양경식 교수

In the blockchain network, the consensus algorithm is used to tolerate node faults with data consistency and integrity, so it is vital in all blockchain services. Previous studies on the consensus algorithm have the following limitations: 1) no resource consumption analysis was done, 2) performance analysis was not comprehensive in terms of blockchain parameters (e.g., number of orderer nodes, number of fault nodes, batch size, payload size), and 3) practical fault scenarios were not evaluated.

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

We introduce TeaVisor, which provides bandwidth isolation guarantee for software-defined networking (SDN)-based network virtualization (NV). SDN-NV provides topology and address virtualization while allowing flexible resource provisioning, control, and monitoring of virtual networks. However, to the best of our knowledge, the bandwidth isolation guarantee, which is essential for providing stable and reliable throughput on network services, is missing in SDN-NV. Without bandwidth isolation guaran

Network virtualization (NV) becomes an essential technology in cloud computing that isolates network flows for tenants. However, because existing NV technologies like overlay do not enable tenants to directly program (i.e., provision, control, and monitor) network resources, software-defined networking (SDN)-based NV (SDN-NV) has been proposed. Despite its great benefits, SDN-NV has been believed to bring considerable overheads due to the network hypervisor (NH). However, to date, there is no de

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

The prediction of the resource consumption for the distributed training of deep learning models is of paramount importance, as it can inform a priori users how long their training would take and also enable users to manage the cost of training. Yet, no such prediction is available for users because the resource consumption itself varies significantly according to "settings" such as GPU types and also by "workloads" like deep learning models. Previous studies have aimed to derive or model such a

This article presents V-Sight, a network monitoring framework for programmable virtual networks in clouds. Network virtualization based on software-defined networking (SDN-NV) in clouds makes it possible to realize programmable virtual networks; consequently, this technology offers many benefits to cloud services for tenants. However, to the best of our knowledge, network monitoring, which is a prerequisite for managing and optimizing virtual networks, has not been investigated in the context of

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

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

Blockchain is increasingly offered as blockchain-as-a-service (BaaS) by cloud service providers. However, configuring BaaS appropriately for optimal performance and reliability resorts to try-and-error. A key challenge is that BaaS is often perceived as a “black-box,” leading to uncertainties in performance and resource provisioning. Previous studies attempted to address this challenge; however, the impacts of both vertical and horizontal scaling remain elusive. To this end, we present machine l

This paper presents "Proactive Congestion Notification" (PCN), a congestion-avoidance technique for distributed deep learning (DDL). DDL is widely used to scale out and accelerate deep neural network training. In DDL, each worker trains a copy of the deep learning model with different training inputs and synchronizes the model gradients at the end of each iteration. However, it is well known that the network communication for synchronizing model parameters is the main bottleneck in DDL. Our key

Predicting resource consumption for the distributed training of deep learning models is of paramount importance, as it can inform a priori users of how long their training would take and enable users to manage the cost of training. Yet, no such prediction is available for users because the resource consumption itself varies significantly according to "settings" such as GPU types and also by "workloads" like deep learning models. Previous studies have attempted to derive or model such a predictio

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

Predicting resource consumption for the distributed training of deep learning models is of paramount importance, as it can inform a priori users of how long their training would take and enable users to manage the cost of training. Yet, no such prediction is available for users because the resource consumption itself varies significantly according to "settings" such as GPU types and also by "workloads" like deep learning models. Previous studies have attempted to derive or model such a predictio

• 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