Ted Taekyoung Kwon

A Survey of Green Mobile Networks: Opportunities and Challenges

The explosive development of Information and Communication Technology (ICT) has significantly enlarged both the energy demands and the CO2 emissions, and consequently contributes to make the energy crisis and global warming problems worse. However, as the main force of the ICT field, the mobile networks, are currently focusing on the capacity, variety and stability of the communication services, without paying too much severe concerns on the energy efficiency. The escalating energy costs and environmental concerns have already created an urgent need for more energy-efficient “green” wireless communications. In this paper, we survey and discuss various remarkable techniques toward green mobile networks to date, mainly targeting mobile cellular networks. We also summarize the current research projects related to green mobile networks, along with the taxonomy of energy-efficiency metrics. We finally discuss and elaborate future research opportunities and design challenges for green mobile networks.

A Survey on content-oriented networking for efficient content delivery

As multimedia contents become increasingly dominant and voluminous, the current Internet architecture will reveal its inefficiency in delivering time-sensitive multimedia traffic. To address this issue, there have been studies on contentoriented networking (CON) by decoupling contents from hosts at the networking level. In this article, we present a comprehensive survey on content naming and name-based routing, and discuss further research issues in CON. We also quantitatively compare CON routing proposals, and evaluate the impact of the publish/subscribe paradigm and in-network caching.

AMES-Cloud: A Framework of Adaptive Mobile Video Streaming and Efficient Social Video Sharing in the Clouds

While demands on video traffic over mobile networks have been souring, the wireless link capacity cannot keep up with the traffic demand. The gap between the traffic demand and the link capacity, along with time-varying link conditions, results in poor service quality of video streaming over mobile networks such as long buffering time and intermittent disruptions. Leveraging the cloud computing technology, we propose a new mobile video streaming framework, dubbed AMES-Cloud, which has two main parts: adaptive mobile video streaming (AMoV) and efficient social video sharing (ESoV). AMoV and ESoV construct a private agent to provide video streaming services efficiently for each mobile user. For a given user, AMoV lets her private agent adaptively adjust her streaming flow with a scalable video coding technique based on the feedback of link quality. Likewise, ESoV monitors the social network interactions among mobile users, and their private agents try to prefetch video content in advance. We implement a prototype of the AMES-Cloud framework to demonstrate its performance. It is shown that the private agents in the clouds can effectively provide the adaptive streaming, and perform video sharing (i.e., prefetching) based on the social network analysis.

TOSS: Traffic offloading by social network service-based opportunistic sharing in mobile social networks

The ever increasing traffic demand becomes a serious concern of mobile network operators. To solve this traffic explosion problem, there have been many efforts to offload the traffic from cellular links to direct communications among users. In this paper, we propose the framework of Traffic Offloading assisted by Social network services (SNS) via opportunistic Sharing in mobile social networks, TOSS, to offload SNS-based cellular traffic by user-to-user sharing. First we select a subset of users who are to receive the same content as initial seeds depending on their content spreading impacts in online SNSs and their mobility patterns in offline mobile social networks (MSNs). Then users share the content via opportunistic local connectivity (e.g., Bluetooth, Wi-Fi Direct, Device-to-device in LTE) with each other. The observation of SNS user activities reveals that individual users have distinct access patterns, which allows TOSS to exploit the user-dependent access delay between the content generation time and each users access time for traffic offloading purposes. We model and analyze the traffic offloading and content spreading among users by taking into account various options in linking SNS and MSN trace data. The trace-driven evaluation demonstrates that TOSS can reduce up to 86.5% of the cellular traffic while satisfying the access delay requirements of all users.

OpenSample: A Low-Latency, Sampling-Based Measurement Platform for Commodity SDN

In this paper we propose, implement and evaluate OpenSample: a low-latency, sampling-based network measurement platform targeted at building faster control loops for software-defined networks. OpenSample leverages sFlow packet sampling to provide near-real-time measurements of both network load and individual flows. While OpenSample is useful in any context, it is particularly useful in an SDN environment where a network controller can quickly take action based on the data it provides. Using sampling for network monitoring allows OpenSample to have a 100 millisecond control loop rather than the 1-5 second control loop of prior polling-based approaches. We implement OpenSample in the Floodlight Open Flow controller and evaluate it both in simulation and on a test bed comprised of commodity switches. When used to inform traffic engineering, OpenSample provides up to a 150% throughput improvement over both static equal-cost multi-path routing and a polling-based solution with a one second control loop.

AMVS-NDN: Adaptive mobile video streaming and sharing in wireless named data networking

Recently, mobile traffic (especially video traffic) explosion becomes a serious concern for mobile network operators. While video streaming services become crucial for mobile users, their traffic may often exceed the bandwidth capacity of cellular networks. To address the video traffic problem, we consider a future Internet architecture: Named Data Networking (NDN). In this paper, we design and implement a framework of adaptive mobile video streaming and sharing in the NDN architecture (AMVS-NDN) considering that most of mobile stations have multiple wireless interfaces (e.g., 3G and WiFi). To demonstrate the benefit of NDN, AMVS-NDN has two key functionalities: (1) a mobile station (MS) seeks to use either 3G/4G or WiFi links opportunistically, and (2) MSs can share content directly by exploiting local WiFi connectivities. We implement AMVS-NDN over CCNx, and perform tests in a real testbed consisting of a WiMAX base station and Android phones. Testing with time-varying link conditions in mobile environments reveals that AMVS-NDN achieves the higher video quality and less cellular traffic than other solutions.

Cloud-assisted adaptive video streaming and social-aware video prefetching for mobile users

While the demands of video streaming services over the mobile networks have been souring over these years, the wireless link capacity cannot practically keep up with the growing traffic load. The gap between the traffic demand and the link capacity, along with time-varying link conditions, results in poor service quality of video streaming services over the mobile networks, such as intermittent disruptions and long buffering delays. Leveraging the current cloud computing technology, we propose and discuss a framework to improve the quality of video services for mobile users, which includes two parts: cloud-assisted adaptive video streaming, and social-aware video prefetching. For each active mobile user, a private agent is constructed in the cloud center to adaptively adjust the video quality (bit rate) by the scalable video coding technique based on the feedback of link condition. Meanwhile, the online social network interactions among mobile users are monitored by the cloud-based agents, so that the videos that are shared among users will be effectively prefetched to mobile users in advance. The adaptability of the video streaming and the effectiveness of the social-aware prefetching supported by the cloud computing are evaluated based on a prototype implementation of the framework.

NeTraMark: a network traffic classification benchmark

Recent research on Internet traffic classification has produced a number of approaches for distinguishing types of traffic. However, a rigorous comparison of such proposed algorithms still remains a challenge, since every proposal considers a different benchmark for its experimental evaluation. A lack of clear consensus on an objective and cientific way for comparing results has made researchers uncertain of fundamental as well as relative contributions and limitations of each proposal. In response to the growing necessity for an objective method of comparing traffic classifiers and to shed light on scientifically grounded traffic classification research, we introduce an Internet traffic classification benchmark tool, NeTraMark. Based on six design guidelines (Comparability, Reproducibility, Efficiency, Extensibility, Synergy, and Flexibility/Ease-of-use), NeTraMark is the first Internet traffic lassification benchmark where eleven different state-of-the-art traffic classifiers are integrated. NeTraMark allows researchers and practitioners to easily extend it with new classification algorithms and compare them with other built-in classifiers, in terms of three categories of performance metrics: per-whole-trace flow accuracy, per-application flow accuracy, and computational performance.

Content dissemination by pushing and sharing in mobile cellular networks: An analytical study

The The ever increasing traffic demand is a serious concern of mobile network operators, and the conventional pull-based (or request-based) communication model may not be able to handle this data explosion problem. To reduce the traffic load on cellular links for disseminating content, we propose to push the content to a subset of subscribers via cellular links, and to allow the subscribers to share the content via opportunistic local connectivity (i.e. Wi-Fi ad-hoc mode). We theoretically model and analyze how the content can be disseminated by both pushing via cellular links and sharing via Wi-Fi links, where handovers are modeled based on the multi-compartment model. We also formulate a mathematical framework to optimize the content dissemination, by which the trade-off between the dissemination delay and the energy cost is explored.

Collecting, organizing, and sharing pins in pinterest: interest-driven or social-driven?

Pinterest, a popular social curating service where people collect, organize, and share content (pins in Pinterest), has gained great attention in recent years. Despite the increasing interest in Pinterest, little research has paid attention to how people collect, manage, and share pins in Pinterest. In this paper, to shed insight on such issues, we study the following questions. How do people collect and manage pins by their tastes in Pinterest? What factors do mainly drive people to share their pins in Pinterest? How do the characteristics of users (e.g., gender, popularity, country) or properties of pins (e.g., category, topic) play roles in propagating pins in Pinterest? To answer these questions, we have conducted a measurement study on patterns of pin curating and sharing in Pinterest. By keeping track of all the newly posted and shared pins in each category (e.g., animal, kids, womens fashion) from June 5 to July 18, 2013, we built 350 K pin propagation trees for 3 M users. With the dataset, we investigate: (1) how users collect and curate pins, (2) how users share their pins and why, and (3) how users are related by shared pins of interest. Our key finding is that pin propagation in Pinterest is mostly driven by pins properties like its topic, not by users characteristics like her number of followers. We further show that users in the same community in the interest graph (i.e., representing the relations among users) of Pinterest share pins (i) in the same category with 94% probability and (ii) of the same URL where pins come from with 89% probability. Finally, we explore the implications of our findings for predicting how pins are shared in Pinterest.