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Search Results: 1 - 10 of 6353 matches for " Chung-Horng Lung "
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Multimedia Streaming for Ad Hoc Wireless Mesh Networks Using Network Coding  [PDF]
Basil Saeed, Chung-Horng Lung, Thomas Kunz, Anand Srinivasan
Int'l J. of Communications, Network and System Sciences (IJCNS) , 2013, DOI: 10.4236/ijcns.2013.65024
Abstract:

Over the past years, we have witnessed an explosive growth in the use of multimedia applications such as audio and video streaming with mobile and static devices. Multimedia streaming applications need new approaches to multimedia transmissions to meet the growing volume demand and quality expectations of multimedia traffic. This paper studies network coding which is a promising paradigm that has the potential to improve the performance of networks for multimedia streaming applications in terms of packet delivery ratio (PDR), latency and jitter. This paper examines several network coding protocols for ad hoc wireless mesh networks and compares their performance on multimedia streaming applications with optimized broadcast protocols, e.g., BCast, Simplified Multicast Forwarding (SMF), and Partial Dominant Pruning (PDP). The results show that the performance increases significantly with the Random Linear Network Coding (RLNC) scheme.

Using Hybrid and Diversity-Based Adaptive Ensemble Method for Binary Classification  [PDF]
Xing Fan, Chung-Horng Lung, Samuel A. Ajila
International Journal of Intelligence Science (IJIS) , 2018, DOI: 10.4236/ijis.2018.83003
Abstract: This paper proposes an adaptive and diverse hybrid-based ensemble method to improve the performance of binary classification. The proposed method is a non-linear combination of base models and the application of adaptive selection of the most suitable model for each data instance. Ensemble method, an important machine learning technique uses multiple single models to construct a hybrid model. A hybrid model generally performs better compared to a single individual model. In a given dataset the application of diverse single models trained with different machine learning algorithms will have different capabilities in recognizing patterns in the given training sample. The proposed approach has been validated on Repeat Buyers Prediction dataset and Census Income Prediction dataset. The experiment results indicate up to 18.5% improvement on F1 score for the Repeat Buyers dataset compared to the best individual model. This improvement also indicates that the proposed ensemble method has an exceptional ability of dealing with imbalanced datasets. In addition, the proposed method outperforms two other commonly used ensemble methods (Averaging and Stacking) in terms of improved F1 score. Finally, our results produced a slightly higher AUC score of 0.718 compared to the previous result of AUC score of 0.712 in the Repeat Buyers competition. This roughly 1% increase AUC score in performance is significant considering a very big dataset such as Repeat Buyers.
Pattern-Oriented Reengineering of a Network System
Chung-Horng Lung,Qiang Zhao
Journal of Systemics, Cybernetics and Informatics , 2004,
Abstract: Reengineering is to reorganize and modify existing systems to enhance them or to make them more maintainable. Reengineering is usually necessary as systems evolve due to changes in requirements, technologies, and/or personnel. Design patterns capture recurring structures and dynamics among software participants to facilitate reuse of successful designs. Design patterns are common and well studied in network systems. In this project, we reengineer part of a network system with some design patterns to support future evolution and performance improvement. We start with reverse engineering effort to understand the system and recover its high level architecture. Then we apply concurrent and networked design patterns to restructure the main sub-system. Those patterns include Half-Sync/Half-Async, Monitor Object, and Scoped Locking idiom. The resulting system is more maintainable and has better performance.
Network Coding and Quality of Service for Mobile Ad Hoc Networks  [PDF]
Michael Hay, Basil Saeed, Chung-Horng Lung, Thomas Kunz, Anand Srinivasan
Int'l J. of Communications, Network and System Sciences (IJCNS) , 2014, DOI: 10.4236/ijcns.2014.710042
Abstract: Network Coding is a relatively new forwarding paradigm where intermediate nodes perform a store, code, and forward operation on incoming packets. Traditional forwarding approaches, which employed a store and forward operation, have not been able to approach the limit of the max-flow min-cut throughput wherein sources transmitting information over bottleneck links have to compete for access to these links. With Network Coding, multiple sources are now able to transmit packets over bottleneck links simultaneously, achieving the max-flow min-cut through-put and increasing network capacity. While the majority of the contemporary literature has focused on the performance of Network Coding from a capacity perspective, the aim of this research has taken a new direction focusing on two Quality of Service metrics, e.g., Packet Delivery Ratio (PDR) and Latency, in conjunction with Network Coding protocols in Mobile Ad Hoc Networks (MANETs). Simulations are performed on static and mobile environments to determine a Quality of Service baseline comparison between Network Coding protocols and traditional ad hoc routing protocols. The results show that the Random Linear Network Coding protocol has the lowest Latency and Dynamic Source Routing protocol has the highest PDR in the static scenarios, and show that the Random Linear Network Coding protocol has the best cumulative performance for both PDR and Latency in the mobile scenarios.
Tracking Per-Flow State – Binned Duration Flow Tracking
Brad Whitehead,Chung-Horng Lung,Peter Rabinovitch
Journal of Networks , 2012, DOI: 10.4304/jnw.7.1.37-51
Abstract: Recent advances in network monitoring have increasingly focused on obtaining per-flow information, such as flow state. Tracking the state of network flows opens up a new dimension of information gathering for network operators, allowing previously unattainable data to be captured. This paper presents a time efficient novel method – Binned Duration Flow Tracking (BDFT) – of tracking per-flow state by grouping valid flows into “bins”. BDFT is intended for high-speed routers where CPU time is crucial. BDFT is time efficient by adopting Bloom filters as the primary data structures. Simulation results show that BDFT can achieve over 99% accuracy on traces of real network traffic.
Network Capacity Region of Multi-Queue Multi-Server Queueing System with Time Varying Connectivities
Hassan Halabian,Ioannis Lambadaris,Chung-Horng Lung
Mathematics , 2010,
Abstract: Network capacity region of multi-queue multi-server queueing system with random ON-OFF connectivities and stationary arrival processes is derived in this paper. Specifically, the necessary and sufficient conditions for the stability of the system are derived under general arrival processes with finite first and second moments. In the case of stationary arrival processes, these conditions establish the network capacity region of the system. It is also shown that AS/LCQ (Any Server/Longest Connected Queue) policy stabilizes the system when it is stabilizable. Furthermore, an upper bound for the average queue occupancy is derived for this policy.
Explicit Characterization of Stability Region for Stationary Multi-Queue Multi-Server Systems
Hassan Halabian,Ioannis Lambadaris,Chung-Horng Lung
Mathematics , 2011,
Abstract: In this paper, we characterize the network stability region (capacity region) of multi-queue multi-server (MQMS) queueing systems with stationary channel distribution and stationary arrival processes. The stability region is specified by a finite set of linear inequalities. We first show that the stability region is a polytope characterized by the finite set of its facet defining hyperplanes. We explicitly determine the coefficients of the linear inequalities describing the facet defining hyperplanes of the stability region polytope. We further derive the necessary and sufficient conditions for the stability of the system for general arrival processes with finite first and second moments. For the case of stationary arrival processes, the derived conditions characterize the system stability region. Furthermore, we obtain an upper bound for the average queueing delay of Maximum Weight (MW) server allocation policy which has been shown in the literature to be a throughput optimal policy for MQMS systems. Using a similar approach, we can characterize the stability region for a fluid model MQMS system. However, the stability region of the fluid model system is described by an infinite number of linear inequalities since in this case the stability region is a convex surface. We present an example where we show that in some cases depending on the channel distribution, the stability region can be characterized by a finite set of non-linear inequalities instead of an infinite number of linear inequalities.
Delay Optimal Server Assignment to Symmetric Parallel Queues with Random Connectivities
Hassan Halabian,Ioannis Lambadaris,Chung-Horng Lung
Mathematics , 2011,
Abstract: In this paper, we investigate the problem of assignment of $K$ identical servers to a set of $N$ parallel queues in a time slotted queueing system. The connectivity of each queue to each server is randomly changing with time; each server can serve at most one queue and each queue can be served by at most one server per time slot. Such queueing systems were widely applied in modeling the scheduling (or resource allocation) problem in wireless networks. It has been previously proven that Maximum Weighted Matching (MWM) is a throughput optimal server assignment policy for such queueing systems. In this paper, we prove that for a symmetric system with i.i.d. Bernoulli packet arrivals and connectivities, MWM minimizes, in stochastic ordering sense, a broad range of cost functions of the queue lengths including total queue occupancy (or equivalently average queueing delay).
On the Stability Region of Multi-Queue Multi-Server Queueing Systems with Stationary Channel Distribution
Hassan Halabian,Ioannis Lambadaris,Chung-Horng Lung
Mathematics , 2011,
Abstract: In this paper, we characterize the stability region of multi-queue multi-server (MQMS) queueing systems with stationary channel and packet arrival processes. Toward this, the necessary and sufficient conditions for the stability of the system are derived under general arrival processes with finite first and second moments. We show that when the arrival processes are stationary, the stability region form is a polytope for which we explicitly find the coefficients of the linear inequalities which characterize the stability region polytope.
Optimal Server Assignment in Multi-Server Queueing Systems with Random Connectivities
Hassan Halabian,Ioannis Lambadaris,Yannis Viniotis,Chung-Horng Lung
Mathematics , 2011,
Abstract: We study the problem of assigning $K$ identical servers to a set of $N$ parallel queues in a time-slotted queueing system. The connectivity of each queue to each server is randomly changing with time; each server can serve at most one queue and each queue can be served by at most one server during each time slot. Such a queueing model has been used in addressing resource allocation problems in wireless networks. It has been previously proven that Maximum Weighted Matching (MWM) is a throughput-optimal server assignment policy for such a queueing system. In this paper, we prove that for a system with i.i.d. Bernoulli packet arrivals and connectivities, MWM minimizes, in stochastic ordering sense, a broad range of cost functions of the queue lengths such as total queue occupancy (which implies minimization of average queueing delays). Then, we extend the model by considering imperfect services where it is assumed that the service of a scheduled packet fails randomly with a certain probability. We prove that the same policy is still optimal for the extended model. We finally show that the results are still valid for more general connectivity and arrival processes which follow conditional permutation invariant distributions.
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