1,187 research outputs found
Learning Augmented Optimization for Network Softwarization in 5G
The rapid uptake of mobile devices and applications are posing unprecedented traffic burdens on the existing networking infrastructures. In order to maximize both user experience and investment return, the networking and communications systems are evolving to the next gen- eration – 5G, which is expected to support more flexibility, agility, and intelligence towards provisioned services and infrastructure management. Fulfilling these tasks is challenging, as nowadays networks are increasingly heterogeneous, dynamic and expanded with large sizes. Network softwarization is one of the critical enabling technologies to implement these requirements in 5G. In addition to these problems investigated in preliminary researches about this technology, many new emerging application requirements and advanced opti- mization & learning technologies are introducing more challenges & opportunities for its fully application in practical production environment. This motivates this thesis to develop a new learning augmented optimization technology, which merges both the advanced opti- mization and learning techniques to meet the distinct characteristics of the new application environment. To be more specific, the abstracts of the key contents in this thesis are listed as follows: • We first develop a stochastic solution to augment the optimization of the Network Function Virtualization (NFV) services in dynamical networks. In contrast to the dominant NFV solutions applied for the deterministic networking environments, the inherent network dynamics and uncertainties from 5G infrastructure are impeding the rollout of NFV in many emerging networking applications. Therefore, Chapter 3 investigates the issues of network utility degradation when implementing NFV in dynamical networks, and proposes a robust NFV solution with full respect to the underlying stochastic features. By exploiting the hierarchical decision structures in this problem, a distributed computing framework with two-level decomposition is designed to facilitate a distributed implementation of the proposed model in large-scale networks. • Next, Chapter 4 aims to intertwin the traditional optimization and learning technologies. In order to reap the merits of both optimization and learning technologies but avoid their limitations, promissing integrative approaches are investigated to combine the traditional optimization theories with advanced learning methods. Subsequently, an online optimization process is designed to learn the system dynamics for the network slicing problem, another critical challenge for network softwarization. Specifically, we first present a two-stage slicing optimization model with time-averaged constraints and objective to safeguard the network slicing operations in time-varying networks. Directly solving an off-line solution to this problem is intractable since the future system realizations are unknown before decisions. To address this, we combine the historical learning and Lyapunov stability theories, and develop a learning augmented online optimization approach. This facilitates the system to learn a safe slicing solution from both historical records and real-time observations. We prove that the proposed solution is always feasible and nearly optimal, up to a constant additive factor. Finally, simulation experiments are also provided to demonstrate the considerable improvement of the proposals. • The success of traditional solutions to optimizing the stochastic systems often requires solving a base optimization program repeatedly until convergence. For each iteration, the base program exhibits the same model structure, but only differing in their input data. Such properties of the stochastic optimization systems encourage the work of Chapter 5, in which we apply the latest deep learning technologies to abstract the core structures of an optimization model and then use the learned deep learning model to directly generate the solutions to the equivalent optimization model. In this respect, an encoder-decoder based learning model is developed in Chapter 5 to improve the optimization of network slices. In order to facilitate the solving of the constrained combinatorial optimization program in a deep learning manner, we design a problem-specific decoding process by integrating program constraints and problem context information into the training process. The deep learning model, once trained, can be used to directly generate the solution to any specific problem instance. This avoids the extensive computation in traditional approaches, which re-solve the whole combinatorial optimization problem for every instance from the scratch. With the help of the REINFORCE gradient estimator, the obtained deep learning model in the experiments achieves significantly reduced computation time and optimality loss
Segment Routing: a Comprehensive Survey of Research Activities, Standardization Efforts and Implementation Results
Fixed and mobile telecom operators, enterprise network operators and cloud
providers strive to face the challenging demands coming from the evolution of
IP networks (e.g. huge bandwidth requirements, integration of billions of
devices and millions of services in the cloud). Proposed in the early 2010s,
Segment Routing (SR) architecture helps face these challenging demands, and it
is currently being adopted and deployed. SR architecture is based on the
concept of source routing and has interesting scalability properties, as it
dramatically reduces the amount of state information to be configured in the
core nodes to support complex services. SR architecture was first implemented
with the MPLS dataplane and then, quite recently, with the IPv6 dataplane
(SRv6). IPv6 SR architecture (SRv6) has been extended from the simple steering
of packets across nodes to a general network programming approach, making it
very suitable for use cases such as Service Function Chaining and Network
Function Virtualization. In this paper we present a tutorial and a
comprehensive survey on SR technology, analyzing standardization efforts,
patents, research activities and implementation results. We start with an
introduction on the motivations for Segment Routing and an overview of its
evolution and standardization. Then, we provide a tutorial on Segment Routing
technology, with a focus on the novel SRv6 solution. We discuss the
standardization efforts and the patents providing details on the most important
documents and mentioning other ongoing activities. We then thoroughly analyze
research activities according to a taxonomy. We have identified 8 main
categories during our analysis of the current state of play: Monitoring,
Traffic Engineering, Failure Recovery, Centrally Controlled Architectures, Path
Encoding, Network Programming, Performance Evaluation and Miscellaneous...Comment: SUBMITTED TO IEEE COMMUNICATIONS SURVEYS & TUTORIAL
Network Function Virtualization in Dynamic Networks: A Stochastic Perspective
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordAs a key enabling technology for 5G network
softwarization, Network Function Virtualization (NFV) provides
an efficient paradigm to optimize network resource utility for
the benefits of both network providers and users. However,
the inherent network dynamics and uncertainties from 5G
infrastructure, resources and applications are slowing down
the further adoption of NFV in many emerging networking
applications. Motivated by this, in this paper, we investigate
the issues of network utility degradation when implementing
NFV in dynamic networks, and design a proactive NFV solution
from a fully stochastic perspective. Unlike existing deterministic
NFV solutions, which assume given network capacities and/or
static service quality demands, this paper explicitly integrates
the knowledge of influential network variations into a twostage
stochastic resource utilization model. By exploiting the
hierarchical decision structures in this problem, a distributed
computing framework with two-level decomposition is designed
to facilitate a distributed implementation of the proposed model
in large-scale networks. The experimental results demonstrate
that the proposed solution not only improves 3∼5 folds of network
performance, but also effectively reduces the risk of service
quality violation.The work of Xiangle Cheng is partially supported by the
China Scholarship Council for the study at the University of
Exeter. This work is also partially supported by the UK EPSRC
project (Grant No.: EP/R030863/1)
VirtuWind: Virtual and programmable industrial network prototype deployed in operational wind park.
With anticipated exponential growth of connected devices, future industrial networks require an open solutions architecture facilitated by standards and a strong ecosystem. Such solutions should also deal with range of quality of service requirements imposed by industrial networks. Preserving strict quality of service is particularly challenging when services pass across domains of multiple provides. VirtuWind aims to develop and demonstrate a Software Defined Networking and Network Function Virtualization ecosystem, based on an open, modular and secure framework to address stringent requirements of the industrial networks. A prototype of the framework for intra-domain and inter-domain scenarios will be showcased in real Wind Parks, as a representative use case of industrial networks. This paper details this vision and explains steps forward
A Scalable Approach for Service Chain (SC) Mapping with Multiple SC Instances in a Wide-Area Network
Network Function Virtualization (NFV) aims to simplify deployment of network
services by running Virtual Network Functions (VNFs) on commercial
off-the-shelf servers. Service deployment involves placement of VNFs and
in-sequence routing of traffic flows through VNFs comprising a Service Chain
(SC). The joint VNF placement and traffic routing is called SC mapping. In a
Wide-Area Network (WAN), a situation may arise where several traffic flows,
generated by many distributed node pairs, require the same SC; then, a single
instance (or occurrence) of that SC might not be enough. SC mapping with
multiple SC instances for the same SC turns out to be a very complex problem,
since the sequential traversal of VNFs has to be maintained while accounting
for traffic flows in various directions. Our study is the first to deal with
the problem of SC mapping with multiple SC instances to minimize network
resource consumption. We first propose an Integer Linear Program (ILP) to solve
this problem. Since ILP does not scale to large networks, we develop a
column-generation-based ILP (CG-ILP) model. However, we find that exact
mathematical modeling of the problem results in quadratic constraints in our
CG-ILP. The quadratic constraints are made linear but even the scalability of
CG-ILP is limited. Hence, we also propose a two-phase column-generation-based
approach to get results over large network topologies within reasonable
computational times. Using such an approach, we observe that an appropriate
choice of only a small set of SC instances can lead to a solution very close to
the minimum bandwidth consumption. Further, this approach also helps us to
analyze the effects of number of VNF replicas and number of NFV nodes on
bandwidth consumption when deploying these minimum number of SC instances.Comment: arXiv admin note: substantial text overlap with arXiv:1704.0671
Intent-based zero-touch service chaining layer for software-defined edge cloud networks
Edge Computing, along with Software Defined Networking and Network Function Virtualization, are causing network infrastructures to become as distributed clouds extended to the edge with services provided as dynamically established sequences of virtualized functions (i.e., dynamic service chains) thereby elastically addressing different processing requirements of application data flows. However, service operators and application developers are not inclined to deal with descriptive configuration directives to establish and operate services, especially in case of service chains. Intent-based Networking is emerging as a novel approach that simplifies network management and automates the implementation of network operations required by applications. This paper presents an intent-based zero-touch service chaining layer that provides the programmable provision of service chain paths in edge cloud networks. In addition to the dynamic and elastic deployment of data delivery services, the intent-based layer offers an automated adaptation of the service chains paths according to the application's goals expressed in the intent to recover from sudden congestion events in the SDN network. Experiments have been carried out in an emulated network environment to show the feasibility of the approach and to evaluate the performance of the intent layer in terms of network resource usage and adaptation overhead
Stochastic performance analysis of Network Function Virtualisation in future internet
This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordIEEE Network Function Virtualisation (NFV) has been considered as a promising technology for future Internet to increase network flexibility, accelerate service innovation and reduce the Capital Expenditures (CAPEX) and Operational Expenditures (OPEX) costs, through migrating network functions from dedicated network devices to commodity hardware. Recent studies reveal that although this migration of network function brings the network operation unprecedented flexibility and controllability, NFV-based architecture suffers from serious performance degradation compared with traditional service provisioning on dedicated devices. In order to achieve a comprehensive understanding of the service provisioning capability of NFV, this paper proposes a novel analytical model based on Stochastic Network Calculus (SNC) to quantitatively investigate the end-to-end performance bound of NFV networks. To capture the dynamic and on-demand NFV features, both the non-bursty traffic, e.g. Poisson process, and the bursty traffic, e.g. Markov Modulated Poisson Process (MMPP), are jointly considered in the developed model to characterise the arriving traffic. To address the challenges of resource competition and end-to-end NFV chaining, the property of convolution associativity and leftover service technologies of SNC are exploited to calculate the available resources of Virtual Network Function (VNF) nodes in the presence of multiple competing traffic, and transfer the complex NFV chain into an equivalent system for performance derivation and analysis. Both the numerical analysis and extensive simulation experiments are conducted to validate the accuracy of the proposed analytical model. Results demonstrate that the analytical performance metrics match well with those obtained from the simulation experiments and numerical analysis. In addition, the developed model is used as a practical and cost-effective tool to investigate the strategies of the service chain design and resource allocations in NFV networks.Engineering and Physical Sciences Research Council (EPSRC
Ultra-reliable Low-latency, Energy-efficient and Computing-centric Software Data Plane for Network Softwarization
Network softwarization plays a significantly important role in the development and deployment of the latest communication system for 5G and beyond. A more flexible and intelligent network architecture can be enabled to provide support for agile network management, rapid launch of innovative network services with much reduction in Capital Expense (CAPEX) and Operating Expense (OPEX). Despite these benefits, 5G system also raises unprecedented challenges as emerging machine-to-machine and human-to-machine communication use cases require Ultra-Reliable Low Latency Communication (URLLC). According to empirical measurements performed by the author of this dissertation on a practical testbed, State of the Art (STOA) technologies and systems are not able to achieve the one millisecond end-to-end latency requirement of the 5G standard on Commercial Off-The-Shelf (COTS) servers. This dissertation performs a comprehensive introduction to three innovative approaches that can be used to improve different aspects of the current software-driven network data plane. All three approaches are carefully designed, professionally implemented and rigorously evaluated. According to the measurement results, these novel approaches put forward the research in the design and implementation of ultra-reliable low-latency, energy-efficient and computing-first software data plane for 5G communication system and beyond
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