Chun-Liang Lee

A computer virus spreading model based on resource limitations and interaction costs

Computer viruses are major threats to Internet security and privacy, therefore many researchers are addressing questions linked to virus propagation properties, spreading models, epidemic dynamics, tipping points, and control strategies. We believe that two important factors - resource limitations and costs - are being overlooked in this area due to an overemphasis on power-law connectivity distributions of scale-free networks affecting computer virus epidemic dynamics and tipping points. The study show (a) a significant epidemic tipping point does exists when resource limitations and costs are considered, with the tipping point exhibiting a lower bound; (b) when interaction costs increase or usable resources decrease, epidemic tipping points in scale-free networks grow linearly while density curves decrease linearly; (c) regardless of whether Internet user resources obey delta, uniform, or normal distributions, they retain the same epidemic dynamics and tipping points as long as the average value of those resources remains unchanged across different scale-free networks; (d) it is possible to control the spread of a computer virus in a scale-free network if resources are restricted and if costs associated with infection events are significantly increased through the use of a throttling strategy.

A memory-efficient Huffman decoding algorithm

To reduce the memory size and fasten the process of searching for a symbol in a Huffman tree, we exploit the property of the encoded symbols and propose a memory-efficient data structure to represent the Huffman tree, which uses memory nd bits, where n is the number of source symbols and d is the depth of the Huffman tree. Based on the proposed data structure, we present an O(log n)-time Huffman decoding algorithm. An adaptive version for single-side growing Huffman tree is also addressed. This version could improve the average performance from log n to /spl Sigma//sub i//sup n//spl lceil/i/(h - 1)/spl rceil/ /spl times/ w/sub i/log h//spl Sigma/w/sub i/, where w/sub i/ is the frequency for i/sub th/ symbol and h is a pre-defined value.

An end-to-end flow control approach based on round trip time

Traditional TCP flow control schemes, such as Tahoe and Reno, continuously increase the window size until possible packet loss is detected. These schemes generate congestion unnecessarily by themselves and utilize the buffer space inefficiently. Therefore, Vegas was proposed to accommodate the drawbacks. It uses the measured round-trip time (RTT) to obtain the information required for the flow control. In this work, we propose a flow control scheme, RTT-based TCP, which uses the RTT directly to control the window size adjustment. RTT-based TCP comprises four mechanisms: the modified exponential window increment, the RTT measurement, the queue occupancy estimation, and the quick queue-occupancy reduction. Simulation results show that RTT-based TCP outperforms Vegas in the aspects of throughput, fairness and efficiency of buffer usage.

A New Service Level Agreement Model for Best-Effort Traffics in IP over WDM

In this paper, we propose a new SLA model for best effort IP traffic over WDM networks. This model jointly considers two QoS metrics, workable traffic volume and availability, as SLA parameters. We devise an algorithm of integer programming to minimize operation cost for the incremental traffic.

A scalable hardware solution for packet classification

One of interesting hardware solutions proposed to solve the packet classification problem is bit-vector algorithm. Different from other hardware solutions such as ternary CAM, it efficiently utilizes the memories to achieve an excellent performance in medium size policy database; however, it cannot scale up with the policy number increases. In this paper, we proposed an improved bit-vector algorithm named bit vector condensation, which can be adapted to large policy databases. Experiments showed that our proposed algorithm drastically improves in the storage requirements and search speed as compared to the original algorithm

Fast hierarchical FLP model for VoD server deployment

Video on demand (VoD) is an important service for the next generation networks. In the VoD servers. it is not efficient to preserve all copies of videos. The videos could be divided into the popular ones and less popular ones according to their playing ratios. Since the popular videos are accessed frequently, they should be placed on every server, the cache server. Oppositely, the unpopular one are placed on the central server. In this work, we propose a detailed mathematical model for the hierarchical facility location problem (FLP) encountered in the cache/central server deployment. A linear model for hierarchical FLP is presented and could obtain the feasible solution easily. The experimental results demonstrate the effectiveness.

A simplified approach based on source control for ATM ABR service

The available bit rate (ABR) service is designed to provide efficient support of data traffic in ATM networks. A variety of rate-based flow control approaches have been proposed to support ABR service. Most of these approaches improve both the fairness among active connections and the link utilization by putting more and more complexity into a switch. In this article, we propose a flow control approach in which part of rate-calculation work is moved from switches to end-systems. As a result, the proposed approach reduces the rate-calculation effort in the switch. This may reduce the switch complexity and thus the implementation cost. Furthermore, it mitigates the difficulty for setting the measurement interval in switches, as well as features lower and more stable queue occupancy in the network. Simulation results are presented to demonstrate that the proposed approach achieves better performance than ERICA+, a well-known switch algorithm.

Hardware accelerator for vector quantization by using pruned look-up table

Vector quantization (VQ) is an elementary technique for image compression. However, searching for the nearest codeword in a codebook is time-consuming. The existing schemes focus on software-based implementation to reduce the computation. However, such schemes also incur extra computation and limit the improvement. In this paper, we propose a hardware-based scheme “Pruned Look-Up Table” (PLUT) which could prune possible codewords. The scheme is based on the observation that the minimum one-dimensional distance between the tested vector and its matched codeword is usually small. The observation inspires us to select likely codewords by the one-dimensional distance, which is represented by bitmaps. With the bitmaps containing the positional information to represent the geometric relation within codewords, the hardware implementation can succinctly reduce the required computation of VQ. Simulation results demonstrate that the proposed scheme can eliminate more than 75% computation with an extra storage of 128 Kbytes.

Improving packet classification for multimedia applications in DiffServ architecture

To provide differentiated quality of service, packet classification is important for determining which flow an incoming packet belongs to so as to decide what service quality it should receive. Packet classification is essentially a problem of multidimensional range matching. Tuple space search is a well-known solution based on multiple hash accesses for various filter length combinations. Tuple pruning algorithm is a tuple-based algorithm which is able to achieve good performance in a practical environment; however, its worst-case speed is not guaranteed. We explore the relative property of filters and reorganize the filters through filter conversion. As compared with the tuple pruning algorithm, the proposed scheme can significantly improve the worst-case performance. Experimental results on both real-world and synthetic filter databases show that the worst-case lookup speed of the proposed scheme is 9 to 31 times faster than that of the tuple pruning algorithm.

Fast and Memory Efficient Conflict Detection for Multidimensional Packet Filters

Packet classification plays an important role in supporting advanced network services such as Virtual Private Networks (VPNs), quality-of-service (QoS), and policy-based routing. Routers classify incoming packets into different categories according to pre-defined rules, which are called packet filters. If two or more filters overlap, a conflict may occur and leads to ambiguity in packet classification. In this paper, we propose an algorithm which can efficiently detect and resolve filter conflicts. The proposed algorithm can handle filters with more than two fields, which is more general than algorithms designed for two-dimensional filters.We use the synthetic filter databases generated by ClassBench to evaluate the proposed algorithm. Compared with the bit-vector algorithm, simulation results show that the proposed algorithm can reduce the detection times by over 84% for 10 out of 12 filter databases, and only uses less than 26% of memory space.