Byungchul Park

Fine‐grained traffic classification based on functional separation

SUMMARY Current efforts to classify Internet traffic highlight accuracy. Previous studies have focused on the detection of major applications such as P2P and streaming applications. However, these applications can generate various types of traffic which are often considered as minor and ignorant traffic portions. As network applications become more complex, the price paid for not concentrating on minor traffic classes is in reduction of accuracy and completeness. In this context, we propose a fine-grained traffic classification scheme and its detailed method, called functional separation. Our proposal can detect, according to functionalities, different types of traffic generated by a single application and should increase completeness by reducing the amount of undetected traffic. We verify our method with real-world traffic. Our performance comparison against existing DPI-based classification frameworks shows that the fine-grained classification scheme achieves consistently higher accuracyand completeness. Copyright

SAVI Testbed Architecture and Federation

The flexibility of cloud computing has afforded users the ability to develop and deploy fully customizable cloud-based applications and services. Thus far, this flexibility has primarily been constrained to the likes of x86 servers and storage devices. The SAVI application platform testbed was developed to realize the hypothesis that all physical infrastructure resources can be virtualized. Key to this work is a novel control and management framework based on Software-Defined Infrastructure (SDI), a concept which provides a unified programmable interface over heterogeneous infrastructures. In this paper, we present the architecture of the Canadian SAVI national testbed based on the SDI framework. The design of an autonomous SAVI node will be described and the multi-node deployment that comprise the national testbed will be discussed. In addition, the orchestration of applications across the multi-node testbed will be described. Lastly, we will report on the progress of our recent efforts to federate the SAVI testbed with the American GENI national testbed.

Joint NFV placement and routing for multicast service on SDN

Network function visualization (NFV) has emerged as a promising paradigm in networking, where the hardware-based middleboxes are replaced with software-based virtualized entities typically running on the cloud to provide specific functionalities. By deploying NFV, network services become more adaptive and cost-effective. Many multicast services such as real-time multimedia streaming and intrusion detection require appropriate services chaining; however, NFVs placement in the network as well as traffic routing strategy to guarantee that the multicast flows traverse through the services chain before reaching the end user is still an open problem. In this paper, we present an algorithm to solve this problem.

JANUS: Design of a software-defined infrastructure manager and its network control architecture

We present an integrated management architecture based on Software-Defined Infrastructure (SDI), an approach where an SDI Manager controls heterogeneous resources with a global view of the entire infrastructure. By exposing APIs on top of our SDI management system, we create a programmatic cloud environment where users can innovate, creating unique applications and services in a software-defined manner. In this paper we first provide an overview of the SDI manager, dubbed JANUS, that was developed and implemented in the SAVI testbed for application platforms. We focus on advanced software-defined network services that can be provided in this SDI environment. We present requirements that must be met by the SDI management system: Pro-active rule installation; Broadcast-free networking; VN creation and management; scalability and extensibility; and support for legacy networks. We describe the Network Control Module of the JANUS SDI Manager which was designed to meet the specified requirements. A key innovation that promotes scalability and performance is a FlowStore that tracks flows and cache rules in the SDN network.

Scalable Network Traffic Classification Using Distributed Support Vector Machines

Internet traffic has increased dramatically in recent years due to the popularization of the Internet and the appearance of wireless Internet mobile devices such as smart-phones and tablets. The explosive growth of Internet traffic has introduced a practical example that demonstrates the concept of Big Data. Accurate identification and classification of large network traffic data plays an important role in network management including capacity planning, network forensics, QoS and intrusion detection. However, the state-of-the-art solutions, which rely on a dedicated server, are not scalable for analyzing high volume network traffic data. In this paper, we implement a distributed Support Vector Machines (SVMs) framework for classifying network traffic using Hadoop, an open-source distributed computing framework for Big Data processing. We design a global parameter store that maintains the global shared parameters between SVM training nodes. The distributed SVMs have been deployed on a 20 node cluster to analyze real network traffic trace. The results demonstrate that with 19 Mapper nodes the system is around 30% faster than Cloud SVM solution and outperforms the standalone SVM with nearly 9 times faster in training process and 15 times in the classifying process. In addition, the distributed SVMs architecture is designed to analyze large scale datasets. Therefore, it can be used not only for processing network traffic dataset, but also other large scale datasets such as Web data.

Sector: TCAM Space Aware Routing on SDN

In Software Defined Networking (SDN), fine-grained control over individual flow can be achieved by installing appropriate forwarding rules in switches and routers. This allows the network to realize a wide variety of functionalities and objectives. But at the same time, this flexibility and versatility come at the expense of (1) a huge burden on the limited Ternary Content Addressable Memory (TCAM) space, and (2) limited scalability due to the large number of forwarding rules handled by the controller. To address these limitations, we present Sector, a switch memory-aware routing scheme that reduces TCAM space usage without introducing network congestion. We consider static and dynamic versions and propose corresponding solution algorithms. Experiments show our algorithms can reduce TCAM space usage and network control traffic by 20%-80% compared with the benchmark algorithms on different network topologies.

MonArch: Monitoring and analytics in software defined infrastructures

Software-Defined Infrastructure (SDI) provides a unified framework for managing heterogeneous virtualized resources in cloud infrastructures. A key challenge in the realization of SDI is the design of the monitoring system that provides integrated monitoring and measurement functionalities at large scale. In this paper, we present the design of our monitoring system called MonArch that tackles the above challenge. Unlike existing cloud and network monitoring systems, MonArch leverages big-data analytics frameworks to provide both high scalability and unified interfaces for monitoring heterogeneous virtualized resources. The effectiveness of MonArch is demonstrated through a performance evaluation and a use case, where MonArch can reduce the overhead of performing management tasks for virtualized resources.

An architecture of IPTV networks based on network coding

In this paper, we propose a novel architecture for IPTV networks, where network coding is deployed in the backbone networks. As the number of subscribers to IPTV services increases, the size of backbone network is correspondingly grows. Therefore, it is challenging to manage network faults in a centralized manner. Moreover, IPTV services that provide larger video stream sizes in a limited network capacity becomes a problem of current networks. In order to overcome such challenges of IPTV services in the current networks, we propose to deploy network coding technique, which can increase network capacity while improving robustness against network faults.

Enabling L2 network programmability in multi-tenant clouds

With the advent of SDN, efforts on network virtualization have accelerated. Many concepts have been developed to enable the coexistence of multiple logical network domains on a shared networking infrastructure while also ensuring isolation between them. In this context, we envision a multi-tenant cloud environment that grants users the ability to dynamically alter the behaviour of the underlying network, thus achieving a truer sense of Infrastructure as a service. In this paper, we propose a method for providing users in multi-tenant cloud environments open APIs for installing custom flows into the network at layer 2 (L2). Our proposed system ensures tenant isolation while detecting possible flow-level conflicts that can lead to tenancy violations and to problems in end-to-end reachability. We prototyped our system on a nationwide testbed environment, which showed that our proposed method reduces flow-level conflict detection time over existing proposals from ms to μs scale.

Enabling network function virtualization over heterogeneous resources

The economies of scale afforded by cloud computing has been a driving force behind the rapid development and deployment of new cloud-based network applications and services. With the massive growth of IoT devices, we expect a sharp rise in the volume of traffic seen going to and coming from cloud datacenters, which will continue to grow over the next several years. Network Function Virtualization (NFV) is a recent concept which promises to grant network operators the required flexibility to quickly develop and provision new network functions and services in the cloud. As NFV is agnostic to the computing resource, we foresee scenarios where unconventional resources such as FPGAs and GPUs will be of benefit. To this end, we present an architecture based on Software-Defined Infrastructure (SDI) which offers an abstracted control and management interface over virtualized heterogeneous resources in the cloud. Through a unified set of APIs, this architecture enables both application developers and network operators to dynamically deploy and manage new services in the cloud alongside the underlying network that interconnects them, all in a fully software-defined manner. We demonstrate and evaluate an implementation of our NFV-enablement architecture using the SAVI testbed, a multi-tier and SDN-enabled cloud containing virtualized heterogeneous compute resources.