FrA1 – Software Defined Networking

Friday, 21 June 2019, 8:30-10:30, Room 1

 

Session Chair: Kamran Sayrafian (NIST, USA)

 
Rule-Based Translation of Application-Level QoS Constraints into SDN Configurations for the IoT

Jan Seeger (Technical University Munich & Siemens AG, Germany); Arne Bröring (Siemens AG, Germany); Marc-Oliver Pahl (Technical University of Munich, Germany); Ermin Sakic (Siemens AG, Germany)
In this paper, we propose an approach for the automated translation of application-level requirements regarding the logical workflow and its QoS into a configuration of the underlying network substrate. Our goal is to facilitate the integration of QoS constraints in the development of industrial IoT applications to make them more reliable. We follow an approach based on two semantic models: The first model allows to design the workflow of an IoT application and to express application-level QoS requirements on its interactions. The second model captures the configuration of a network and can be used as input to a north-bound interface of an SDN controller. Finally, we make use of rule-based semantic reasoning to automatically translate from the application requirements into SDN parameters.

 

Machine Learning-assisted Planning and Provisioning for SDN/NFV-enabled Metropolitan Networks

Sebastian Troia (Politecnico di Milano, Italy); David Eugui Martinez and Ignacio Martín (Universidad Carlos III de Madrid, Spain); Ligia Maria Moreira Zorello and Guido Maier (Politecnico di Milano, Italy); José Alberto Hernández (Universidad Carlos III de Madrid, Spain); Oscar González de Dios (Telefonica I+D, Spain); Miquel Garrich, José-Luis Romero-Gázquez and Francisco-Javier Moreno-Muro (Universidad Politécnica de Cartagena, Spain); Pablo Pavon-Marino (Technical University of Cartagena, Spain); Ramon Casellas (Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Spain)
After more than ten years of research and development, Software-Defined Networking (SDN) and Network Function Virtualization (NFV) are finally going mainstream. The fifth generation telecommunication standard (5G) will make use of novel technologies to create increasingly intelligent and autonomous networks. The METRO-HAUL project proposes an advanced SDN/NFV metro-area infrastructure based on an optical backbone interconnecting edge-computing nodes, to support 5G and advanced services. In this work, we present the METRO-HAUL planning tool subsystem that aims to optimize network resources from two different perspectives: off-line network design and on-line resource allocation. Off-line network design algorithms are mainly devoted to capacity planning. Once network infrastructure is in production stages and operational, on-line resource allocation takes into account flows generated by end-user-oriented services that have different requirements in terms of bandwidth, delay, QoS and set of VNFs to be traversed. Through the paper, we describe the components inside the planning tool, which compose a framework that enables intelligent optimization algorithms based on Machine Learning (ML) to assist the control plane in taking strategic decisions. The proposed framework aims to guarantee a fair behavior towards past, current and future requests as network resource allocation decisions are assisted with ML approaches. Additionally, interaction schemes are proposed between the open-source JAVA-based Net2Plan tool, ML libraries and algorithms in Python easing algorithm development and prototyping for rapid interaction with SDN/NFV control and orchestration modules.

 

Network Optimization as a Service with Net2Plan

Miquel Garrich, César San Nicolás Martínez and Francisco-Javier Moreno-Muro (Universidad Politécnica de Cartagena, Spain); Maria Victoria Bueno (UPCT, Spain); Pablo Pavon-Marino (Technical University of Cartagena, Spain)
Carrier-grade telecommunication infrastructure must support an ever-increasing traffic volume and dynamicity in operationally-complex networks characterized by multiple domains, multiple technologies and equipment from multiple vendors. At the same time, the advent of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) technologies create transport ecosystems with unprecedented network control and resource dynamicity capabilities. In this context, network optimization is essential to efficiently employ network resources, relying whenever possible on automated procedures and exploiting open application programmatic interfaces (APIs) inherent of SDN/NFV ecosystems. Automated network optimization procedures on top of network controllers promote a client layer of third-party applications that enable optimization-as-a-service (OaaS) business opportunities. In this paper, we present a network OaaS framework built as an extension of the Net2Plan open-source network planning tool. The proposed framework is based on a classical client-server architecture, and through a REST-based API, holders of network infrastructures (i.e. client role) may request the resolution of a network resource allocation problem through the execution of an algorithmic technique. Thus, third-party players can focus on the development of optimization algorithms (i.e. server role) while potentially providing high-performance computing (HPC) capabilities for their execution. Through the paper, we detail the workflow of the proposed OaaS, illustrate its usage with several use-cases and discuss its applicability in different scenarios.

 

Towards Cross-Layer Optimization of Virtualized Radio Access Networks

Behnam Rouzbehani (Instituto Superior Técnico & GROW – Group for Research on Wireless, INOV – INESC Inovação, Portugal); Vladimir Marbukh (National Institute of Standards and Technology, USA); Kamran Sayrafian (NIST, USA); Luis M. Correia (IST/INESC-ID – University of Lisbon & INESC, Portugal)
This paper proposes an approach to cross-layer optimization of virtualized Radio Access Network resources in future mobile communications. Assuming that the Virtual Network Operators (VNOs) guarantee contracted Service Level Agreements (SLAs) with the users, the proposed approach uses weighted proportional fairness as a basis for allocation of the remaining capacity. This allocation is achieved by a distributed, pricing-based solution to a two-layer convex optimization problem. Through this mechanism, some of the key functionalities of the centralized virtualization platform are transferred to the individual VNOs and users. This allows for a drastic reduction in the complexity of the system management compared to the previously proposed centralized approaches. Therefore, it leads to a much more scalable design for dense network deployments with real-time applications. Another advantage of the proposed distributed cross-layer optimization is the enhanced level of isolation among different VNOs. The proposed approach is evaluated by simulating a scenario with 3 types of VNOs and differentiated SLAs sharing radio resources from an underlying physical heterogeneous network. Results for the 4 types of service classes confirm that given sufficient aggregate capacity, all SLAs are satisfied, the entire aggregated capacity is utilized, and the residual available capacity is shared among the users proportionally fair.

 

A Fingerprint-based Bloom Filter with DeletionCapabilities

Minseok Kwon and Vijay Shankar (Rochester Institute of Technology, USA); Salvatore Pontarelli (National Inter-University Consortium for Telecommunications (CNIT), Italy); Pedro Reviriego (University Carlos III of Madrid, Spain)
One drawback of Bloom filters is the inability to delete items due to hash collisions. Counting Bloom filters address this drawback using counters at the expense of increased filter size. Other alternatives like the Deletable Bloom Filter (DlBF) or the Ternary Bloom Filter (TBF) have been proposed to maintain a small filter size while supporting deletions of elements with some probability. These structures offer different trade-offs between deletability, false positive rate and filter size. In this paper, we propose a new filter called the D-FP (Deletable Fingerprint) Bloom filter that stores two-bit fingerprints, instead of a single bit or a counter, that can indicate whether the item can be deleted. By using the fingerprints, the D-FP Bloom filter provides lower false positive rates than the TBF at low filter occupancies and better deletability than the DlBF thus providing more options to desginers that want to trade-off both parameters. In addition to presenting the D-FP Bloom filter, this paper also presents an analysis of the effects of deletions on the performance of the DlBF and TBF. Finally, as a result of the simulations, we observe that the original analytical estimate for the deletable probability of DlBF tends to overestimate the probability.