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Maximization of Communication Network Throughput using Dynamic Traffic Allocation Scheme

Md. Arquam, Suchi Kumari

TL;DR

The paper tackles throughput optimization in dynamic multimedia networks under QoS constraints. It proposes Dynamic Traffic Allocation (DTA), a priority-based resource distribution method that maximizes throughput while respecting bandwidth, latency, jitter, and packet loss constraints, formulated as $\max Tr(t)$ subject to $0 \le A_i(t) \le R(t)$ and relevant bounds. The approach defines $A_i(t)$ with proportional allocations across priority, latency sensitivity, demand, and QoS via terms like $A_i(t) \propto \frac{P_i(t)}{\sum_j P_j(t)}$ and $A_i(t) \propto \frac{Lt_i(t)}{\sum_j Lt_j(t)} \cdot \frac{D_i(t)}{\sum_j D_j(t)} \cdot \frac{QoS_i(t)}{\sum_j QoS_j(t)} \cdot \frac{(1-Ld_i(t))}{\sum_j (1-Ld_j(t))}$. Empirical results from simulations on a 50-node network with four traffic types show that DTA outperforms static load balancing, delivering higher throughput for delay-sensitive traffic and faster convergence to a stable operating point, with throughput approaching $1.3\times 10^6$ units.

Abstract

Optimizing network throughput in real-world dynamic systems is critical, especially for diverse and delay-sensitive multimedia data types such as VoIP and video streaming. Traditional routing protocols, which rely on static metrics and single shortest-path algorithms, were unable in managing this complex information. To address these challenges, we propose a novel approach that enhances resource utilization while maintaining Quality of Service (QoS). Our dynamic traffic allocation model prioritizes different data types based on their delay sensitivity and allocates traffic by considering factors such as bandwidth, latency, and network failures. This approach is shown to significantly improve network throughput compared to static load balancing, especially for multimedia applications. Simulation results confirm the effectiveness of this dynamic method in maximizing network throughput and maintaining QoS across various data types.

Maximization of Communication Network Throughput using Dynamic Traffic Allocation Scheme

TL;DR

The paper tackles throughput optimization in dynamic multimedia networks under QoS constraints. It proposes Dynamic Traffic Allocation (DTA), a priority-based resource distribution method that maximizes throughput while respecting bandwidth, latency, jitter, and packet loss constraints, formulated as subject to and relevant bounds. The approach defines with proportional allocations across priority, latency sensitivity, demand, and QoS via terms like and . Empirical results from simulations on a 50-node network with four traffic types show that DTA outperforms static load balancing, delivering higher throughput for delay-sensitive traffic and faster convergence to a stable operating point, with throughput approaching units.

Abstract

Optimizing network throughput in real-world dynamic systems is critical, especially for diverse and delay-sensitive multimedia data types such as VoIP and video streaming. Traditional routing protocols, which rely on static metrics and single shortest-path algorithms, were unable in managing this complex information. To address these challenges, we propose a novel approach that enhances resource utilization while maintaining Quality of Service (QoS). Our dynamic traffic allocation model prioritizes different data types based on their delay sensitivity and allocates traffic by considering factors such as bandwidth, latency, and network failures. This approach is shown to significantly improve network throughput compared to static load balancing, especially for multimedia applications. Simulation results confirm the effectiveness of this dynamic method in maximizing network throughput and maintaining QoS across various data types.
Paper Structure (4 sections, 5 equations, 4 figures, 2 tables)

This paper contains 4 sections, 5 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Proposed Methodology
  3. Results and Analysis
  4. Conclusions and Future Scope

Figures (4)

  • Figure 1: The impact of Dynamics Traffic Allocation Scheme on network throughput for various types of network traffic.
  • Figure 2: Plot showing impact of QoS parameters; (a) bandwidth requirement, (b) varying latency, (c) varying jitter and (d) packet loss rate, on throughput in Dynamics Traffic Allocation Scheme
  • Figure 3: 3-D Plot showing impact of bandwidth and Latency parameters on throughput in Dynamics Traffic Allocation Scheme
  • Figure 4: Plot showing Change in throughput according to (a) bandwidth requirement, (b) varying latency, (c) varying priority and (d) varying time in Dynamics Traffic Allocation Scheme