Data Traffic Management Research Articles

Immediate traffic flow monitoring and management based on multimodal data in cloud computing

This study uses cloud computing platform to process multi-modal data, and constructs a traffic flow prediction model based on LSTM neural network by integrating data from multiple dimensions such as traffic flow, occupancy and speed. In the process of model construction, we fully consider the hourly characteristics and hysteresis characteristics, and carry out fine scaling and splitting of the data to improve the accuracy and generalization ability of the model. The experimental results show that our model outperforms the baseline on both the training set and the test set, which verifies its effectiveness in traffic flow prediction. By keeping our models in a cloud environment, we provide reliable tools and support for future real-time data analysis and traffic management decisions. This study provides an important reference for the development of traffic management system based on cloud computing, and also provides new ideas and methods for other fields to solve practical problems by using multi-modal data.

FDPHI: Fast Deep Packet Header Inspection for Data Traffic Classification and Management

FDPHI: Fast Deep Packet Header Inspection for Data Traffic Classification and Management

A Reinforcement Learning Based Data Storage and Traffic Management in Information-Centric Data Center Networks

Data Center Networks (DCN), a core infrastructure of cloud computing, place heavy demands on efficient storage and management of massive data. The data storage scheme, which decides how to assign data to nodes for storage, has a significant impact on the performance of the data center. However, most of the existing solutions focus on where to store the data (i.e., the selection of storage node) but have not considered how to store them (i.e., the traffic management such as routing and transmission rate adjustment). By leveraging the Information-Centric Networks (ICN) architecture, this paper tackles the data storage and traffic management issue in Information-Centric Data Center Networks (ICDCN) based on Reinforcement Learning (RL) method, since RL has been developed as a promising solution to address dynamic network issues. We present a global optimization of joint traffic management and data storage and then solve it by the distributed multi-agent Q-learning. In ICDCN, the data is routed based on the data’s name, which achieves better routing scalability by decoupling the data and its physical location. Compared with IP’s stateless forwarding plane, the stateful forwarding information maintained at every node supports adaptively routing and hop-by-hop traffic control by using the Q-learning method. We evaluate our proposal on an NS-3-based simulator, and the results show that the proposed scheme can effectively reduce transmission time and increase throughput while achieving load-balanced among servers.

Towards 5G: A Reinforcement Learning-Based Scheduling Solution for Data Traffic Management

Dominated by delay-sensitive and massive data applications, radio resource management in 5G access networks is expected to satisfy very stringent delay and packet loss requirements. In this context, the packet scheduler plays a central role by allocating user data packets in the frequency domain at each predefined time interval. Standard scheduling rules are known limited in satisfying higher quality of service (QoS) demands when facing unpredictable network conditions and dynamic traffic circumstances. This paper proposes an innovative scheduling framework able to select different scheduling rules according to instantaneous scheduler states in order to minimize the packet delays and packet drop rates for strict QoS requirements applications. To deal with real-time scheduling, the reinforcement learning (RL) principles are used to map the scheduling rules to each state and to learn when to apply each. Additionally, neural networks are used as function approximation to cope with the RL complexity and very large representations of the scheduler state space. Simulation results demonstrate that the proposed framework outperforms the conventional scheduling strategies in terms of delay and packet drop rate requirements.

A Game-Theoretic Analysis of Energy Efficiency and Performance for Cloud Computing in Communication Networks

Continuing growth in cloud-based services and global Internet protocol traffic necessitates performance improvements in energy consumption, network delay, and service availability. Data centers providing cloud services and transport networks have often multiple stakeholders, which makes it difficult to implement centralized traffic management. This paper adopts a game-theoretic approach to data traffic management to obtain a distributed and energy-efficient solution, where each edge router is acting as a strategic player. A multiobjective optimization problem with a priori user-specific preferences is formulated for each player, and a distributed iterative algorithm is proposed to solve the game. The existence of Nash equilibrium of the proposed game is shown, followed by the theoretical convergence analysis of the iterative algorithm. The efficiency loss between the strategic game and the corresponding global optimization method is analyzed to quantify the impact of selfish behavior on the overall system performance. Simulation results show notable challenges for operators to plan, design, and operate a multimedia content network to optimize energy consumption, network delay, and load balance over a diurnal cycle.

Smart Grid Network Optimization: Data-Quality-Aware Volume Reduction

The power industry is in the early stages of a fundamental change, driving the integration of energy, communications, and information technologies into one intelligent utility network, known as the smart grid. This paper investigates data traffic management in smart grid networks, in which huge volumes of data produced by advanced meters cannot be fully delivered to utility data centers due to limited bandwidth. To develop a solution for optimizing traffic flow, we exploit a particular characteristic of this network-power-related applications can benefit from different levels of data quality along the path to the final destination. We thus handle congestion by performing intelligent quality-aware volume reduction of the flows within the network. Our optimization problem is that of computing for each flow the amount of volume reductions in different locations, so as to maximize overall revenue. We address both an off-line scenario, in which all flows are known beforehand, and an online framework, in which new flows can be generated during system operation. For the off-line case, we propose an efficient, near-optimal solution, while for the online case, we derive almost tight polylogarithmic upper and lower bounds on the competitive ratio. We also consider a more restricted dynamic setting, for which we demonstrate how to compute an optimal solution. This paper initiates a rigorous treatment of cross-layer traffic management in the smart grid.

A FRAMEWORK FOR DATA TRAFFIC IN COGNITIVE RADIO NETWORKS USING TRUSTED TOKEN ANALYZER SCHEME

A cognitive radio network is considered as a key technology for future wireless communications. The motivation behind cognitive radio is the effective usage of limited spectrum. Cognitive radio networks are designed to opportunistically exploit the underutilized or unused spectrum bands. Cognitive radio combines sensing, learning, and optimization algorithms to manage and adapt the radio system from the physical layer and up the communication stack. Spectrum Sensing, Spectrum Decision, Spectrum Mobility and Spectrum Sharing, are the four functionalities involved in Cognitive Radio Networks. This paper proposes a framework for data traffic management by considering the QoS requirement of the secondary user and also the primary user activity. The Location information of the primary user in cognitive radio networks can be used to assist the communication among secondary users outside the transmission coverage area of primary users and also for tracking the primary user activities. The objective of the paper is to design a framework which will support the secondary user data transmission while considering the primary user activity and QoS requirement.

A Review on Mobile and Sensor Networks Innovations in Intelligent Transportation Systems

Rapid developments of mobile technologies, data acquisition and big data analytics, and their integration with critical application domains such as transportation systems have the potential to produce more efficient, real-time, intelligent and safe transportation infrastructure. To increase the quality of transportation services, wireless sensor networks, mobile phones, crowd sourcing, RFID and Bluetooth technologies are being used. We surveyed innovations that were transformed from ideas in research labs into commercial systems in practical use. In this paper, we present some innovative mobile technologies, services and platforms that are being used in modern transportation applications including traffic data acquisition, traffic management and control, route optimizations, infrastructure redesign, road safety and enhancing user experience.

Industrial Feedback cum Control System through CAN Protocol

Industrial automation is a sector having vast possibilities for major improvements. The system described in this paper consists of a console master computer (CMC) which will monitor various physical nodes usually found in a large industry. The proposed work analyzes the capability of CAN networking which includes data traffic management. The CMC is designed using MATLAB 7.12; the CAN networking is supported using the Vehicular Network Toolbox. The proposed system using CAN has the advantages of being simple in its design which contributes to the overall low cost. The novelty of the work lies in the low cost approach, and fails safe methodology of CAN communication. The proposed system is capable of sending and receiving signals with the additional benefit of feedback mechanism .The proposed work is implementable in any industry with the cost advantage of CAN interface. The proposed work can be used as a cheaper and robust alternative to native technologies like PLC (Programmable Logic Controller). Moreover, the CAN network system is immune from the electrical interferences.