MANOaaS: A Multi-Tenant NFV MANO for 5G Network Slices

The dramatic densification of connected mobile devices and the expected use cases from the vertical industry demand an innovative network design that meets upcoming stringent requirements. The adoption and harmonized integration of novel concepts, such as network functions virtualization and network programmability, enables the system to master the high expectation -- from the fifth generation communication network in support of flexibility -- to provide tailored and mutually isolated network slices, high performance, agility, and automation. This effectively involves a number of technical challenges for managing and orchestrating physical and virtualized slice resources by means of an advanced management and orchestration (MANO) system. This article sheds light on potential benefits and implementation aspects when the MANO framework is abstracted into customized and distributed MANO instances, thereby empowering the MANO-as-a-service (MANOaaS) paradigm. In particular, such distributed instances are provided to different network tenants for a greater level of control on requested network slice(s). The notion of management level agreements in the context of MANOaaS is introduced as well as differentiated per tenant while being embedded into the proposed architecture. We also position the proposed MANOaaS concept and associated extensions to the MANO reference architecture from the viewpoint of standardization bodies and ongoing open source projects.This work has been partially funded by the European Union Horizon-2020 Project 5G-CARMEN under Grant Agreement 825012 and Project 5G-Transformer under Grant Agreement 761536

Dynamic control of NFV forwarding graphs with end-to-end deadline constraints

There is a strong industrial drive to use cloud computing technologies and concepts for providing timing sensitive services in the networking domain since it would provide the means to share the physical resources among multiple users and thus increase the elasticity and reduce the costs. In this work, we develop a mathematical model for user-stateless virtual network functions forming a forwarding graph. The model captures uncertainties of the performance of these virtual resources as well as the time-overhead needed to instantiate them. The model is used to derive a service controller for horizontal scaling of the virtual resources as well as an admission controller that guarantees that packets exiting the forwarding graph meet their end-to-end deadline. The Automatic Service and Admission Controller (AutoSAC) developed in this work uses feedback and feedforward making it robust against uncertainties of the underlying infrastructure. Also, it has a fast reaction time to changes in the input

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)

Throughput optimization for admitting NFV-enabled requests in cloud networks

Network softwarization is emerging as a techno-economic transformation trend that impacts the way that network service providers deliver their network services significantly. As a key ingredient of such a trend, network function virtualization (NFV) is shown to enable elastic and inexpensive network services for next-generation networks, through deploying flexible virtualized network functions (VNFs) running in virtual computing platforms. Different VNFs can be chained together to form different service chains for different network services, to meet various user data routing demands. From the service provider point of view, such services are usually implemented by VNF instances in a cloudlet network consisting of a set of data centers and switches. In this paper we consider provisioning network services in a cloud network for implementing VNF instances of service chains, where the VNF instances in each data center are partitioned into K types with each hosting one type of service chain. We investigate the throughput maximization problem with the aim to admit as many user requests as possible while minimizing the implementation cost of the requests, assuming that limited numbers of instances of each service chain have been instantiated in data centers. We first show the problem is NP-Complete, and propose an optimal algorithm for a special case of the problem when all requests have identical packet rates; otherwise, we devise two approximation algorithms with approximation ratios, depending on whether the packet traffic of each request is splittable. If arrivals of future requests are not known in advance, we study the online throughput maximization problem by proposing an online algorithm with a competitive ratio. We finally conduct experiments to evaluate the performance of the proposed algorithms by simulations. Simulation results show that the performance of the proposed algorithms are promising

Enabling multicast slices in edge networks

Telecommunication networks are undergoing a disruptive transition towards distributed mobile edge networks with virtualized network functions (VNFs) (e.g., firewalls, Intrusion Detection Systems (IDSs), and transcoders) within the proximity of users. This transition will enable network services, especially IoT applications, to be provisioned as network slices with sequences of VNFs, in order to guarantee the performance and security of their continuous data and control flows. In this paper we study the problems of delay-aware network slicing for multicasting traffic of IoT applications in edge networks. We first propose exact solutions by formulating the problems into Integer Linear Programs (ILPs). We further devise an approximation algorithm with an approximation ratio for the problem of delay-aware network slicing for a single multicast slice, with the objective to minimize the implementation cost of the network slice subject to its delay requirement constraint. Given multiple multicast slicing requests, we also propose an efficient heuristic that admits as many user requests as possible, through exploring the impact of a non-trivial interplay of the total computing resource demand and delay requirements. We then investigate the problem of delay-oriented network slicing with given levels of delay guarantees, considering that different types of IoT applications have different levels of delay requirements, for which we propose an efficient heuristic based on Reinforcement Learning (RL). We finally evaluate the performance of the proposed algorithms through both simulations and implementations in a real test-bed. Experimental results demonstrate that the proposed algorithms is promising

Fault Tolerant Placement of Stateful VNFs and Dynamic Fault Recovery in Cloud Networks

Traditional network functions such as firewalls are implemented in costly dedicated hardware. By decoupling network functions from physical devices, network function virtualization enables virtual network functions (VNF) to run in virtual machines (VMs). However, VNFs are vulnerable to various faults such as software and hardware failures. To enhance VNF fault tolerance, the deployment of backup VNFs in stand-by VM instances is necessary. In case of stateful VNFs, stand-by instances require constant state updates from active instances during its operation. This will guarantee a correct and seamless handover from failed instances to stand-by instances after failures. Nevertheless, such state updates to stand-by instances could consume significant network bandwidth resources and lead to potential admission failures for VNF requests. In this paper, we study the fault-tolerant VNF placement problem with the optimization objective of admitting as many requests as possible. In particular, the VNF placement of active/stand-by instances, the request routing paths to active instances, and state transfer paths to stand-by instances are jointly considered. We devise an efficient heuristic algorithm to solve this problem. For the fault tolerance problem without computing or bandwidth constraints, we also propose two bicriteria approximation algorithms with performance guarantees for a special case of the problem. Given the placement locations of VNFs, some of them may go faulty. We thus consider the dynamic fault recovery problem, for which we propose an approximation algorithm that dynamically switches traffic processing from faulty VNFs to normal ones. Simulations with realistic settings show that our algorithms can significantly improve the request admission rate compared to conventional approaches

Efficient NFV-Enabled Multicasting in SDNs

IEEE Multicasting is a fundamental functionality of many network applications, including online conferencing, event monitoring, video streaming, and so on. To ensure reliable, secure and scalable multicasting, a service chain that consists of network functions (e.g., firewalls, Intrusion Detection Systems (IDSs), and transcoders) usually is associated with each multicast request. We refer to such a multicast request with service chain requirement as an NFV-enabled multicast request. In this paper, we study NFV-enabled multicasting in a Software- Defined Network (SDN) with an aim to maximize network throughput while minimizing the implementation cost of admitted NFV-enabled multicast requests, subject to network resource capacity, where the implementation cost of a request consists of its computing resource consumption cost in servers and its network bandwidth consumption cost when routing and processing its data packets in the network. To this end, we first formulate two NFV-enabled multicasting problems with and without resource capacity constraints and one online NFV-enabled multicasting problem.We then devise two approximation algorithms with an approximation ratio of 2M for the NFV-enabled multicasting problems with and without resource capacity constraints, if the number of servers for implementing the service chain of each request is no greater than a constant M (≥1). We also study dynamic admissions of NFV-enabled multicast requests without the knowledge of future request arrivals with the objective to maximize the network throughput, for which we propose an efficient heuristic, and for a special case of dynamic request admissions, we devise an online algorithm with a competitive ratio of O(log n) for it when M = 1, where n is the number of nodes in the network. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising and outperform existing heuristics