 5G-CLARITY (Grant No. 871428)National research project 5G-City: TEC2016-76795-C6-4-RSpanish Ministry of Education, Culture and Sport (FPU Grant 13/04833). We would also like to thank the reviewers for their valuable feedback to enhance the quality and contribution of this wor

    A Survey of Deep Learning for Data Caching in Edge Network

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    The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network as well as reducing latency to access popular content. In that respect end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e, at close proximity to the users. In addition to model based caching schemes learning-based edge caching optimizations has recently attracted significant attention and the aim hereafter is to capture these recent advances for both model based and data driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, a number of key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for cachin

    Generation of Network Service Descriptors from Network Service Requirements

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    Network Function Virtualization (NFV) is a new paradigm in Network Service (NS) provisioning. European Telecommunications Standards Institute (ETSI) proposed and standardized an architectural framework for NFV. By leveraging virtualization and Software-Defined Networking (SDN) technologies, NFV decouples network functionality from hardware infrastructure. This enables the automated provisioning of NSs and reduces the capital and operational costs for service operators. NFV Management and Orchestration (NFV-MANO) is a functional block in the NFV framework, and it is responsible for the deployment and life-cycle management of NSs. With NFV, the telecommunication industry is moving towards zero-touch, i.e. automation of all the processes. In order to orchestrate and manage an NS, NFV-MANO requires the NS’s deployment template. This template is referred to as NS Descriptor (NSD) and contains all the details for deployment and orchestration of the NS. De-signing such a descriptor requires the design of the NS, which is actually out of the NFV scope. Traditionally, service operators’ experts design NSs and NSDs. However, this design activity is time-consuming and error-prone; moreover, it is not fitting the Telecom’s vision of zero-touch. In this thesis, we will propose an approach to automate the process of NS and NSD design. The approach starts from a set of requirements provided as Network Service Requirements (NSReq). The NSReq describes the required network service at a high level of abstraction and focuses on the functional, architectural, and non-functional characteristics. With the help of an ontology representing the knowledge from Telecom standards and previous successful experiences, we decompose the NSReq. We select the set of Virtual Network Functions (VNF) from a catalog to design the NS. Considering all the levels of decomposition and the VNF’s dependencies captured from the ontology, we design all the possible for-warding graphs that can form an NS. We design each forwarding graph through different steps at different abstraction levels, i.e. functional, architectural, and VNF levels. According to each forwarding graph, we design an NSD along with the traffic flows in the NS. We re-fine each NSD by dimensioning its VNFs using the non-functional requirements in the NSReq. Accordingly, we refine the deployment flavor of each NSD. We have developed a prototype tool as a proof of concept for our proposed approach which we will discuss later in this thesis
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