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49 results on '"Kuo, Jian-Jhih"'

2. Online Multicast Traffic Engineering for Software-Defined Networks

Author

Chiang, Sheng-Hao, Kuo, Jian-Jhih, Shen, Shan-Hsiang, Yang, De-Nian, and Chen, Wen-Tsuen

Subjects
Computer Science - Networking and Internet Architecture
Abstract

Previous research on SDN traffic engineering mostly focuses on static traffic, whereas dynamic traffic, though more practical, has drawn much less attention. Especially, online SDN multicast that supports IETF dynamic group membership (i.e., any user can join or leave at any time) has not been explored. Different from traditional shortest-path trees (SPT) and graph theoretical Steiner trees (ST), which concentrate on routing one tree at any instant, online SDN multicast traffic engineering is more challenging because it needs to support dynamic group membership and optimize a sequence of correlated trees without the knowledge of future join and leave, whereas the scalability of SDN due to limited TCAM is also crucial. In this paper, therefore, we formulate a new optimization problem, named Online Branch-aware Steiner Tree (OBST), to jointly consider the bandwidth consumption, SDN multicast scalability, and rerouting overhead. We prove that OBST is NP-hard and does not have a $|D_{max}|^{1-\epsilon}$-competitive algorithm for any $\epsilon >0$, where $|D_{max}|$ is the largest group size at any time. We design a $|D_{max}|$-competitive algorithm equipped with the notion of the budget, the deposit, and Reference Tree to achieve the tightest bound. The simulations and implementation on real SDNs with YouTube traffic manifest that the total cost can be reduced by at least 25% compared with SPT and ST, and the computation time is small for massive SDN., Comment: Full version (accepted by INFOCOM 2018)

Published
2017

3. Scalable Rate Control for Traffic Engineering with Aggregated Flows in Software Defined Networks

Author

Kuo, Jian-Jhih, Wang, Chih-Hang, Tsai, Cheng-Da, Yang, De-Nian, and Chen, Wen-Tsuen

Subjects
Computer Science - Networking and Internet Architecture
Abstract

To increase the scalability of Software Defined Networks (SDNs), flow aggregation schemes have been proposed to merge multiple mouse flows into an elephant aggregated flow for traffic engineering. In this paper, we first notice that the user bit-rate requirements of mouse flows are no longer guaranteed in the aggregated flow since the flow rate decided by the TCP allocation is usually different from the desired bit-rate of each user. To address the above issue, we present a novel architecture, named Flexible Flow And Rate Management (F$^2$ARM), to control the rates of only a few flows in order to increase the scalability of SDN, while leaving the uncontrolled flows managed by TCP. We formulate a new optimization problem, named Scalable Per-Flow Rate Control for SDN (SPFRCS), which aims to find a minimum subset of flows as controlled flows but ensure that the flow rates of all uncontrolled flows can still satisfy the minimum required rates by TCP. We prove that SPFRCS is NP-hard and design an efficient algorithm, named Joint Flow Selection and Rate Determination (JFSRD). Simulation results based on real networks manifest that JFSRD performs nearly optimally in small-scale networks, and the number of controlled flows can be effectively reduced by 50% in real networks., Comment: Corrected Version

Published
2017

4. Service Overlay Forest Embedding for Software-Defined Cloud Networks

Author

Kuo, Jian-Jhih, Shen, Shan-Hsiang, Yang, Ming-Hong, Yang, De-Nian, Tsai, Ming-Jer, and Chen, Wen-Tsuen

Subjects
Computer Science - Networking and Internet Architecture
Abstract

Network Function Virtualization (NFV) on Software-Defined Networks (SDN) can effectively optimize the allocation of Virtual Network Functions (VNFs) and the routing of network flows simultaneously. Nevertheless, most previous studies on NFV focus on unicast service chains and thereby are not scalable to support a large number of destinations in multicast. On the other hand, the allocation of VNFs has not been supported in the current SDN multicast routing algorithms. In this paper, therefore, we make the first attempt to tackle a new challenging problem for finding a service forest with multiple service trees, where each tree contains multiple VNFs required by each destination. Specifically, we formulate a new optimization, named Service Overlay Forest (SOF), to minimize the total cost of all allocated VNFs and all multicast trees in the forest. We design a new $3\rho_{ST}$-approximation algorithm to solve the problem, where $\rho_{ST}$ denotes the best approximation ratio of the Steiner Tree problem, and the distributed implementation of the algorithm is also presented. Simulation results on real networks for data centers manifest that the proposed algorithm outperforms the existing ones by over 25%. Moreover, the implementation of an experimental SDN with HP OpenFlow switches indicates that SOF can significantly improve the QoE of the Youtube service., Comment: Technical Report

Published
2017

6. Efficient RRH Activation Management for 5G V2X

Author

Ke, Jing-Wen, Hwang, Ren-Hung, Wang, Chih-Yu, Kuo, Jian-Jhih, and Chen, Wei-Yu

Abstract

Vehicle-to-everything (V2X) communication is one of the key technologies of 5G New Radio to support emerging applications such as autonomous driving. Due to the high density of vehicles, Remote Radio Heads (RRHs) will be deployed as Road Side Units to support V2X. Nevertheless, activation of all RRHs during low-traffic off-peak hours may cause energy wasting. The proper activation of RRH and association between vehicles and RRHs while maintaining the required service quality are the keys to reducing energy consumption. In this work, we first formulate the problem as an Integer Linear Programming optimization problem and prove that the problem is NP-hard. Then, we propose two novel algorithms, referred to as “Least Delete (LD)” and ”Largest-First Rounding with Capacity Constraints (LFRCC).” The simulation results show that the proposed algorithms can achieve significantly better performance compared with existing solutions and are competitive with the optimal solution. Specifically, the LD and LFRCC algorithms can reduce the number of activated RRHs by 86$\%$% and 89$\%$% in low-density scenarios. In high-density scenarios, the LD algorithm can reduce the number of activated RRHs by 90$\%$%. In addition, the solution of LFRCC is larger than that of the optimal solution within 7$\%$% on average.

Published
2024
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8. Intelligent Aerial Relay Deployment for Enhancing Connectivity in Emergency Communications

Author

Nguyen, Van-Linh, Nguyen, Lan-Huong, Kuo, Jian-Jhih, Lin, Po-Ching, and Hwang, Ren-Hung

Abstract

Guaranteeing stable connectivity for emergency communications in rural and remote areas (e.g., to transfer high-quality video for remote surgery) has been challenging for any cellular network generation. With many initiatives proposed to expand the network coverage in 6 G, emergency communications are supposed to be enhanced significantly. For example, with line-of-sight propagation and high mobility advantages, aerial-assisted vehicular networks are expected to be key technologies in 6 G to enable broadband connectivity from the sky to emergency vehicles in rescue missions. However, deploying a group of low-altitude short = ABS, long=Aerial Base Stations (ABSs) to provide connectivity for emergency vehicles remains an open issue due to the challenge of maintaining the trade-off between connectivity quality guarantee and efficient flights. This work presents a hybrid Deep Reinforcement Learning-based scheme with Accumulative Training, namely EVRELAY, to address the problem. Based on a pre-trained signal map, the system can provide the best trajectories for deploying high-capability ABSs. Each DRL agent can dynamically adjust the ABS's movements to serve the maximum number of EVs based on the average signal power, remaining capacity (available data rate and energy), and collision avoidance with surrounding obstacles. The simulation results show that our method can maintain the highest overall connectivity coverage and data rate, 7% and 25% better than the existing methods while maintaining 10% lower energy consumption. The system is particularly efficient in large-scale deployment scenarios with many emergency vehicles departing simultaneously.

Published
2024
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49. GPS-Free, Boundary-Recognition-Free, and Reliable Double-Ruling-Based Information Brokerage Scheme in Wireless Sensor Networks.

Author

Lin, Chia-Hung, Kuo, Jian-Jhih, Liu, Bing-Hong, and Tsai, Ming-Jer

Subjects
*GLOBAL Positioning System, *INFORMATION services, *SCHEME programming language, *WIRELESS sensor networks, *PERFORMANCE evaluation, *INFORMATION retrieval, *EMAIL
Abstract

We study the information brokerage schemes in wireless sensor networks, which allow consumers to obtain data from producers by replicating and retrieving data in a certain set of sensors, and propose a novel information brokerage scheme, termed RDRIB. Unlike existing information brokerage schemes, RDRIB guarantees successful data retrieval without using any boundary detection algorithm and the geographic location information acquired by the global positioning system (GPS). In RDRIB, the double-ruling technique is used to replicate and retrieve the data within a constructed virtual boundary, and simulations show that RDRIB has good performance in terms of the replication memory overhead, the replication message overhead, the retrieval message overhead, the retrieval latency, and the construction message overhead. [ABSTRACT FROM PUBLISHER]

Published
2012
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