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| 2. |
- Aboueata, N., et al.
(author)
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Supervised machine learning techniques for efficient network intrusion detection
- 2019
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In: Proceedings - International Conference on Computer Communications and Networks, ICCCN. - : IEEE. - 9781728118567
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Conference paper (peer-reviewed)abstract
- Cloud computing is gaining significant traction and virtualized data centers are becoming popular as a cost-effective infrastructure in telecommunication industry. Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) are being widely deployed and utilized by end users, including many private as well as public organizations. Despite its wide-spread acceptance, security is still the biggest threat in cloud computing environments. Users of cloud services are under constant fear of data loss, security breaches, information theft and availability issues. Recently, learning-based methods for security applications are gaining popularity in the literature with the advents in machine learning (ML) techniques. In this work, we explore applicability of two well-known machine learning approaches, which are, Artificial Neural Networks (ANN) and Support Vector Machines (SVM), to detect intrusions or anomalous behavior in the cloud environment. We have developed ML models using ANN and SVM techniques and have compared their performances. We have used UNSW-NB-15 dataset to train and test the models. In addition, we have performed feature engineering and parameter tuning to find out optimal set of features with maximum accuracy to reduce the training time and complexity of the ML models. We observe that with proper features set, SVM and ANN techniques have been able to achieve anomaly detection accuracy of 91% and 92% respectively, which is higher compared against that of the one achieved in the literature, with reduced number of features needed to train the models.
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| 3. |
- Agapi, Mesodiakaki, et al.
(author)
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Joint User Association and Backhaul Routing for Green 5G Mesh Millimeter Wave Backhaul Networks
- 2017
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In: Proceedings Of The 20Th Acm International Conference On Modelling, Analysis And Simulation Of Wireless And Mobile Systems. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450351645 ; , s. 179-186
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Conference paper (peer-reviewed)abstract
- With the advance of fifth generation (5G) networks, network density needs to grow significantly in order to meet the required capacity demands. A massive deployment of small cells may lead to a high cost for providing. ber connectivity to each node. Consequently, many small cells are expected to be connected through wireless links to the umbrella eNodeB, leading to a mesh backhaul topology. This backhaul solution will most probably be composed of high capacity point-to-point links, typically operating in the millimeter wave (mmWave) frequency band due to its massive bandwidth availability. In this paper, we propose a mathematical model that jointly solves the user association and backhaul routing problem in the aforementioned context, aiming at the energy efficiency maximization of the network. Our study considers the energy consumption of both the access and backhaul links, while taking into account the capacity constraints of all the nodes as well as the fulfillment of the service-level agreements (SLAs). Due to the high complexity of the optimal solution, we also propose an energy efficient heuristic algorithm (Joint), which solves the discussed joint problem, while inducing low complexity in the system. We numerically evaluate the algorithm performance by comparing it not only with the optimal solution but also with reference approaches under different traffic load scenarios and backhaul parameters. Our results demonstrate that Joint outperforms the state-of-the-art, while being able to find good solutions, close to optimal, in short time.
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| 4. |
- Alizadeh Noghani, Kyoomars, et al.
(author)
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A Generic Framework for Task Offloading in mmWave MEC Backhaul Networks
- 2018
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In: 2018 IEEE Global Communications Conference (GLOBECOM). - : IEEE. - 9781538647271 - 9781538669761 ; , s. 1-7
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Conference paper (peer-reviewed)abstract
- 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.
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| 5. |
- Alizadeh Noghani, Kyoomars, et al.
(author)
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Automating Ethernet VPN deployment in SDN-based Data Centers
- 2017
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In: 2017 Fourth International Conference on Software Defined Systems (SDS).. - : IEEE. - 9781538628553 ; , s. 61-66
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Conference paper (peer-reviewed)abstract
- 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.
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| 6. |
- Alizadeh Noghani, Kyoomars, et al.
(author)
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EVPN/SDN Assisted Live VM Migration between Geo-Distributed Data Centers
- 2018
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In: 4th IEEE Conference on Network Softwarization (NetSoft). - : IEEE. - 9781538646335 ; , s. 105-113
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Conference paper (peer-reviewed)abstract
- 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.
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| 7. |
- Alizadeh Noghani, Kyoomars, et al.
(author)
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On the Cost-Optimality Trade-off for Service Function Chain Reconfiguration
- 2019
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Conference paper (peer-reviewed)abstract
- 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.
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| 8. |
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| 9. |
- Alizadeh Noghani, Kyoomars, et al.
(author)
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SDN Enhanced Ethernet VPN for Data Center Interconnect
- 2017
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In: 2017 IEEE 6th International Conference on Cloud Networking (CloudNet). - : IEEE. - 9781509040261 ; , s. 77-82
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Conference paper (peer-reviewed)abstract
- 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.
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| 10. |
- Alizadeh Noghani, Kyoomars
(author)
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Towards Seamless Live Migration in SDN-Based Data Centers
- 2018
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Licentiate thesis (other academic/artistic)abstract
- 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.
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