1,507 research outputs found
Effectiveness of segment routing technology in reducing the bandwidth and cloud resources provisioning times in network function virtualization architectures
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
Scalable and Reliable Middlebox Deployment
Middleboxes are pervasive in modern computer networks providing functionalities beyond mere packet forwarding. Load balancers, intrusion detection systems, and network address translators are typical examples of middleboxes. Despite their benefits, middleboxes come with several challenges with respect to their scalability and reliability.
The goal of this thesis is to devise middlebox deployment solutions that are cost effective, scalable, and fault tolerant. The thesis includes three main contributions: First, distributed service function chaining with multiple instances of a middlebox deployed on different physical servers to optimize resource usage; Second, Constellation, a geo-distributed middlebox framework enabling a middlebox application to operate with high performance across wide area networks; Third, a fault tolerant service function chaining system
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)
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
Clustering algorithms for dynamic adaptation of service function chains
Network function virtualization is a pillar-stone of today’s network architectures as it offers better management and elasticity and allows also a flexible maintenance of services running on shared resources over cloud environments.
Network functions traditionally hosted on dedicated hardware are now provided over software based components that might run either on virtual machines or on containers. The major advantage of this transition is that it makes the deployment of new services easier while optimizing the management and administration of network architectures. It is much easier to spin up a new virtual machine/container hosting a network function or a specific application described as a service function chain, than to deploy a new hardware based equipment and checking its compatibility with the rest of the architecture.
With all the advantages that this new paradigm offers comes a set of challenges related mainly to: 1) optimizing the resource consumption on the shared infrastructure 2) making the best decision of placing the virtual functions that respects at the same time clients’ requirements and also leverages the available resources on the substrate network in terms of different metrics (e.g., CPU, memory, latency, bandwidth).
This aspect of Network Function Virtualization-NFV and Service Function Chains-SFC placement have been treated in so many research works that propose approaches ensuring optimal placement and chaining of VNFs in virtualized networks, but as the adoption of these technologies gets more important in real network setups, and given the strict restrictions of today’s’ applications (e.g. latency highly-sensitive applications, or availability highly-sensitive service, etc.), it is always important to consider all the parameters impacting the network management in cloud environments.
In this research project, we develop new approaches for placement and chaining of virtual network functions in cloud-based environments. The first approach allows forming on demand clusters of servers deployed in a physical infrastructure. These servers are grouped according to their similar attributes (e.g., CPU-intensive server, energy-efficient server, etc).
This process is a proactive measure to ensure that SFCs are hosted in servers that meet their specific metrics requirements (CPU, memory, disk, etc.). It employs a meta-heuristic called CRO (Chemical Reaction Optimization) to decide of the best VNF placement guaranteeing optimal resource consumption in terms of CPU / memory. We employ CRO also to ensure the lowest latencies during the routing between the different VNFs. In fact, the E2E delay is an important aspect to consider, as most current applications require low latencies and shortest run times. In the second approach, the clusters are formed using algorithms based on meta-heuristics, including the CRO, allowing to improve the quality of clusters formed in terms of similarity, density and modularity
Online Service Provisioning in NFV-enabled Networks Using Deep Reinforcement Learning
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 %
Dynamic VNF Placement, Resource Allocation and Traffic Routing in 5G  
5G networks are going to support a variety of vertical services, with a
diverse set of key performance indicators (KPIs), by using enabling
technologies such as software-defined networking and network function
virtualization. It is the responsibility of the network operator to efficiently
allocate the available resources to the service requests in such a way to honor
KPI requirements, while accounting for the limited quantity of available
resources and their cost. A critical challenge is that requests may be highly
varying over time, requiring a solution that accounts for their dynamic
generation and termination. With this motivation, we seek to make joint
decisions for request admission, resource activation, VNF placement, resource
allocation, and traffic routing. We do so by considering real-world aspects
such as the setup times of virtual machines, with the goal of maximizing the
mobile network operator profit. To this end, first, we formulate a one-shot
optimization problem which can attain the optimum solution for small size
problems given the complete knowledge of arrival and departure times of
requests over the entire system lifespan. We then propose an efficient and
practical heuristic solution that only requires this knowledge for the next
time period and works for realistically-sized scenarios. Finally, we evaluate
the performance of these solutions using real-world services and large-scale
network topologies. {Results demonstrate that our heuristic solution performs
better than a state-of-the-art online approach and close to the optimum
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