18 research outputs found
NFV Orchestrator Placement for Geo-Distributed Systems
The European Telecommunications Standards Institute (ETSI) developed Network
Functions Virtualization (NFV) Management and Orchestration (MANO) framework.
Within that framework, NFV orchestrator (NFVO) and Virtualized Network Function
(VNF) Manager (VNFM) functional blocks are responsible for managing the
lifecycle of network services and their associated VNFs. However, they face
significant scalability and performance challenges in large-scale and
geo-distributed NFV systems. Their number and location have major implications
for the number of VNFs that can be accommodated and also for the overall system
performance. NFVO and VNFM placement is therefore a key challenge due to its
potential impact on the system scalability and performance. In this paper, we
address the placement of NFVO and VNFM in large-scale and geo-distributed NFV
infrastructure. We provide an integer linear programming formulation of the
problem and propose a two-step placement algorithm to solve it. We also conduct
a set of experiments to evaluate the proposed algorithm.Comment: This paper has been accepted for presentation in 16th IEEE
International Symposium on Network Computing and Applications (IEEE NCA 2017
Getting the Most Out of Your VNFs: Flexible Assignment of Service Priorities in 5G
Through their computational and forwarding capabilities, 5G networks can
support multiple vertical services. Such services may include several common
virtual (network) functions (VNFs), which could be shared to increase resource
efficiency. In this paper, we focus on the seldom studied VNF-sharing problem,
and decide (i) whether sharing a VNF instance is possible/beneficial or not,
(ii) how to scale virtual machines hosting the VNFs to share, and (iii) the
priorities of the different services sharing the same VNF. These decisions are
made with the aim to minimize the mobile operator's costs while meeting the
verticals' performance requirements. Importantly, we show that the
aforementioned priorities should not be determined a priori on a per-service
basis, rather they should change across VNFs since such additional flexibility
allows for more efficient solutions. We then present an effective methodology
called FlexShare, enabling near-optimal VNF-sharing decisions in polynomial
time. Our performance evaluation, using real-world VNF graphs, confirms the
effectiveness of our approach, which consistently outperforms baseline
solutions using per-service priorities
Resource Orchestration of 5G Transport Networks for Vertical Industries
The future 5G transport networks are envisioned to support a variety of vertical services through network slicing and efficient orchestration over multiple administrative domains. In this paper, we propose an orchestrator architecture to support vertical services to meet their diverse resource and service requirements. We then present a system model for resource orchestration of transport networks as well as low-complexity algorithms that aim at minimizing service deployment cost and/or service latency. Importantly, the proposed model can work with any level of abstractions exposed by the underlying network or the federated domains depending on their representation of resources.This work has been partially funded by the EU H2020 5G-Transformer Project (grant no. 761536)
A resource allocation framework for network slicing
International audienceTelecommunication networks are converging to a massively distributed cloud infrastructure interconnected with software defined networks. In the envisioned architecture, services will be deployed flexibly and quickly as network slices. Our paper addresses a major bottleneck in this context, namely the challenge of computing the best resource provisioning for network slices in a robust and efficient manner. With tractability in mind, we propose a novel optimization framework which allows fine-grained resource allocation for slices both in terms of network bandwidth and cloud processing. The slices can be further provisioned and auto-scaled optimally based on a large class of utility functions in real-time. Furthermore, by tuning a slice-specific parameter, system designers can trade off traffic-fairness with computing-fairness to provide a mixed fairness strategy. We also propose an iterative algorithm based on the alternating direction method of multipliers (ADMM) that provably converges to the optimal resource allocation and we demonstrate the method’s fast convergence in a wide range of quasi-stationary and dynamic settings