79 research outputs found
Performance Modeling of Softwarized Network Services Based on Queuing Theory with Experimental Validation
Network Functions Virtualization facilitates the automation of the scaling of softwarized network services (SNSs).
However, the realization of such a scenario requires a way to
determine the needed amount of resources so that the SNSs performance requisites are met for a given workload. This problem is
known as resource dimensioning, and it can be efficiently tackled
by performance modeling. In this vein, this paper describes an
analytical model based on an open queuing network of G/G/m
queues to evaluate the response time of SNSs. We validate our
model experimentally for a virtualized Mobility Management
Entity (vMME) with a three-tiered architecture running on
a testbed that resembles a typical data center virtualization
environment. We detail the description of our experimental
setup and procedures. We solve our resulting queueing network
by using the Queueing Networks Analyzer (QNA), Jackson’s
networks, and Mean Value Analysis methodologies, and compare
them in terms of estimation error. Results show that, for medium
and high workloads, the QNA method achieves less than half of
error compared to the standard techniques. For low workloads,
the three methods produce an error lower than 10%. Finally,
we show the usefulness of the model for performing the dynamic
provisioning of the vMME experimentally.This work has been partially funded by the H2020 research
and innovation project 5G-CLARITY (Grant No. 871428)National research
project 5G-City: TEC2016-76795-C6-4-RSpanish Ministry of
Education, Culture and Sport (FPU Grant 13/04833). We would also like to
thank the reviewers for their valuable feedback to enhance the quality
and contribution of this wor
Dynamic Resource Provisioning of a Scalable E2E Network Slicing Orchestration System
Network slicing allows different applications and
network services to be deployed on virtualized resources running
on a common underlying physical infrastructure. Developing
a scalable system for the orchestration of end-to-end (E2E)
mobile network slices requires careful planning and very reliable
algorithms. In this paper, we propose a novel E2E Network
Slicing Orchestration System (NSOS) and a Dynamic Auto-
Scaling Algorithm (DASA) for it. Our NSOS relies strongly on
the foundation of a hierarchical architecture that incorporates
dedicated entities per domain to manage every segment of the
mobile network from the access, to the transport and core
network part for a scalable orchestration of federated network
slices. The DASA enables the NSOS to autonomously adapt
its resources to changes in the demand for slice orchestration
requests (SORs) while enforcing a given mean overall time taken
by the NSOS to process any SOR. The proposed DASA includes
both proactive and reactive resource provisioning techniques).
The proposed resource dimensioning heuristic algorithm of the
DASA is based on a queuing model for the NSOS, which consists
of an open network of G/G/m queues. Finally, we validate the
proper operation and evaluate the performance of our DASA
solution for the NSOS by means of system-level simulations.This research work is partially supported by the European
Union’s Horizon 2020 research and innovation program under
the 5G!Pagoda project, the MATILDA project and the
Academy of Finland 6Genesis project with grant agreement
No. 723172, No. 761898 and No. 318927, respectively. It was
also partially funded by the Academy of Finland Project CSN
- under Grant Agreement 311654 and the Spanish Ministry of
Education, Culture and Sport (FPU Grant 13/04833), and the
Spanish Ministry of Economy and Competitiveness and the
European Regional Development Fund (TEC2016-76795-C6-
4-R)
An adaptive scaling mechanism for managing performance variations in network functions virtualization: A case study in an NFV-based EPC
The scaling is a fundamental task that allows addressing performance variations in Network Functions Virtualization (NFV). In the literature, several approaches propose scaling mechanisms that differ in the utilized technique (e.g., reactive, predictive and machine learning-based). The scaling in NFV must be accurate both at the time and the number of instances to be scaled, aiming at avoiding unnecessary procedures of provisioning and releasing of resources; however, achieving a high accuracy is a non-trivial task. In this paper, we propose for NFV an adaptive scaling mechanism based on Q-Learning and Gaussian Processes that are utilized by an agent to carry out an improvement strategy of a scaling policy, and therefore, to make better decisions for managing performance variations. We evaluate our mechanism by simulations, in a case study in a virtualized Evolved Packet Core, corroborating that it is more accurate than approaches based on static threshold rules and Q-Learning without a policy improvement strategy
Operational Aspects of Network Slicing in LTE and 5G Architectures
With the advances in cloud computing, the telecom operators are moving towards virtualization and cloud model. The future mobile networks are going to be deployed in the cloud with the help of Software Defined Networks (SDN) and Network Functions Virtualization (NFV) technologies. This thesis discusses various aspects related to prototyping and orchestration of slicing in LTE and 5G core networks using open-source technologies. The network slices are created in the user plane of the core network for differentiating the user traffic in terms of their QoS requirements. The Service Based Architecture (SBA) of 5G core is realized with the help of Google Remote Procedure Call (gRPC) as Service Based Interface (SBI) and Consul as Network Function Repository Function. On top of the setup created, the concept of Look-aside Load Balancing is used, to suit the 5G SBA and efficiently handle the incoming traffic load. With the Access and Mobility Management Function as a use case for load balancing, it is concluded that carefully chosen load balancing algorithms can significantly lessen the control plane latency when compared to simple random or round-robin schemes
5G Infrastructure Network Slicing: E2E Mean Delay Model and Effectiveness Assessment to Reduce Downtimes in Industry 4.0
This work has been partially funded by the H2020 project 5G-CLARITY (Grant No. 871428) and the Spanish national project TRUE-5G (PID2019-108713RB-C53).Fifth Generation (5G) is expected to meet stringent performance network requisites of
the Industry 4.0. Moreover, its built-in network slicing capabilities allow for the support of the
traffic heterogeneity in Industry 4.0 over the same physical network infrastructure. However, 5G
network slicing capabilities might not be enough in terms of degree of isolation for many private
5G networks use cases, such as multi-tenancy in Industry 4.0. In this vein, infrastructure network
slicing, which refers to the use of dedicated and well isolated resources for each network slice at every
network domain, fits the necessities of those use cases. In this article, we evaluate the effectiveness of
infrastructure slicing to provide isolation among production lines (PLs) in an industrial private 5G
network. To that end, we develop a queuing theory-based model to estimate the end-to-end (E2E)
mean packet delay of the infrastructure slices. Then, we use this model to compare the E2E mean
delay for two configurations, i.e., dedicated infrastructure slices with segregated resources for each
PL against the use of a single shared infrastructure slice to serve the performance-sensitive traffic
from PLs. Also we evaluate the use of Time-Sensitive Networking (TSN) against bare Ethernet to
provide layer 2 connectivity among the 5G system components. We use a complete and realistic
setup based on experimental and simulation data of the scenario considered. Our results support the
effectiveness of infrastructure slicing to provide isolation in performance among the different slices.
Then, using dedicated slices with segregated resources for each PL might reduce the number of the
production downtimes and associated costs as the malfunctioning of a PL will not affect the network
performance perceived by the performance-sensitive traffic from other PLs. Last, our results show
that, besides the improvement in performance, TSN technology truly provides full isolation in the
transport network compared to standard Ethernet thanks to traffic prioritization, traffic regulation,
and bandwidth reservation capabilities.H2020 project 5G-CLARITY 871428Spanish Government PID2019-108713RB-C53TRUE-5
Optimizing total cost of ownership (TCO) for 5G multi-tenant mobile backhaul (MBH) optical transport networks
Legacy network elements are reaching end-of-life and packet-based transport networks are not efficiently optimized. In particular, high density cell architecture in future 5G networks will face big technical and financial challenges due to avalanche of traffic volume and massive growth in connected devices. Raising density and ever-increasing traffic demand within future 5G Heterogeneous Networks (HetNets) will result in huge deployment, expansion and operating costs for upcoming Mobile BackHaul (MBH) networks with flat revenue generation. Thus, the goal of this dissertation is to provide an efficient physical network planning mechanism and an optimized resource engineering tool in order to reduce the Total Cost of Ownership (TCO) and increase the generated revenues. This will help Service Providers (SPs) and Mobile Network Operators (MNOs) to improve their network scalability and maintain positive Project Profit Margins (PPM).
In order to meet this goal, three key issues are required to be addressed in our framework and are summarized as follows: i) how to design and migrate to a scalable and reliable MBH network in an optimal cost?, ii) how to control the deployment and activation of the network resources in such MBH based on required traffic demand in an efficient and cost-effective way?, and iii) how to enhance the resource sharing in such network and maximize the profit margins in an efficient way?
As part of our contributions to address the first issue highlighted above and to plan the MBH with reduced network TCO and improved scalability, we propose a comprehensive migration plan towards an End-to-End Integrated-Optical-Packet-Network (E2-IOPN) for SP optical transport networks. We review various empirical challenges faced by a real SP during the transformation process towards E2-IOPN as well as the implementation of an as-built plan and a high-level design (HLD) for migrating towards lower cost-per-bit GPON, MPLS-TP, OTN and next-generation DWDM technologies. Then, we propose a longer-term strategy based on SDN and NFV approach that will offer rapid end-to-end service provisioning with costefficient centralized network control. We define CapEx and OpEx cost models and drive a cost comparative study that shows the benefit and financial impact of introducing new low-cost packet-based technologies to carry traffic from legacy and new services.
To address the second issue, we first introduce an algorithm based on a stochastic geometry model (Voronoi Tessellation) to more precisely define MBH zones within a geographical area and more accurately calculate required traffic demands and related MBH infrastructure. In order to optimize the deployment and activation of the network resources in the MBH in an efficient and cost-effective way, we propose a novel method called BackHauling-as-a-Service (BHaaS) for network planning and Total Cost of Ownership (TCO) analysis based on required traffic demand and a "You-pay-only-for-what-you-use" approach. Furthermore, we enhance BHaaS performance by introducing a more service-aware method called Traffic-Profile-asa- Service (TPaaS) to further drive down the costs based on yearly activated traffic profiles. Results show that BHaaS and TPaaS may enhance by 22% the project benefit compared to traditional TCO model.
Finally, we introduce a new cost (CapEx and OpEx) models for 5G multi-tenant Virtualized MBH (V-MBH) as part of our contribution to address the third issue. In fact, in order to enhance the resource sharing and maximize the network profits, we drive a novel pay-as-yougrow and optimization model for the V-MBH called Virtual-Backhaul-as-a-Service (VBaaS). VBaaS can serve as a planning tool to optimize the Project Profit Margin (PPM) while considering the TCO and the yearly generated Return-on-Investment (ROI). We formulate an MNO Pricing Game (MPG) for TCO optimization to calculate the optimal Pareto-Equilibrium pricing strategy for offered Tenant Service Instances (TSI). Then, we compare CapEx, OpEx, TCO, ROI and PPM for a specific use-case known in the industry as CORD project using Traditional MBH (T-MBH) versus Virtualized MBH (V-MBH) as well as using randomized versus Pareto-Equilibrium pricing strategies.
The results of our framework offer SPs and MNOs a more precise estimation of traffic demand, an optimized infrastructure planning and yearly resource deployment as well as an optimized TCO analysis (CapEx and OpEx) with enhanced pricing strategy and generated ROI. Numerical results show more than three times increase in network profitability using our proposed solutions compared with Traditional MBH (T-MBH) methods
LTE Optimization and Resource Management in Wireless Heterogeneous Networks
Mobile communication technology is evolving with a great pace. The development of the Long Term Evolution (LTE) mobile system by 3GPP is one of the milestones in this direction. This work highlights a few areas in the LTE radio access network where the proposed innovative mechanisms can substantially improve overall LTE system performance. In order to further extend the capacity of LTE networks, an integration with the non-3GPP networks (e.g., WLAN, WiMAX etc.) is also proposed in this work. Moreover, it is discussed how bandwidth resources should be managed in such heterogeneous networks. The work has purposed a comprehensive system architecture as an overlay of the 3GPP defined SAE architecture, effective resource management mechanisms as well as a Linear Programming based analytical solution for the optimal network resource allocation problem. In addition, alternative computationally efficient heuristic based algorithms have also been designed to achieve near-optimal performance
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