More Like this

1. P4Guard: Designing P4 Based Firewall

   https://doi.org/10.1109/MILCOM.2018.8599726  

   Datta, Rakesh ; Choi, Sean ; Chowdhary, Anurag ; Park, Younghee ( October 2018 , IEEE Military Communications Conference (MILCOM))

   A virtual firewall based on Network Function Virtualization (NFV) with Software Defined Networking (SDN) provides high scalability and flexibility for low-cost monitoring of legacy networks by dynamically deploying virtual network appliances rather than traditional hardware-based appliances. However, full utilization of virtual firewalls requires efficient management of computer virtualization resources and on-demand placement of virtual firewalls by steering traffic to the correct routing path using an SDN controller. In this paper, we design P4Guard, a software-based configurable firewall based on a high-level domain-specific language to specify packet processing logic using P4. P4Guard is a protocol-independent and platform-agnostic software-based firewall that can be incorporated into software switches that is highly usable and deployable. We evaluate the efficiency of P4Guard in processing traffic, compared to our previous virtual firewall in NFV.
2. Flow-level Dynamic Bandwidth Allocation in SDN-enabled Edge Cloud using Heuristic Reinforcement Learning

   Arslan Qadeer, Myung Lee ( January 2021 , IEEE Conference, on Future Internet of Things and Cloud)

   Edge Cloud (EC) is poised to brace massive machine type communication (mMTC) for 5G and IoT by providing compute and network resources at the edge. Yet, the EC being regionally domestic with a smaller scale, faces the challenges of bandwidth and computational throughput. Resource management techniques are considered necessary to achieve efficient resource allocation objectives. Software Defined Network (SDN) enabled EC architecture is emerging as a potential solution that enables dynamic bandwidth allocation and task scheduling for latency sensitive and diverse mobile applications in the EC environment. This study proposes a novel Heuristic Reinforcement Learning (HRL) based flowlevel dynamic bandwidth allocation framework and validates it through end-to-end implementation using OpenFlow meter feature. OpenFlow meter provides granular control and allows demand-based flow management to meet the diverse QoS requirements germane to IoT traffics. The proposed framework is then evaluated by emulating an EC scenario based on real NSF COSMOS testbed topology at The City College of New York. A specific heuristic reinforcement learning with linear-annealing technique and a pruning principle are proposed and compared with the baseline approach. Our proposed strategy performs consistently in both Mininet and hardware OpenFlow switches based environments. The performance evaluation considers key metrics associated with real-timemore »applications: throughput, end-to-end delay, packet loss rate, and overall system cost for bandwidth allocation. Furthermore, our proposed linear annealing method achieves faster convergence rate and better reward in terms of system cost, and the proposed pruning principle remarkably reduces control traffic in the network.« less
3. Evolving to 6G: Improving the Cellular Core to lower control and data plane latency

   https://doi.org/10.1109/6GNet54646.2022.9830519  

   Jain, Vivek ; Panda, Sourav ; Qi, Shixiong ; Ramakrishnan, K. K. ( July 2022 , 1st International Conference on 6G Networking (6GNet), 2022)

   With the commercialization and deployment of 5G, efforts are beginning to explore the design of the next generation of cellular networks, called 6G. New and constantly evolving use cases continue to place performance demands, especially for low latency communications, as these are still challenges for the 3GPP-specified 5G design, and will have to be met by the 6G design. Therefore, it is helpful to re-examine several aspects of the current cellular network’s design and implementation.Based on our understanding of the 5G cellular network specifications, we explore different implementation options for a dis-aggregated 5G core and their performance implications. To improve the data plane performance, we consider advanced packet classification mechanisms to support fast packet processing in the User Plane Function (UPF), to improve the poor performance and scalability of the current design based on linked lists. Importantly, we implement the UPF function on a SmartNIC for forwarding and tunneling. The SmartNIC provides the fastpath for device traffic, while more complex functions of buffering and processing flows that suffer a miss on the SmartNIC P4 tables are processed by the host-based UPF. Compared to an efficient DPDK-based host UPF, the SmartNIC UPF increases the throughput for 64 Byte packets by almostmore »2×. Furthermore, we lower the packet forwarding latency by 3.75× by using the SmartNIC. In addition, we propose a novel context-level QoS mechanism that dynamically updates the Packet Detection Rule priority and resource allocation of a flow based on the user context. By combining our innovations, we can achieve low latency and high throughput that will help us evolve to the next generation 6G cellular networks.« less
4. OpenStack Network Acceleration Scheme for Datacenter Intelligent Applications

   https://doi.org/10.1109/CLOUD.2018.00146  

   Phan, Linh ; Liu, Kaikai ( July 2018 , 2018 IEEE 11th International Conference on Cloud Computing (CLOUD))

   Cloud virtualization and multi-tenant networking provide Infrastructure as a Service (IaaS) providers a new and innovative way to offer on-demand services to their customers, such as easy provisioning of new applications and better resource efficiency and scalability. However, existing data-intensive intelligent applications require more powerful processors, higher bandwidth and lower-latency networking service. In order to boost the performance of computing and networking services, as well as reduce the overhead of software virtualization, we propose a new data center network design based on OpenStack. Specifically, we map the OpenStack networking services to the hardware switch and utilize hardware-accelerated L2 switch and L3 routing to solve the software limitations, as well as achieve software-like scalability and flexibility. We design our prototype system via the Arista Software-Defined-Networking (SDN) switch and provide an automatic script which abstracts the service layer that decouples OpenStack from the physical network infrastructure, thereby providing vendor-independence. We have evaluated the performance improvement in terms of bandwidth, delay, and system resource utilization using various tools and under various Quality-of-Service (QoS) constraints. Our solution demonstrates improved cloud scaling and network efficiency via only one touch point to control all vendors' devices in the data center.
5. Multi-tenant network acceleration scheme for OpenStack

   https://doi.org/10.1109/UIC-ATC.2017.8397577  

   Phan, Linh Paul ; Liu, Kaikai ( August 2017 , The IEEE Smart City Innovations 2017 (IEEE SCI 2017))

   Cloud visualization and multi-tenant networking provide Infrastructure as a Service (IaaS) provider a new and innovative way to offer the on-demand services to their customers, such as the easy provisioning of new applications and the better resource efficiency and scalability. However, existing data-intensive applications require more powerful processor and computing power, as well as a high bandwidth, low latency and consistent networking service. In order to boost the performance of computing and networking services, as well as reduce the overhead of the software virtualization, we propose a new data center network design based on OpenStack, which is a promising cloud operating system solution. Specifically, we map the OpenStack networking services to the hardware switch, and perform hardware-accelerated L2 switch and L3 routing to solve the software limitations, as well as achieve the software-like scalability and flexibility. We designed our prototype system via the Arista Software-Defined-Networking (SDN) switch, and evaluated the performance improvement in terms of the bandwidth and delay using various tools. Our experimental results demonstrate that our datacenter networking solution achieves higher bandwidth, lower latency, and lower CPU utilization of the host server.