Gyeongsik Yang

Current network virtualization allows tenants to have their own virtual networks. However, new demands to "program" virtual networks at a finer granularity have arisen as tenants want the ability to provision and control switches and links in their virtual networks. This study proposes a new concept called the p-NIaaS model. The p-NIaaS model enables tenants to program their own packet processing logic and monitor network status from any virtual network infrastructure, which is not possible with

In SDN, as the control channel becomes a performance bottleneck, modeling the control channel traffic is important. Such a model is useful in predicting the control channel traffic for network provisioning. However, previously proposed models are quite limited in that they assume only the forwarding function of a specific controller for their models. To overcome the limitations, first, this paper analyzes the control traffic by seven functions (including forwarding function) of a controller. The

This paper proposes V-Sight, a network monitoring framework for software-defined networking (SDN)-based virtual networks. Network virtualization with SDN (SDN-NV) makes it possible to realize programmable virtual networks; so, the technology can be beneficial to cloud services for tenants. However, to the best of our knowledge, although network monitoring is a vital prerequisite for managing and optimizing virtual networks, it has not been investigated in the context of SDN-NV. Thus, virtual net

Virtualized software-defined networking (SDN) has been drawing increasing attention in data centers. SDN enables the creation of arbitrary and flexible virtual networks. However, given that the physical network is shared among multiple tenants, the throughput of a tenant can interfere with that of the other tenants, and the throughput requirement on each tenant cannot be met. To solve this problem, we propose TALON, a throughput allocation scheme based on traffic load-balancing. TALON allocates

We propose a new concept called "flow rule virtualization" (FlowVirt) for programmable network virtualization (P-NV). In P-NV, network hypervisor is a key component in that it plays a role in creating and managing virtual networks. This paper first reports a critical limitation of network hypervisor - scalability problem, which results in the high consumption of the switch memory, control channel, and CPU cycles: 3.9, 4.7, and 1.7 times higher than host-based network virtualization, respectively

5G networks offer various network services based on software defined networking and network function virtualization. However, certain services are sensitive to link latency which is why it is consistently observed to provide high quality services. Previous studies have proposed two approaches to this task: measuring the latency by probe packets and link-layer discovery protocol (LLDP) packets. However, they have several limitations like flow rule preconfiguration, influence of the control plane

Network virtualization based on software-defined networking (SDN) has become a necessary technology to provide various services in cloud datacenters. Although many network hypervisors have been proposed to support SDN-based network virtualization, their forwarding techniques excessively consume the limited ternary contents addressable memory of OpenFlow-enabled switches. Moreover, they do not consider network reconfiguration after virtual machine migration. In this paper, we propose LiteVisor, t

This paper investigates the performance interference of blockchain services that run on cloud data centers. As the data centers offer shared computing resources to multiple services, the blockchain services can experience performance interference due to the co-located services. We explore the impact of the interference on Fabric performance and develop a new technique to offer performance isolation for Hyperledger Fabric, the most popular blockchain platform. First, we analyze the characteristic

Network virtualization based on SDN has gained attention in cloud networking. However, existing studies have not provided any failover technique in the event of physical link failure. We propose FAVE, which provides seamless failover and bandwidth-aware protection. FAVE carefully allocates backup routes to handle both failure and interference between tenants. Evaluation shows that FAVE is effective. To our knowledge, FAVE is the first attempt to address failover in virtualized SDN environments.

Network virtualization involves creating multiple virtual networks within a physical network, based on the flexible network environment of software-defined networking (SDN). In a virtualized SDN (vSDN), a network hypervisor plays a key role in translating physical network resources into virtual network resources. However, this translation in vSDN incurs considerable overhead in network monitoring that is critical for network management. In this paper, we propose Flo-v to provide network monitori

• Formulate the inconsistent JCT problem using gSLA for the first time. • Design a new JCT increase prediction model and job scheduler for GPU sharing. • Achieve up to 47.3× better gSLA satisfaction and 50× lower gSLA excess ratio. • Improve JCT and GPU efficiency by ∼ 60% and ∼ 44% over existing methods. • Demonstrate TensorShare’s effectiveness in improving gSLA and JCT for unseen jobs. GPU sharing aims to enhance the efficiency of GPU utilization by running distributed deep learning training