Kien A. Hua

ZIGZAG: an efficient peer-to-peer scheme for media streaming

A peer-to-peer technique called ZIGZAG for single-source media streaming is designed . ZIGZAG allows the media server to distribute content to many clients by organizing them into an appropriate tree rooted at the server. This application-layer multicast tree has a height logarithmic with the number of clients and a node degree bounded by a constant. This helps reduce the number of processing hops on the delivery path to a client while avoiding network bottleneck. Consequently, the end-to-end delay is kept small. Although one could build a tree satisfying such properties easily, an efficient control protocol between the nodes must be in place to maintain the tree under the effects of network dynamics and unpredictable client behaviors. ZIGZAG handles such situations gracefully requiring a constant amortized control overhead. Especially, failure recovery can be done regionally with little impact on the existing clients and mostly no burden on the server.

Skyscraper broadcasting: a new broadcasting scheme for metropolitan video-on-demand systems

We investigate a novel multicast technique, called Skyscraper Broadcasting (SB), for video-on-demand applications. We discuss the data fragmentation technique, the broadcasting strategy, and the client design. We also show the correctness of our technique, and derive mathematical equations to analyze its storage requirement. To assess its performance, we compare it to the latest designs known as Pyramid Broadcasting (PB) and Permutation-Based Pyramid Broadcasting (PPB). Our study indicates that PB offers excellent access latency. However, it requires very large storage space and disk bandwidth at the receiving end. PPB is able to address these problems. However, this is accomplished at the expense of a larger access latency and more complex synchronization. With SB, we are able to achieve the low latency of PB while using only 20% of the buffer space required by PPB.

A peer-to-peer architecture for media streaming

Given that the Internet does not widely support Internet protocol multicast while content-distribution-network technologies are costly, the concept of peer-to-peer could be a promising start for enabling large-scale streaming systems. In our so-called Zigzag approach, we propose a method for clustering peers into a hierarchy called the administrative organization for easy management, and a method for building the multicast tree atop this hierarchy for efficient content transmission. In Zigzag, the multicast tree has a height logarithmic with the number of clients, and a node degree bounded by a constant. This helps reduce the number of processing hops on the delivery path to a client while avoiding network bottlenecks. Consequently, the end-to-end delay is kept small. Although one could build a tree satisfying such properties easily, an efficient control protocol between the nodes must be in place to maintain the tree under the effects of network dynamics. Zigzag handles such situations gracefully, requiring a constant amortized worst-case control overhead. Especially, failure recovery is done regionally with impact on, at most, a constant number of existing clients and with mostly no burden on the server.

P2VoD: providing fault tolerant video-on-demand streaming in peer-to-peer environment

We present a system for video-on-demand streaming in peer-to-peer environment. We start by realizing the major differences between two types of streaming: live and on-demand. These observations lead to a set of problems that need to be solved for a peer-to-peer video-on-demand system. To address these problems, we propose a solution, which includes detail algorithms for building and maintaining an application multicast tree. The novel ideas in this paper are the use of a new caching scheme at clients, and the introduction of generation for better client management. Performance study based on simulation is carried out. The results show that our system outperforms a recently proposed system in a number of important performance metrics.

Decision tree classifier for network intrusion detection with GA-based feature selection

Machine Learning techniques such as Genetic Algorithms and Decision Trees have been applied to the field of intrusion detection for more than a decade. Machine Learning techniques can learn normal and anomalous patterns from training data and generate classifiers that then are used to detect attacks on computer systems. In general, the input data to classifiers is in a high dimension feature space, but not all of features are relevant to the classes to be classified. In this paper, we use a genetic algorithm to select a subset of input features for decision tree classifiers, with a goal of increasing the detection rate and decreasing the false alarm rate in network intrusion detection. We used the KDDCUP 99 data set to train and test the decision tree classifiers. The experiments show that the resulting decision trees can have better performance than those built with all available features.

Optimizing patching performance

Patching has been shown to be cost efficient for video-on- demand systems. Unlike conventional multicast, patching is a dynamic multicast scheme which enables a new request to join an ongoing multicast. Since a multicast can now grow dynamically to serve new users, this approach is more efficiency than traditional multicast. In addition, since a new request can be serviced immediately without having to wait for the next multicast, true video-on-demand can be achieved. In this paper, we introduce the notion of patching window, and present a generalized patching method. We show that existing schemes are special cases with a specific patching window size. We derive a mathematical formula to help determine the optimal size for the patching window. This formula allows us to design the best patching scheme given a workload. The proposed technique is validated using simulations. They show that the analytical results are very accurate. We also provide performance results to demonstrate that the optimal technique outperforms the existing schemes by a significant margin. It is also up to two times better than the best Piggybacking method which provides data sharing by merging the services in progress into a single stream by altering their display rates.

Video delivery technologies for large-scale deployment of multimedia applications

Deployment of a large-scale multimedia streaming application requires an enormous amount of server and network resources. The simplest delivery technique allocates server resources for each specific request. This technique is very expensive and is not scalable to support a very large user community such as the Internet. Hence, the past decade has witnessed tremendous research efforts to facilitate cost-effective, large-scale deployment of multimedia streaming applications. In this paper, we describe three complementary research approaches: server transmission schemes using multicast, streaming strategies with application layer multicast, and proxy caching techniques. We discuss pros and cons of these technologies and provide our observations on current business solutions.

Processing range-monitoring queries on heterogeneous mobile objects

We consider in this paper how to leverage heterogeneous mobile computing capability for efficient processing of real-time range-monitoring queries. In our environment, each mobile object is associated with a resident domain and when an object moves, it monitors its spatial relationship with its resident domain and the monitoring areas inside it. An object reports its location to server whenever its movement affects any query results (i.e., crossing any query boundaries) or it moves out of its resident domain. In the first case, the server updates the affected query results accordingly while in the second case, the server determines a new resident domain for the object. This distributive approach is able to provide accurate query results and real-time monitoring updates with minimal location update and server processing costs. In addition, the new scheme allows a mobile object to negotiate a resident domain based on its computing capability. Thus, a more capable object can have a larger resident domain reducing its chance of having to request a new resident domain because of moving out of it. This feature makes the new approach highly adaptive to the heterogeneity of mobile objects. In our performance study, we compare it with an existing approach using simulation. The study shows that the new technique is many times better in reducing mobile communication and server processing costs.

Multi-level multi-channel air cache designs for broadcasting in a mobile environment

Investigates efficient ways of broadcasting data to mobile users over multiple physical channels, which cannot be coalesced into a lesser number of high-bandwidth channels. We propose the use of an MLMC (multi-level multi-channel) air cache, which can provide mobile users with data based on their popularity factor. We provide a wide range of design considerations for the server which broadcasts over the MLMC cache. We also investigate some novel techniques for a mobile user to access data from the MLMC cache and show the advantages of designing the broadcasting strategy in tandem with the access behavior of the mobile users. Finally, we provide experimental results to compare the techniques we introduce.

Image retrieval based on regions of interest

Query-by-example is the most popular query model in recent content-based image retrieval (CBIR) systems. A typical query image includes relevant objects (e.g., Eiffel Tower), but also irrelevant image areas (including background). The irrelevant areas limit the effectiveness of existing CBIR systems. To overcome this limitation, the system must be able to determine similarity based on relevant regions alone. We call this class of queries region-of-interest (ROI) queries and propose a technique for processing them in a sampling-based matching framework. A new similarity model is presented and an indexing technique for this new environment is proposed. Our experimental results confirm that traditional approaches, such as Local Color Histogram and Correlogram, suffer from the involvement of irrelevant regions. Our method can handle ROI queries and provide significantly better performance. We also assessed the performance of the proposed indexing technique. The results clearly show that our retrieval procedure is effective for large image data sets.