13,150 research outputs found
A Q-learning-based approach for deploying dynamic service function chains
As the size and service requirements of today’s networks gradually increase, large numbers of proprietary devices are deployed, which leads to network complexity, information security crises and makes network service and service provider management increasingly difficult. Network function virtualization (NFV) technology is one solution to this problem. NFV separates network functions from hardware and deploys them as software on a common server. NFV can be used to improve service flexibility and isolate the services provided for each user, thus guaranteeing the security of user data. Therefore, the use of NFV technology includes many problems worth studying. For example, when there is a free choice of network path, one problem is how to choose a service function chain (SFC) that both meets the requirements and offers the service provider maximum profit. Most existing solutions are heuristic algorithms with high time efficiency, or integer linear programming (ILP) algorithms with high accuracy. It’s necessary to design an algorithm that symmetrically considers both time efficiency and accuracy. In this paper, we propose the Q-learning Framework Hybrid Module algorithm (QLFHM), which includes reinforcement learning to solve this SFC deployment problem in dynamic networks. The reinforcement learning module in QLFHM is responsible for the output of alternative paths, while the load balancing module in QLFHM is responsible for picking the optimal solution from them. The results of a comparison simulation experiment on a dynamic network topology show that the proposed algorithm can output the approximate optimal solution in a relatively short time while also considering the network load balance. Thus, it achieves the goal of maximizing the benefit to the service provider
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
Understand Your Chains: Towards Performance Profile-based Network Service Management
Allocating resources to virtualized network functions and services to meet
service level agreements is a challenging task for NFV management and
orchestration systems. This becomes even more challenging when agile
development methodologies, like DevOps, are applied. In such scenarios,
management and orchestration systems are continuously facing new versions of
functions and services which makes it hard to decide how much resources have to
be allocated to them to provide the expected service performance. One solution
for this problem is to support resource allocation decisions with performance
behavior information obtained by profiling techniques applied to such network
functions and services.
In this position paper, we analyze and discuss the components needed to
generate such performance behavior information within the NFV DevOps workflow.
We also outline research questions that identify open issues and missing pieces
for a fully integrated NFV profiling solution. Further, we introduce a novel
profiling mechanism that is able to profile virtualized network functions and
entire network service chains under different resource constraints before they
are deployed on production infrastructure.Comment: Submitted to and accepted by the European Workshop on Software
Defined Networks (EWSDN) 201
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 %
- …