Evaluation of Traffic Engineering Routing Models Based on Type of Service in Communication Networks
Abstract
:1. Introduction
- Analyze existing traffic engineering (TE) principles and technologies by comprehensively reviewing current TE specifications, technologies with TE support, and advanced solutions.
- Develop an updated multipath routing model based on type of service (ToS) by formulating new load-balancing conditions, prioritizing packet flows according to their QoS requirements, and defining a mathematical model integrating ToS into traffic engineering for more precise service differentiation.
- Perform a numerical analysis of the proposed TE solutions by conducting numerical experiments on different network topologies using the proposed ToS-based routing models and analyzing two variants of initial data to assess the performance of different load-balancing conditions.
- Evaluate the effectiveness of load-balancing models in handling packet flows with different priorities and compare the results to identify the most suitable model for achieving a differentiated QoS.
- Discuss the implications of the numerical research results regarding their practical application in communication networks.
- Provide recommendations for implementing the most effective load-balancing model in real-world networks to improve service differentiation, based on flow priority.
- Conclude the study by summarizing the research findings, thereby highlighting the significance of the selected model and its contribution to enhancing the QoS in traffic engineering.
2. Overview of Technological and Theoretical Solutions for Traffic Engineering
2.1. Analysis of the Basic Principles of Traffic Engineering in IP Networks under Existing Specifications
- path selection policy;
- resource management (redundancy, bandwidth allocation, network device queue management, etc.) to control losses and delays.
- Constraint-based routing;
- RSVP and RSVP-TE;
- MPLS and generalized MPLS (GMPLS);
- IP performance metrics (IPPM);
- Flow measurement;
- Endpoint congestion management;
- TE extensions to the IGPs (IS-IS and OSPF)
- BGP—link state;
- Path computation element;
- Segment routing (SR);
- Tree engineering for the bit index’s explicit replication;
- Network TE state definition and presentation;
- System management and control interfaces.
2.2. Technologies with TE Support
2.3. Advanced Solutions with TE Support
3. Traffic Engineering Multipath Routing Model, Based on the Type of Service
- The additive form of the first term in Equation (14) emphasizes the selection of routes with the minimum number of hops.
- Including link capacity in the denominator prioritizes selecting links and routes with maximum capacity.
- Introducing packet-flow priority as a power of 10 ensures that higher-priority flows are transmitted via routes with fewer hops than lower-priority flows, particularly when multiple routes possess identical bandwidths but differ in terms of hop counts.
4. Numerical Research of the Proposed ToS Traffic Engineering Solutions in a Communication Network
4.1. Analysis of Initial Data
- TE model, which describes a classical traffic engineering solution based on solving an optimization problem with an optimality criterion (6) and constraints (1)–(4);
- ToS-TE1 model, which is based on solving the optimization problem with an optimality criterion (14) and constraints (1), (2), (7), (8), and (13);
- ToS-TE2 model, which is based on solving the optimization problem with an optimality criterion (14) and constraints (1), (2), (8), (9), and (13);
- ToS-TE3 model, which is based on solving an optimization problem with an optimality criterion (14) and constraints (1), (2), (8), (10), and (13);
- ToS-TE4 model, which is based on solving an optimization problem with an optimality criterion (14) and constraints (1), (2), (8), (11), and (13).
- Path 1: ;
- Path 2: ;
- Path 3: ;
- Path 4: ;
- Path 5: .
4.2. Research Results for the First Variant of the Initial Data
4.3. Research Results for the Second Variant of the Initial Data
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
BGP | Border Gateway Protocol |
DetNet | Deterministic networking |
DiffServ | Differentiated services |
ECMP | Equal-cost multi-path |
EGP | Exterior gateway protocol |
FHRP | First hop redundancy protocol |
GLBP | Gateway load-balancing protocol |
IGP | Interior gateway protocol |
IntServ | Integrated services |
IP | Internet protocol |
IS-IS | Intermediate system to intermediate system |
ML | Machine learning |
MPLS | Multiprotocol label switching |
OSPF | Open shortest path first |
QoS | Quality of service |
RL | Reinforcement learning |
RSVP | Resource reservation protocol |
SDN | Software-defined network |
SR | Segment routing |
TE | Traffic engineering |
ToS | Type of service |
WSN | Wireless sensor network |
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Ref. | Year | Key Contribution | Underlying Approach | Application |
---|---|---|---|---|
[26] | 2023 | Heuristic traffic engineering algorithm SR-ELS that effectively reduces maximum link utilization and improves traffic engineering in segment routing networks. | Heuristics | Segment routing network |
[27] | 2023 | Router activation heuristics for energy-saving ECMP and valiant routing in data center networks effectively reduce energy consumption in computing systems; energy-aware routing. | Heuristics | Data center network |
[28] | 2019 | Model-free traffic engineering framework that adopts multi-agent reinforcement learning for distributed control to minimize end-to-end delay in large-scale multi-hop networks. | Multi-agent learning system and reinforcement learning | Large-scale network |
[29] | 2023 | Fuzzy-based load-balanced opportunistic routing for asynchronous duty-cycled WSNs (FLORA) that achieves better performance in terms of energy consumption, overhead packets, waiting times, packet delivery ratios, and network lifetime compared to other protocols. | Fuzzy logic | WSN |
[30] | 2023 | A self-driving system for intelligent flow routing in programmable networks is proposed. Compared to equal-cost multi-pathing (ECMP), it improves load-sharing and path utilization in programmable networks. | Machine learning traffic prediction | Programmable networks |
[31] | 2021 | The traffic engineering weight adjustment algorithm WASAR is used to optimize routing in a dynamic hybrid SR network. | Optimization | Hybrid segment routing networks |
[32] | 2023 | The traffic engineering approach, combining contrastive learning and reinforcement learning, significantly improves hybrid SDN performance by adapting to fast-changing network flows. | Optimization, contrastive learning, and reinforcement learning | Hybrid SDN |
[33] | 2022 | A destination-based traffic engineering solution, FlexEntry, reduces time complexity and routing update overhead while maintaining good network performance by intelligently selecting critical entries with reinforcement learning and optimizing traffic split ratios with linear programming. | Optimization and reinforcement learning | SDN |
[34] | 2023 | A flexible and disturbance-aware traffic engineering solution, FlexDATE, which can achieve near-optimal performance, can generalize well to unseen traffic scenarios, and will remain resilient to single-link failures. | Optimization, reinforcement learning, and linear programming | SDN |
[35] | 2023 | A scalable learning-based TE, Roracle, which can quickly predict a good routing strategy for a long sequence of future traffic matrices. | Optimization, supervised learning, and linear programming | SDN |
[36] | 2021 | The RACKE+AD system, which combines oblivious routing and average delay, significantly improves the performance and resource utilization of software-defined networks. | Optimization | SDN |
[37] | 2023 | MOLS is a new segment routing-based optimization algorithm that performs similarly to conventional methods but requires fewer policies. This results in faster deployment and the removal of congestion in sub-second time frames. | Midpoint optimization | Backbone networks |
[38] | 2023 | QoS-Aware adaptation traffic engineering solution for multipath routing when the network load balancing is optimized, so that more priority flows are routed through links that are less loaded than those links through which packets of lower-priority flows are transmitted. | Optimization | SDN |
[39] | 2023 | Two modifications of the traffic engineering routing were created, including the linear limitation model (TER-LLM) and traffic engineering limitation (TER-TEL), each considering the main features of packet flow: intensity and priority. | Optimization | Network Edge |
[40] | 2022 | Secure traffic engineering routing model with modified load-balancing conditions, considering network characteristics such as topology, features of the traffic being transmitted, and the link bandwidth and the probabilities of their being compromised. The model allows for the reduction of the links load with a high value of compromise probability, while more traffic will be transmitted over secure links. | Optimization | SDN |
Symbol | Meaning |
---|---|
graph presenting the network structure | |
set of vertices simulating the routers | |
set of arcs representing the links | |
source node | |
destination node | |
) | |
capacity (packets per second, or pps) | |
kth flow average packet rate (pps) | |
kth flow precedence | |
utilization coefficient |
Variant | Link Bandwidth, pps | |||||||
---|---|---|---|---|---|---|---|---|
E1,2 | E2,5 | E1,3 | E3,5 | E1,4 | E4,5 | E3,2 | E3,4 | |
1 | 600 | 600 | 600 | 600 | 600 | 600 | 600 | 600 |
2 | 350 | 700 | 900 | 350 | 350 | 800 | 900 | 900 |
Model Type | End-to-End Delay for Flow IP-Precedence (Milliseconds) | Upper Bound of Link Utilization, α | ||
---|---|---|---|---|
Flow 1 | Flow 2 | Flow 3 | ||
IP Precedence 7 | IP Precedence 3 | IP Precedence 0 | ||
ToS-TE1 | Down to 4.13 ms | Up to 7.85 ms | Up to 8.57 ms | Up to 0.61 |
↓ to 17.4% | ↑ to 57% | ↑ to 71.5% | ↑ to 83% | |
ToS-TE2 | 4.13 ms | 7.85 ms | 8.57 ms | 0.61 |
↓ in 17.4% | ↑ in 57% | ↑ in 71.5% | ↑ in 83% | |
ToS-TE3 | Down to 4.6 ms | Up to 5.1 ms | Up to 5.74 ms | Up to 0.42 |
↓ to 8% | ↑ to 2% | ↑ to 14.8% | ↑ to 26% | |
ToS-TE4 | 4.6 ms | 5.1 ms | 5.74 ms | 0.42 |
↓ in 8% | ↑ in 2% | ↑ in 14.8% | ↑ in 26% |
Model Type | End-to-End Delay for Flow IP-Precedence (Milliseconds) | Upper Bound of Link Utilization, α | ||
---|---|---|---|---|
Flow 1 | Flow 2 | Flow 3 | ||
IP Precedence 7 | IP Precedence 3 | IP Precedence 0 | ||
ToS-TE1 | Down to 5 ms | Up to 12.7 ms | Up to 20.2 ms | Up to 0.835 |
↓ to 17% | ↑ to 2.12 times | ↑ to 3.37 times | ↑ to 2.27 times | |
ToS-TE2 | 5 ms | 12.7 ms | 20.2 ms | 0.835 |
↓ in 17% | ↑ by 2.12 times | ↑ by 3.37 times | ↑ by 2.27 times | |
ToS-TE3 | 5–5.2 ms | 5.56–6 ms | 6.8–8 ms | 0.375–0.5 |
↓ From 13.7 to 17% | ↓ to 17.3% | ↑ From 13 to 33% | ↑ to 33% | |
ToS-TE4 | 5 ms | 6 ms | 8 ms | 0.5 |
↓ in 17% | not changed | ↑ in 33% | ↑ in 33% |
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Barkalov, A.; Lemeshko, O.; Persikov, A.; Yeremenko, O.; Titarenko, L. Evaluation of Traffic Engineering Routing Models Based on Type of Service in Communication Networks. Electronics 2024, 13, 3638. https://doi.org/10.3390/electronics13183638
Barkalov A, Lemeshko O, Persikov A, Yeremenko O, Titarenko L. Evaluation of Traffic Engineering Routing Models Based on Type of Service in Communication Networks. Electronics. 2024; 13(18):3638. https://doi.org/10.3390/electronics13183638
Chicago/Turabian StyleBarkalov, Alexander, Oleksandr Lemeshko, Anatoliy Persikov, Oleksandra Yeremenko, and Larysa Titarenko. 2024. "Evaluation of Traffic Engineering Routing Models Based on Type of Service in Communication Networks" Electronics 13, no. 18: 3638. https://doi.org/10.3390/electronics13183638
APA StyleBarkalov, A., Lemeshko, O., Persikov, A., Yeremenko, O., & Titarenko, L. (2024). Evaluation of Traffic Engineering Routing Models Based on Type of Service in Communication Networks. Electronics, 13(18), 3638. https://doi.org/10.3390/electronics13183638