Performance Anomaly Detection Models of Virtual Machines for Network Function Virtualization Infrastructure with Machine Learning

2018 ◽  
pp. 479-488 ◽  
Author(s):  
Juan Qiu ◽  
Qingfeng Du ◽  
Yu He ◽  
YiQun Lin ◽  
Jiaye Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Virtual Machines ◽  
Network Function Virtualization ◽  
Performance Anomaly ◽  
Network Function

scholarly journals Dynamic Service Function Chaining Orchestration in a Multi-Domain: A Heuristic Approach Based on SRv6

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6563
Author(s):  
Yutong Wu ◽  
Jinhe Zhou
Keyword(s):  
Virtual Machines ◽  
Search Algorithm ◽  
Resource Consumption ◽  
Network Function Virtualization ◽  
Ant Colony Optimization Algorithm ◽  
Network Resource ◽  
Load Pressure ◽  
Service Function ◽  
Network Function ◽  
Series Of Experiments

With the emergence of virtualization technology, Network Function Virtualization (NFV) and Software Defined Networking (SDN) make the network function abstract from the hardware and allow it to be run on virtual machines. These technologies can help to provide more efficient services to users by Service Function Chaining (SFC). The sequence of multiple VNFs required by network operators to perform traffic steering is called SFC. Mapping and deploying SFC on the physical network can enable users to obtain customized services in time. At present, a key problem in deploying SFC is how to reduce network resource consumption and load pressure while ensuring the corresponding services for users. In this paper, we first introduce an NFV architecture for SFC deployment, and illustrate the SFC orchestration process which is based on SRv6 in multi-domain scenario. Then, we propose an effective SFC dynamic orchestration algorithm. First, we use Breadth-First Search algorithm to traverse network and find the shortest path for deploying VNFs. Next, we use the improved Ant Colony Optimization algorithm to generate the optimal deployment scheme. Finally, we conduct a series of experiments to verify the performance of our algorithm. Compared with other deployment algorithms, the results show that our solution effectively optimizes end-to-end delay, bandwidth resource consumption and load balancing.

scholarly journals Machine-Learning Defined Networking: Towards Intelligent Networks

2021 ◽  
pp. 3-12
Author(s):  
Sebastian Troia
Keyword(s):  
Machine Learning ◽  
Service Providers ◽  
Dynamic Network ◽  
Network Function Virtualization ◽  
Traffic Demand ◽  
Network Function ◽  
Network Utilization ◽  
Underlying Network ◽  
Network Operations ◽  
Network Technologies

AbstractWith the advent of 5G technology and an ever-increasing traffic demand, today Communication Service Providers (CSPs) experience a progressive congestion of their networks. The operational complexity, the use of manual configuration, the static nature of current technologies together with fast-changing traffic profiles lead to: inefficient network utilization, over-provisioning of resources and very high Capital Expenditures (CapEx) and Operational Expenses (OpEx). This situation is forcing the CSPs to change their underlying network technologies, and have started to look at new technological solutions that increase the level of programmability, control, and flexibility of configuration, while reducing the overall costs related to network operations. Software Define Networking (SDN), Network Function Virtualization (NFV) and Machine Learning (ML) are accepted as effective solutions to reduce CapEx and OpEx and to boost network innovation. This chapter summarizes the content of my Ph.D. thesis, by presenting new ML-based approaches in order to efficiently optimize resources in 5G metro-core SDN/NFV networks. The main goal is to provide the modern CSP with intelligent and dynamic network optimization tools in order to address the requirements of increasing traffic demand and 5G technology.

scholarly journals Impact of the Maximum Number of Switching Reconfigurations on the Cost Saving in Network Function Virtualization Environments with Elastic Optical Interconnection

2019 ◽  
Vol 9 (23) ◽  
pp. 5167
Author(s):  
Vincenzo Eramo ◽  
Francesco G. Lavacca ◽  
Tiziana Catena
Keyword(s):  
Computational Complexity ◽  
Cost Saving ◽  
Virtual Machines ◽  
New Technology ◽  
Network Function Virtualization ◽  
Optical Interconnection ◽  
Network Function ◽  
Network Functions ◽  
The Cost ◽  
The Impact

Network Function Virtualization is based on the virtualization of the network functions and it is a new technology allowing for a more flexible allocation of cloud and bandwidth resources. In order to employ the flexibility of the technology and to adapt its use according to the traffic variation, reconfigurations of the cloud and bandwidth resources are needed by means of both migration of the Virtual Machines executing the network functions and reconfiguration of circuits interconnecting the Virtual Machines. The objective of the paper is to study the impact of the maximum number of switch reconfigurations on the cost saving that the Networking Function Virtualization technology allows us to achieve. The problem is studied in the case of a scenario with an elastic optical network interconnecting datacenters in which the Virtual Machines are executed. The problem can be formulated as an Integer Linear Programming one introducing a constraint on the maximum number of switch reconfigurations but due to its computational complexity we propose a low computational complexity heuristic allowing for results close to the optimization ones. The results show how the limitation on the number of possible reconfigurations has to be taken into account to evaluate the effectiveness in terms of cost saving that the Virtual Machine migrations in Network Function Virtualization environment allows us to achieve.

scholarly journals Application of Machine Learning Algorithms for Traffic Forecasting in Dynamic Optical Networks with Service Function Chains

2020 ◽  
Vol 45 (3) ◽  
pp. 217-232
Author(s):  
Daniel Szostak ◽  
Krzysztof Walkowiak
Keyword(s):  
Machine Learning ◽  
Optical Networks ◽  
Network Traffic ◽  
Optical Network ◽  
Machine Learning Algorithms ◽  
Network Function Virtualization ◽  
Service Function ◽  
Traffic Forecasting ◽  
Network Function ◽  
Traffic Characteristics

AbstractKnowledge about future optical network traffic can be beneficial for network operators in terms of decreasing an operational cost due to efficient resource management. Machine Learning (ML) algorithms can be employed for forecasting traffic with high accuracy. In this paper we describe a methodology for predicting traffic in a dynamic optical network with service function chains (SFC). We assume that SFC is based on the Network Function Virtualization (NFV) paradigm. Moreover, other type of traffic, i.e. regular traffic, can also occur in the network. As a proof of effectiveness of our methodology we present and discuss numerical results of experiments run on three benchmark networks. We examine six ML classifiers. Our research shows that it is possible to predict a future traffic in an optical network, where SFC can be distinguished. However, there is no one universal classifier that can be used for each network. Choice of an ML algorithm should be done based on a network traffic characteristics analysis.

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

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
V. Eramo ◽  
A. Tosti ◽  
E. Miucci
Keyword(s):  
Power Consumption ◽  
Virtual Machines ◽  
Blocking Probability ◽  
Network Function Virtualization ◽  
Traffic Demand ◽  
Service Function ◽  
Network Function ◽  
Service Function Chain ◽  
Commercial Off The Shelf ◽  
Resource Dimensioning

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.

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