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Register a new userPlacement and Routing Optimization Problem for Service Function Chain: State of Art and Future Opportunities
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Added by
Weihan Chen
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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Network Functions Virtualization (NFV) allows implantation of network functions to be independent of dedicated hardware devices. Any series of services can be represented by a service function chain which contains a set of virtualized network functions in a specified order. From the perspective of network performance optimization, the challenges of deploying service chain in network is twofold: 1) the location of placing virtualized network functions and resources allocation scheme; and 2) routing policy for traffic flow among different instances of network function. This article introduces service function chain related optimization problems, summarizes the optimization motivation and mainstream algorithm of virtualized network functions deployment and traffic routing. We hope it can help readers to learn about the current research progress and make further innovation in this field.
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Software Defined Networking and Network Function Virtualization are two paradigms that offer flexible software-based network management. Service providers are instantiating Virtualized Network Functions - e.g., firewalls, DPIs, gateways - to highly facilitate the deployment and reconfiguration of network services with reduced time-to-value. They employ Service Function Chaining technologies to dynamically reconfigure network paths traversing physical and virtual network functions. Providing a cost-efficient virtual function deployment over the network for a set of service chains is a key technical challenge for service providers, and this problem has recently caught much attention from both Industry and Academia. In this paper, we propose a formulation of this problem as an Integer Linear Program that allows one to find the best feasible paths and virtual function placement for a set of services with respect to a total financial cost, while taking into account the (total or partial) order constraints for Service Function Chains of each service and other constraints such as end-to-end latency, anti-affinity rules between network functions on the same physical node and resource limitations in terms of network and processing capacities. Furthermore, we propose a heuristic algorithm based on a linear relaxation of the problem that performs close to optimum for large scale instances.
Network function virtualization (NFV) and software defined networking (SDN) are two promising technologies to enable 5G and 6G services and achieve cost reduction, network scalability, and deployment flexibility. However, migration to full SDN/NFV networks in order to serve these services is a time consuming process and costly for mobile operators. This paper focuses on energy efficiency during the transition of mobile core networks (MCN) to full SDN/NFV networks, and explores how energy efficiency can be addressed during such migration. We propose a general system model containing a combination of legacy nodes and links, in addition to newly introduced NFV and SDN nodes. We refer to this system model as partial SDN and hybrid NFV MCN which can cover different modes of SDN and NFV implementations. Based on this framework, we formulate energy efficiency by considering joint routing and function placement in the network. Since this problem belongs to the class of non-linear integer programming problems, to solve it efficiently, we present a modified Viterbi algorithm (MVA) based on multi-stage graph modeling and a modified Dijkstras algorithm. We simulate this algorithm for a number of network scenarios with different fractions of NFV and SDN nodes, and evaluate how much energy can be saved through such transition. Simulation results confirm the expected performance of the algorithm which saves up to 70% energy compared to network where all nodes are always on. Interestingly, the amount of energy saved by the proposed algorithm in the case of hybrid NFV and partial SDN networks can reach up to 60-90% of the saved energy in full NFV/SDN networks.
Mobile Crowd Sensing (MCS) is the special case of crowdsourcing, which leverages the smartphones with various embedded sensors and users mobility to sense diverse phenomenon in a city. Task allocation is a fundamental research issue in MCS, which is crucial for the efficiency and effectiveness of MCS applications. In this article, we specifically focus on the task allocation in MCS systems. We first present the unique features of MCS allocation compared to generic crowdsourcing, and then provide a comprehensive review for diversifying problem formulation and allocation algorithms together with future research opportunities.
We consider the task of computing (combined) function mapping and routing for requests in Software-Defined Networks (SDNs). Function mapping refers to the assignment of nodes in the substrate network to various processing stages that requests must undergo. Routing refers to the assignment of a path in the substrate network that begins in a source node of the request, traverses the nodes that are assigned functions for this request, and ends in a destination of the request. The algorithm either rejects a request or completely serves a request, and its goal is to maximize the sum of the benefits of the served requests. The solution must abide edge and vertex capacities. We follow the framework suggested by Even for the specification of the processing requirements and routing of requests via processing-and-routing graphs (PR-graphs). In this framework, each request has a demand, a benefit, and PR-graph. Our main result is a randomized approximation algorithm for path computation and function placement with the following guarantee. Let $m$ denote the number of links in the substrate network, $eps$ denote a parameter such that $0< eps <1$, and $opt_f$ denote the maximum benefit that can be attained by a fractional solution (one in which requests may be partly served and flow may be split along multiple paths). Let $cmin$ denote the minimum edge capacity, and let $dmax$ denote the maximum demand. Let $Deltamax$ denote an upper bound on the number of processing stages a request undergoes. If $cmin/(Deltamaxcdotdmax)=Omega((log m)/eps^2)$, then with probability at least $1-frac{1}{m}-textit{exp}(-Omega(eps^2cdot opt_f /(bmax cdot dmax)))$, the algorithm computes a $(1-eps)$-approximate solution.
The networking industry, compared to the compute industry, has been slow in evolving from a closed ecosystem with limited abstractions to a more open ecosystem with well-defined sophisticated high level abstractions. This has resulted in an ossified Internet architecture that inhibits innovation and is unnecessarily complex. Fortunately, there has been an exciting flux of rapid developments in networking in recent times with prominent trends emerging that have brought us to the cusp of a major paradigm shift. In particular, the emergence of technologies such as cloud computing, software defined networking (SDN), and network virtualization are driving a new vision of `networking as a service (NaaS) in which networks are managed flexibly and efficiently cloud computing style. These technologies promise to both facilitate architectural and technological innovation while also simplifying commissioning, orchestration, and composition of network services. In this article, we introduce our readers to these technologies. In the coming few years, the trends of cloud computing, SDN, and network virtualization will further strengthen each others value proposition symbiotically and NaaS will increasingly become the dominant mode of commissioning new networks.
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