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  • 1.
    Alizadeh Noghani, Kyoomars
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Towards Seamless Live Migration in SDN-Based Data Centers2018Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Live migration of Virtual Machines (VMs) has significantly improved the flexibility of modern Data Centers (DCs). Ideally, live migration ought to be seamless which in turn raises challenges on how to minimize service disruption and avoid performance degradation. To address these challenges, a comprehensive support from the underlying network is required. However, legacy DC networks fall short to help as they take a reactive approach to live migration procedure. Moreover, the complexity and inflexibility of legacy DC networks make it difficult to deploy, manage, and improve network technologies that DC providers may need to use for migration.

    In this thesis, we explore the application of Software Defined Networking (SDN) paradigm for making live VM migration more seamless. Exploiting the characteristics of SDN such as its centralized view on network states, we contribute to the body of knowledge by enhancing the quality of intra- and inter-DC live migration. Firstly, for intra-DC migration, we provide an SDN-based solution which minimizes the service disruption by employing OpenFlow-based resiliency mechanisms to prepare a DC network for migration proactively. Secondly, we improve the inter-DC live migration by accelerating the network convergence through announcing the migration in the control plane using MP-BGP protocol. Further, our proposed framework resolves the sub-optimal routing problem by conducting the gateway functionality at the SDN controller. Finally, with the ultimate goal of improving the inter-DC migration, we develop an SDN-based framework which automates the deployment, improves the management, enhances the performance, and increases the scalability of interconnections among DCs.

  • 2.
    Alizadeh Noghani, Kyoomars
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Ghazzai, Hakim
    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).
    A Generic Framework for Task Offloading in mmWave MEC Backhaul Networks2018In: 2018 IEEE Global Communications Conference (GLOBECOM), IEEE, 2018, p. 1-7Conference paper (Refereed)
    Abstract [en]

    With the emergence of millimeter-Wave (mmWave) communication technology, the capacity of mobile backhaul networks can be significantly increased. On the other hand, Mobile Edge Computing (MEC) provides an appropriate infrastructure to offload latency-sensitive tasks. However, the amount of resources in MEC servers is typically limited. Therefore, it is important to intelligently manage the MEC task offloading by optimizing the backhaul bandwidth and edge server resource allocation in order to decrease the overall latency of the offloaded tasks. This paper investigates the task allocation problem in MEC environment, where the mmWave technology is used in the backhaul network. We formulate a Mixed Integer NonLinear Programming (MINLP) problem with the goal to minimize the total task serving time. Its objective is to determine an optimized network topology, identify which server is used to process a given offloaded task, find the path of each user task, and determine the allocated bandwidth to each task on mmWave backhaul links. Because the problem is difficult to solve, we develop a two-step approach. First, a Mixed Integer Linear Program (MILP) determining the network topology and the routing paths is optimally solved. Then, the fractions of bandwidth allocated to each user task are optimized by solving a quasi-convex problem. Numerical results illustrate the obtained topology and routing paths for selected scenarios and show that optimizing the bandwidth allocation significantly improves the total serving time, particularly for bandwidth-intensive tasks.

  • 3.
    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.

  • 4.
    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.

  • 5.
    Alizadeh Noghani, Kyoomars
    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).
    SDN Enhanced Ethernet VPN for Data Center Interconnect2017In: 2017 IEEE 6th International Conference on Cloud Networking (CloudNet) / [ed] Maga, D, IEEE, 2017, p. 77-82Conference paper (Refereed)
    Abstract [en]

    Ethernet Virtual Private Network (EVPN) is an emerging technology that addresses the networking challenges presented by geo-distributed Data Centers (DCs). One of the major advantages of EVPN over legacy layer 2 VPN solutions is providing All-Active (A-A) mode of operation so that the traffic can truly be multi-homed on Provider Edge (PE) routers. However, A-A mode of operation introduces new challenges. In the case where the Customer Edge (CE) router is multi-homed to one or more PE routers, it is necessary that only one of the PE routers should forward Broadcast, Unknown unicast, and Multicast (BUM) traffic into the DC. The PE router that assumes the primary role for forwarding BUM traffic to the CE device is called the Designated Forwarder (DF). The proposed solution to select the DF in the EVPN standard is based on a distributed algorithm which has a number of drawbacks such as unfairness and intermittent behavior. In this paper, we introduce a Software-Defined Networking (SDN) based architecture for EVPN support, where the SDN controller interacts with EVPN control plane. We demonstrate how our solution mitigates existing problems for DF selection which leads to improved EVPN performance.

  • 6.
    Alizadeh Noghani, Kyoomars
    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).
    Sankar Gopannan, Prem
    Ericsson, USA.
    EVPN/SDN Assisted Live VM Migration between Geo-Distributed Data Centers2018In: 4th IEEE Conference on Network Softwarization (NetSoft), IEEE, 2018, p. 105-113Conference paper (Refereed)
    Abstract [en]

    Live Virtual Machine (VM) migration has significantly improved the flexibility of modern Data Centers (DC). However, seamless live migration of a VM between geo-distributed DCs faces several challenges due to difficulties in preserving the network configuration after the migration paired with a large network convergence time. Although SDN-based approaches can speed up network convergence time, these techniques have two limitations. First, they typically react to the new topology by installing new flow rules once the migration is finished. Second, because the WAN is typically not under SDN control, they result in sub-optimal routing thus severely degrading the network performance once the VM is attached at the new location.

    In this paper, we identify networking challenges for VM migration across geo-distributed DCs. Based on those observations, we design a novel long-haul VM migration scheme that overcomes those limitations. First, instead of reactively restoring connectivity after the migration, our SDN-based approach proactively restores flows across the WAN towards the new location with the help of EVPN and VXLAN overlay technologies. Second, the SDN controller accelerates the network convergence by announcing the migration to other controllers using MP-BGP control plane messages. Finally, the SDN controller resolves the sub-optimal routing problem that arises as a result of migration implementing a distributed anycast gateway. We implement our approach as extensions to the OpenDaylight controller. Our evaluation shows that our approach outperforms existing approaches in reducing the downtime by 400 ms and increasing the application performance up to 12 times.

  • 7.
    Alizadeh Noghani, Kyoomars
    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).
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    On the Cost-Optimality Trade-off for Service Function Chain Reconfiguration2019Conference paper (Refereed)
    Abstract [en]

    Optimal placement of Virtual Network Functions (VNFs) in virtualized data centers enhances the overall performance of Service Function Chains (SFCs) and decreases the operational costs for mobile network operators. Maintaining an optimal placement of VNFs under changing load requires a dynamic reconfiguration that includes adding or removing VNF instances, changing the resource allocation of VNFs, and re-routing corresponding service flows. However, such reconfiguration may lead to notable service disruptions and impose additional overhead on the VNF infrastructure, especially when reconfiguration entails state or VNF migration. On the other hand, not changing the existing placement may lead to high operational costs. In this paper, we investigate the trade-off between the reconfiguration of SFCs and the optimality of the resulting placement and service flow (re)routing. We model different reconfiguration costs related to the migration of stateful VNFs and solve a joint optimization problem that aims to minimize both the total cost of the VNF placement and the reconfiguration cost necessary for repairing a suboptimal placement. Numerical results show that a small number of reconfiguration operations can significantly reduce the operational cost of the VNF infrastructure; however, too much reconfiguration may not pay off should heavy costs be involved.

  • 8.
    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)
  • 9.
    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

  • 10.
    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.

  • 11.
    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.

  • 12.
    Rezgui, Abdelmounaam
    et al.
    Department of Computer Science and Engineering, New Mexico Tech, Socorro, TX, USA .
    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 helps Big Data to become fault tolerant2018In: Big Data and Software Defined Networks / [ed] Javid Taheri, London: IET Digital Library, 2018, 1, p. 319-336Chapter in book (Refereed)
    Abstract [en]

    SDN networks would have many advantages to be used as fault-tolerant Big Data infrastructures such as programmability and global network view which help monitor and control the network behavior adaptively and efficiently. This chapter studied a number of requirements to provide fault tolerance in networks that Big Data applications perform upon. First, we studied the key requirements to be fault tolerant. The network topology design is crucial to provide resiliency against node or link failure. Second, we mentioned the principle concepts of fault tolerance and elaborated on reactive and proactive methods as two common approaches to deal with the failures in networks. Third, the fault-tolerant mechanisms in SDN architecture and their advantages were elucidated. Consequently, we investigated a number of studies that leverage SDN to provide fault tolerance. Finally, this chapter was concluded by introducing open issues and challenges in SDN architecture to provide a perfect fault-tolerant network.

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