Jonghwan Hyun

Monitoring and detecting abnormal behavior in mobile cloud infrastructure

Recently, several mobile services are changing to cloud-based mobile services with richer communications and higher flexibility. We present a new mobile cloud infrastructure that combines mobile devices and cloud services. This new infrastructure provides virtual mobile instances through cloud computing. To commercialize new services with this infrastructure, service providers should be aware of security issues. In this paper, we first define new mobile cloud services through mobile cloud infrastructure and discuss possible security threats through the use of several service scenarios. Then, we propose a methodology and architecture for detecting abnormal behavior through the monitoring of both host and network data. To validate our methodology, we injected malicious programs into our mobile cloud test bed and used a machine learning algorithm to detect the abnormal behavior that arose from these programs.

Scalable failover method for Data Center Networks using OpenFlow

With the emergence of significant amounts of mobile and cloud services, the scale of Data Center Networks (DCNs) has been growing rapidly. A DCN has different network requirements compared to a traditional Internet Protocol (IP) network, and the existing Ethernet/IP style protocols constrain the DCNs scalability and its manageability. In this paper, we present a scalable failover method for large scale DCNs. Because most of the current DCNs are managed in a logically centralized manner with a specialized topology and growth model, we adopt Fat-Tree [1] as the reference DCN topology and design our failover method using an OpenFlow-based approach. Further, to provide scalability, we design our failover algorithm in a local optimal manner, with which only three switches must be modified for handling a single fault, regardless of the size of the target network. We evaluate our failover method in terms of failover time by varying the network size and load balancing capability during failover. The experiment results show that our method scales well, even for a large-scale DCN with more than ten thousands hosts.

Software defined networking-based traffic engineering for data center networks

Todays Data Center Networks (DCNs) contain tens of thousands of hosts with significant bandwidth requirements as the needs for cloud computing, multimedia contents, and big data analysis are increasing. However, the existing DCN technologies accompany the following two problems. First, power consumptions of a DCN is constant regardless of the utilization of network resources. Second, due to a static routing scheme, a few links in DCNs are experiencing congestions while other majority links are being underutilized. To overcome these limitations of the current DCNs, we propose a Software Defined Networking (SDN)-based Traffic Engineering (TE), which consists of optimal topology composition and traffic load balancing. We can reduce the power consumptions of the DCN by turning off links and switches that are not included in the optimal subset topology. To diminish network congestions, the traffic load balancing distributes ever-changing traffic demands over the found optimal subset topology. Simulation results revealed that the proposed SDN-based TE approach can reduce power consumptions of a DCN about 41% and Maximum Link Utilization (MLU) about 60% on average in comparison with a static routing scheme.

A smart home control system based on context and human speech

Traditionally, home appliances are controlled by switches or remote controllers manually. In order to control the appliances, a user have to be near the switches or carry remote controllers all the time. To alleviate this inconvenience, this paper proposes a control system for home appliances using human speech and context information. We present an architecture of the control system and a process to control home appliances composed of three steps: speech detection, speech recognition, and command execution. To validate our system, we show two useful case studies by testing it at POSTECH smart home and UPnP-OSGi simulator.

LISP controller: a centralized LISP management system for ISP networks

Summary As the current Internet architecture is suffering from scalability issues, the network research community has proposed alternative designs for the Internet architecture. Among those solutions that adopt the idea of locator/identifier split paradigm, the locator/identifier separation protocol (LISP) has been considered as the most promising solution because of its incrementally deployable feature. Despite various advantages provided by LISP, many ISPs are still conservative to adopt LISP into their production network because the standard LISP does not fully satisfy ISPs requirements on LISP-enabled services. In this paper, we define ISPs requirements on LISP-enabled commercial services and describe limitations of the standard LISP from an ISPs perspective. Also, we propose LISP controller, a centralized LISP management system. By using LISP controller, we evaluate three ISPs representative LISP use cases: traffic engineering, virtual machine live migration, and vertical handover. The results show that the proposed LISP controller provides centralized management, controllability, and fast map entry update, without any modifications on the standard LISP. LISP controller allows an ISP to control and manage its LISP-enabled services while satisfying ISPs requirements. Copyright

Experience on the development of LISP-enabled services: An ISP perspective

As the current Internet architecture is suffering from scalability issues, the network research community has proposed alternative designs for the Internet architecture. Among those solutions, the Locator/Identifier Separation Protocol (LISP) has been considered as the most promising solution due to its incrementally deployable feature. Despite of various advantages provided by LISP, many ISPs are still conservative to adopt LISP into their production network due to the fact that the standard LISP does not fully satisfy ISPs requirements on LISP-enabled services. In this paper, we propose LISP controller, a centralized LISP management system. By using the LISP controller, we evaluate ISPs three representative LISP use cases: traffic engineering, VM live migration and vertical handover. The results show that the proposed LISP controller allows an ISP to control and manage its LISP-enabled services while satisfying ISPs requirements.

A VoLTE traffic classification method in LTE network

With the emergence of the Long Term Evolution (LTE) technology, LTE based VoIP services are gaining more and more popularity in these days. Voice over LTE (VoLTE) is a VoIP based multimedia service on All-IP based LTE network, which was initially introduced and commercialized by Korea telecommunication companies in 2012. As more and more subscribers use VoLTE service, the VoLTE traffic volume increases, and this may in turn lower down the VoLTE quality; therefore to preserve QoS of VoLTE service, we need to periodically monitor VoLTE traffic among LTE traffic, and perform appropriate actions to LTE network. To be the first step of realizing this goal, in this paper, we propose a VoLTE traffic classification method and its distributed processing architecture to achieve high throughput. For the classification, we analyze SIP/SDP packets for VoLTE service and generate VoLTE tailored SIP packet signature using SIP User-Agent header field. Since the proposed classification method is relying on Deep Packet Inspection (DPI) method, the classification accuracy can be guaranteed, yet it suffers from the poor processing performance. Therefore, we further propose a distributed and parallel traffic processing architecture with the help of HTCondor.

Measurement and analysis of application-level crowd-sourced LTE and LTE-A networks

LTE-Advanced (LTE-A) theoretically can provide better network performance than 4G LTE. Mobile network operators around the world are eager to deploy LTE-A to attract more subscribers. However, is it really making difference to the user experience compared to the existing LTE service? To investigate this question, we collected the cellular network performance log from 111 user smartphones on 3G, LTE, and LTE-A. For in-depth analysis, we also asked for privacy information, such as data plan, subscribed network, monthly payment details, and etc. In addition, we also collected the WiFi network performance log from 83 user smartphones to compare with the cellular ones. By analyzing the collected log, we observed that (i) LTE-A was faster than LTE only for download bandwidth by 14.1%, yet the users pay on average 13.6% more for LTE-A; (ii) the mVoIP traffic was blocked by all carriers when the users exceed their mVoIP quota; (iii) the subscribed data plan showed no discrimination in network performance.

Measuring auto switch between Wi-Fi and mobile data networks in an urban area

To preserve consistent throughput, smartphones are equipped with a network switch feature (handover in heterogeneous networks). Frequent switching is often blamed to be a QoE downgrader in populated areas. In this paper, we measured auto switch occurrences between Wi-Fi and mobile data networks. We deployed an Android monitoring application for 89 participants and collected network status information up to 10 days long. We observed that auto switch occurred on average 2.53 times per hour and RTT decreased as the smartphone preferred to stay in Wi-Fi. Also, 68% of all users connected to Wi-Fi longer than the mobile data networks.

A high performance VoLTE traffic classification method using HTCondor

Voice-over-LTE (VoLTE) is a VoIP-based multimedia service which is provided using All-IP based LTE networks. VoLTE service was first commercialized by Korean telcos in 2012, and now more and more telcos are trying to adopt this technology. With the increased VoLTE service popularity, it is inevitable to have large VoLTE traffic volume (possibly degrading the service quality) and the potential attacks (possibly degrading the service reliability and availability) in the near future. Therefore, in order to avoid such potential issues, we need to perform thorough analysis on VoLTE traffic. As a first step, we propose a VoLTE traffic classification method and its distributed architecture. As the proposed classification method relies on Deep Packet Inspection (DPI) technique, it severely suffers from the large processing time and scalability issues. To overcome these issues, we further propose a distributed architecture for VoLTE traffic classification by adopting a high throughput computing framework - HTCondor. We performed a set of experiments using real-world traces captured from a commercial LTE core network, and have shown that with the proposed architecture, we can achieve up to 23.869 Gbps classification throughput which was almost 35 times faster than the system without distributed processing.