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  • 1.
    Alizadeh Noghani, Kyoomars
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Hernandez Benet, Cristian
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Kassler, Andreas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Marotta, Antonio
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Jestin, Patrick
    Ericsson AB, Sweden.
    Srivastava, Vivek V.
    Ericsson AB, Sweden.
    Automating Ethernet VPN deployment in SDN-based Data Centers2017In: 2017 Fourth International Conference on Software Defined Systems (SDS)., IEEE, 2017, p. 61-66Conference paper (Refereed)
    Abstract [en]

    Layer 2 Virtual Private Network (L2VPN) is widely deployed in both service provider networks and enterprises. However, legacy L2VPN solutions have scalability limitations in the context of Data Center (DC) interconnection and networking which require new approaches that address the requirements of service providers for virtual private cloud services. Recently, Ethernet VPN (EVPN) has been proposed to address many of those concerns and vendors started to deploy EVPN based solutions in DC edge routers. However, manual configuration leads to a time-consuming, error-prone configuration and high operational costs. Automating the EVPN deployment from cloud platforms such as OpenStack enhances both the deployment and flexibility of EVPN Instances (EVIs). This paper proposes a Software Defined Network (SDN) based framework that automates the EVPN deployment and management inside SDN-based DCs using OpenStack and OpenDaylight (ODL). We implemented and extended several modules inside ODL controller to manage and interact with EVIs and an interface to OpenStack that allows the deployment and configuration of EVIs. We conclude with scalability analysis of our solution.

  • 2.
    Alizadeh Noghani, Kyoomars
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Hernandez Benet, Cristian
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    SDN helps volume in Big Data2018In: Big Data and Software Defined Networks / [ed] Javid Taheri, London: IET Digital Library, 2018, 1, p. 185-206Chapter in book (Refereed)
    Abstract [en]

    Both Big Data and SDN are described in detail in previous chapters. This chapter investigates how SDN architecture can leverage its unique features to mitigate the challenges of Big Data volume. Accordingly, first, we provide an overview of Big Data volume, its effects on the underlying network, and mention some potential SDN solutions to address the corresponding challenges. Second, we elaborate more on the network-monitoring, traffic-engineering, and fault-tolerant mechanisms which we believe they may help to address the challenges of Big Data volume. Finally, this chapter is concluded with some open issues.

  • 3.
    Bozakov, Zdravko
    et al.
    Dell EMC, Ireland.
    Mangiante, Simone
    Dell EMC, Ireland.
    Hernandez Benet, Cristian
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Brunström, Anna
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). Karlstad University, Faculty of Economic Sciences, Communication and IT, Centre for HumanIT.
    Santos, Ricardo
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Kassler, Andreas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Buckley, Donagh
    Dell EMC, Ireland.
    A NEAT framework for enhanced end-host integration in SDN environments2017In: 2017 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), IEEE, 2017Conference paper (Refereed)
    Abstract [en]

    SDN aims to facilitate the management of increasingly complex, dynamic network environments and optimize the use of the resources available therein with minimal operator intervention. To this end, SDN controllers maintain a global view of the network topology and its state. However, the extraction of information about network flows and other network metrics remains a non-trivial challenge. Network applications exhibit a wide range of properties, posing diverse, often conflicting, demands towards the network. As these requirements are typically not known, controllers must rely on error-prone heuristics to extract them. In this work, we develop a framework which allows applications deployed in an SDN environment to explicitly express their requirements to the network. Conversely, it allows network controllers to deploy policies on end-hosts and to supply applications with information about network paths, salient servers and other relevant metrics. The proposed approach opens the door for fine grained, application-aware resource optimization strategies in SDNs

  • 4.
    Hernandez Benet, Cristian
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Alizadeh Noghani, Kyoomars
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Kassler, Andreas
    Karlstad University, Faculty of Economic Sciences, Communication and IT, Centre for HumanIT. Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Minimizing Live VM Migration Downtime Using OpenFlow based Resiliency Mechanisms2016In: Cloud Networking (Cloudnet), 2016 5th IEEE International Conference on, IEEE, 2016Conference paper (Refereed)
  • 5.
    Hernandez Benet, Cristian
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Alizadeh Noghani, Kyoomars
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Kassler, Andreas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). Karlstad University, Faculty of Economic Sciences, Communication and IT, Centre for HumanIT.
    Dobrijevic, Ognjen
    University of Zagreb, Croatia.
    Jestin, Patrick
    Ericsson AB, Sweden.
    Policy-based routing and load balancing for EVPN-based data center interconnections2017In: Network Function Virtualization and Software Defined Networks (NFV-SDN), 2017 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), IEEE, 2017Conference paper (Refereed)
    Abstract [en]

    The Ethernet VPN (EVPN) technology has emerged as a key solution for the interconnection of geo-distributed Data Centers (DCs) over provider-managed MPLS networks. Such interconnections need to satisfy service-level agreements, which can be achieved by enforcing Traffic Engineering (TE) policies. However, deploying an effective TE policy is challenging and complex. This stems from the fact that network administrators should have a detailed insight into the network status and protocol specifics. Software-Defined Networking (SDN) may facilitate both the policy definition and deployment based on its comprehensive network view and existing integration with DC management systems, such as OpenStack. This paper presents an SDN-based framework for policy-driven DC interconnections that are built around EVPN. The framework is designed to translate routing and other TE policies, which are defined for EVPN instances, into appropriate low-level network actions to meet the policy goals. A generic programming interface allows an SDN controller to load different TE strategies so as to implement the policy, without the need to hard-code it. Moreover, our evaluations illustrate how clients might benefit from specific TE strategies and what is their impact on network performance

  • 6.
    Hernandez Benet, Cristian
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Alizadeh Noghani, Kyoomars
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    SDN implementations and protocols2018In: Big Data and Software Defined Networks / [ed] Javid Taheri, IET Digital Library, 2018, 1, p. 27-48Chapter in book (Refereed)
    Abstract [en]

    This chapter begins by explaining the main SDN concepts with the focus on a SDN controller. It presents the most important aspects to consider when we desire to go from traditional network to a SDN networks. We present an in-depth analysis of the most commonly used and modern SDN controllers and analyse the main features, capabilities and requirements of one of the presented controllers. OpenFlow is the standard leading in the market allowing the management of the forwarding plane devices such as routers or switches. While there are other standards with the same aim, OpenFlow has secured a position in the market and has been expanded rapidly. Therefore, an analysis is presented on a different OpenFlow compatible device for the implementation of an SDN network. This study encompasses both software and hardware solutions along with the scope of implementation or use of these devices. This chapter ends up presenting a description of OpenFlow protocol alternatives, a more detailed description of OpenFlow and its components and other wellknown southbound protocols involved for the management and configuration of the devices.

  • 7.
    Hernandez Benet, Cristian
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Kassler, Andreas
    Karlstad University, Faculty of Economic Sciences, Communication and IT, Department of Computer Science. Karlstad University, Faculty of Economic Sciences, Communication and IT, Centre for HumanIT. Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    FlowDyn: Towards a Dynamic Flowlet Gap Detection using Programmable Data Planes2019Conference paper (Refereed)
    Abstract [en]

    Data center networks offer multiple disjoint paths between Top-of-Rack (ToR) switches to connect server racks providing large bisection bandwidth. An effective load-balancing mechanism is required in order to fully utilize the available capacity of the multiple paths. While packet-based loadbalancing can achieve high utilization, it suffers from reordering. Flow-based load-balancing such as equal-cost multipath routing (ECMP) spreads traffic uniformly across multiple paths leading to frequent hash collisions and suboptimal performance. Finally, flowlet based load-balancing such as CONGA or HULA splits flows into smaller units, which are sent on different paths. Most flowlet based load-balancing schemes depend on a proper static setting of the flowlet gap, which decides when new flowlets are detected. While a too small gap may lead to reordering, a too large gap results in missed load-balancing opportunities. In this paper,weproposeFlowDyn,whichdynamicallyadaptstheflowlet gap to increase the efficiency of the load-balancing schemes while avoiding the reordering problem. Using programmable data planes, FlowDyn uses active probes together with telemetry informationtotrackpathlatencybetweendifferentToRswitches. FlowDyn calculates dynamically a suitable flowlet gap that can be used for flowlet based load-balancing mechanism. We evaluate FlowDyn extensively in simulation, showing that it achieves 3.19 times smaller flow completion time at 10% load and 1.16x at 90% load.

  • 8.
    Hernandez Benet, Cristian
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Kassler, Andreas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Benson, T.
    Brown University, United States.
    Pongracz, G.
    Networking Research - Ericsson, United States.
    MP-HULA: Multipath transport aware load balancing using programmable data planes2018In: NetCompute 2018 - Proceedings of the 2018 Morning Workshop on In-Network Computing, Part of SIGCOMM 2018, Association for Computing Machinery, Inc , 2018, p. 7-13Conference paper (Refereed)
    Abstract [en]

    Datacenter networks ofer a large degree of multipath in order to provide large bisectional bandwidth. The end-to-end performance is determined by the load-balancing strategy which needs to be designed to efectively manage congestion. Consequently, congestion aware load-balancing strategies such as CONGA or HULA have been designed. Recently, more and more applications that are hosted on cloud servers use multipath transport protocols such as MPTCP. However, in the presence of MPTCP, existing load-balancing schemes including ECMP, HULA or CONGA may lead to suboptimal forwarding decisions where multiple MPTCP subfows of one connection are pinned on the same bottleneck link. In this paper, we present MP-HULA, a transport layer multi-path aware load-balancing scheme using Programmable Data Planes. First, instead of tracking congestion information for the best path towards the destination, each MP-HULA switch tracks congestion information for the best-k paths to a destination through the neighbor switches. Second, we design MP-HULA using Programmable Data Planes, where each leaf switch can identify, using P4, which MPTCP subfow belongs to which connection. MP-HULA then load-balances diferent MPTCP subfows of a MPTCP connection on diferent next hops considering congestion state while aggregating bandwidth. Our evaluation shows that MP-HULA with MPTCP outperforms HULA in average flow completion time (2.1x at 50% load, 1.7x at 80% load).

  • 9.
    Hernandez Benet, Cristian
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Kassler, Andreas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Zola, Enrica
    Univ Politecn Cataluna, C Jordi Girona 1-3, Barcelona, Spain..
    Predicting expected TCP throughput using genetic algorithm2016In: Computer Networks, ISSN 1389-1286, E-ISSN 1872-7069, Vol. 108, p. 307-322Article in journal (Refereed)
    Abstract [en]

    Predicting the expected throughput of TCP is important for several aspects such as e.g. determining handover criteria for future multihomed mobile nodes or determining the expected throughput of a given MPTCP subflow for load-balancing reasons. However, this is challenging due to time varying behavior of the underlying network characteristics. In this paper, we present a genetic-algorithm-based prediction model for estimating TCP throughput values. Our approach tries to find the best matching combination of mathematical functions that approximate a given time series that accounts for the TCP throughput samples using genetic algorithm. Based on collected historical datapoints about measured TCP throughput samples, our algorithm estimates expected throughput over time. We evaluate the quality of the prediction using different selection and diversity strategies for creating new chromosomes. Also, we explore the use of different fitness functions in order to evaluate the goodness of a chromosome. The goal is to show how different tuning on the genetic algorithm may have an impact on the prediction. Using extensive simulations over several TCP throughput traces, we find that the genetic algorithm successfully finds reasonable matching mathematical functions that allow to describe the TCP sampled throughput values with good fidelity. We also explore the effectiveness of predicting time series throughput samples for a given prediction horizon and estimate the prediction error and confidence. 

  • 10.
    Hernandez Benet, Cristian
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Nasim, Robayet
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Alizadeh Noghani, Kyoomars
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Kassler, Andreas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    OpenStackEmu - A Cloud Testbed Combining Network Emulation with OpenStack and SDN2017In: Consumer Communications & Networking Conference (CCNC), 2017 14th IEEE Annual, IEEE, 2017, p. 566-568Conference paper (Refereed)
    Abstract [en]

    OpenStack has been widely acknowledged to be one of the most important open source cloud platforms. In order to perform experimentally driven research in the area of cloud and cloud networking, there is however a big gap, because most researchers do not have access to a large cloud deployment and cannot change networking or compute infrastructure in order to test their algorithms and protocols on a large-scale. We developed OpenStackEmu, which is to the best of our knowledge the first attempt that combines OpenStack infrastructure with a Software Defined Networking (SDN) based controller such as OpenDaylight and a large-scale network emulator CORE (Common Open Research Emulator). The OpenStack compute and control nodes are connected to the CORE emulation server using TUN/TAP interfaces that inject the control (e.g. for VM migration) and data (VM-to-VM traffic) packets into a customizable network topology that is emulated using configurable Open vSwitches using CORE emulator. Experimenters can define e.g. fat-tree or distributed data center topologies and study the behavior of real VMs and services in those VMs under different background loads and SDN routing policies. We integrated the data center traffic generator DCT2Gen that allows to generate realistic background traffic based on traces from real data centers. Experimenters can study the performance impact of different VM migration strategies or different routing and load balancing schemes on real VM and application performance using different emulated topologies. We believe that OpenStackEmu is an important tool for both the SDN and OpenStack community in order to evaluate the performance of novel algorithms and protocols in the area of cloud networking.

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