111 research outputs found

    Learning Augmented Optimization for Network Softwarization in 5G

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    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

    Knowledge-defined networking

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    The research community has considered in the past the application of Artificial Intelligence (AI) techniques to control and operate networks. A notable example is the Knowledge Plane proposed by D.Clark et al. However, such techniques have not been extensively prototyped or deployed in the field yet. In this paper, we explore the reasons for the lack of adoption and posit that the rise of two recent paradigms: Software-Defined Networking (SDN) and Network Analytics (NA), will facilitate the adoption of AI techniques in the context of network operation and control. We describe a new paradigm that accommodates and exploits SDN, NA and AI, and provide use-cases that illustrate its applicability and benefits. We also present simple experimental results that support, for some relevant use-cases, its feasibility. We refer to this new paradigm as Knowledge-Defined Networking (KDN).Peer ReviewedPostprint (author's final draft

    Online Service Provisioning in NFV-enabled Networks Using Deep Reinforcement Learning

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    In this paper, we study a Deep Reinforcement Learning (DRL) based framework for an online end-user service provisioning in a Network Function Virtualization (NFV)-enabled network. We formulate an optimization problem aiming to minimize the cost of network resource utilization. The main challenge is provisioning the online service requests by fulfilling their Quality of Service (QoS) under limited resource availability. Moreover, fulfilling the stochastic service requests in a large network is another challenge that is evaluated in this paper. To solve the formulated optimization problem in an efficient and intelligent manner, we propose a Deep Q-Network for Adaptive Resource allocation (DQN-AR) in NFV-enable network for function placement and dynamic routing which considers the available network resources as DQN states. Moreover, the service's characteristics, including the service life time and number of the arrival requests, are modeled by the Uniform and Exponential distribution, respectively. In addition, we evaluate the computational complexity of the proposed method. Numerical results carried out for different ranges of parameters reveal the effectiveness of our framework. In specific, the obtained results show that the average number of admitted requests of the network increases by 7 up to 14% and the network utilization cost decreases by 5 and 20 %

    Scalable Orchestration of Service Function Chains in NFV-Enabled Networks: A Federated Reinforcement Learning Approach

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    This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordNetwork function virtualization (NFV) is critical to the scalability and flexibility of various network services in the form of service function chains (SFCs), which refer to a set of Virtual Network Functions (VNFs) chained in a specific order. However, the NFV performance is hard to fulfill the ever-increasing requirements of network services mainly due to the static orchestrations of SFCs. To tackle this issue, a novel Scalable SFC Orchestration (SSCO) scheme is proposed in this paper for NFV-enabled networks via federated reinforcement learning. SSCO has three remarkable characteristics distinguishing from the previous work: (1) A federated-learning-based framework is designed to train a global learning model, with time-variant local model explorations, for scalable SFC orchestration, while avoiding data sharing among stakeholders; (2) SSCO allows for parameter update among local clients and the cloud server just at the first and last epochs of each episode to ensure that distributed clients can make model optimization at a low communication cost; (3) SSCO introduces an efficient deep reinforcement learning (DRL) approach, with the local learning knowledge of available resources and instantiation cost, to map VNFs into networks flexibly. Furthermore, a loss-weight-based mechanism is proposed to generate and exploit reference samples in replay buffers for future training, avoiding the strong relevance of samples. Simulation results obtained from different working scenarios demonstrate that SSCO can significantly reduce placement errors and improve resource utilization ratio to place time-variant VNFs compared with the state-of-the-art mechanisms. Furthermore, the results show that the proposed approach can achieve desirable scalability

    Towards Zero Touch Next Generation Network Management

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    The current trend in user services places an ever-growing demand for higher data rates, near-real-time latencies, and near-perfect quality of service. To meet such demands, fundamental changes were made to the front and mid-haul and backbone networking segments servicing them. One of the main changes made was virtualizing the networking components to allow for faster deployment and reconfiguration when needed. However, adopting such technologies poses several challenges, such as improving the performance and efficiency of these systems by properly orchestrating the services to the ideal edge device. A second challenge is ensuring the backbone optical networking maximizes and maintains the throughput levels under more dynamically variant conditions. A third challenge is addressing the limitation of placement techniques in O-RAN. In this thesis, we propose using various optimization modeling and machine learning techniques in three segments of network systems towards lowering the need for human intervention targeting zero-touch networking. In particular, the first part of the thesis applies optimization modeling, heuristics, and segmentation to improve the locally driven orchestration techniques, which are used to place demands on edge devices throughput to ensure efficient and resilient placement decisions. The second part of the thesis proposes using reinforcement learning (RL) techniques on a nodal base to address the dynamic nature of demands within an optical networking paradigm. The RL techniques ensure blocking rates are kept to a minimum by tailoring the agents’ behavior based on each node\u27s demand intake throughout the day. The third part of the thesis proposes using transfer learning augmented reinforcement learning to drive a network slicing-based solution in O-RAN to address the stringent and divergent demands of 5G applications. The main contributions of the thesis consist of three broad parts. The first is developing optimal and heuristic orchestration algorithms that improve demands’ performance and reliability in an edge computing environment. The second is using reinforcement learning to determine the appropriate spectral placement for demands within isolated optical paths, ensuring lower fragmentation and better throughput utilization. The third is developing a heuristic controlled transfer learning augmented reinforcement learning network slicing in an O-RAN environment. Hence, ensuring improved reliability while maintaining lower complexity than traditional placement techniques
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