SACWOM: Synergistic Adaptive Congestion Window Optimization Mechanism for Self-Clocked Algorithm
Congestion Control (CC) is essential in networked systems, especially in environments with strict delay and throughput requirements. While CC algorithms like Self-Clocked Rate Adaptation for Multimedia (SCReAM) and Bottleneck Bandwidth and Round-trip propagation time (BBR) have shown progress, each...
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IEEE
2025-01-01
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| author | Haider Dhia Zubaydi Ahmed Samir Jagmagji Sandor Molnar |
| author_facet | Haider Dhia Zubaydi Ahmed Samir Jagmagji Sandor Molnar |
| author_sort | Haider Dhia Zubaydi |
| collection | DOAJ |
| description | Congestion Control (CC) is essential in networked systems, especially in environments with strict delay and throughput requirements. While CC algorithms like Self-Clocked Rate Adaptation for Multimedia (SCReAM) and Bottleneck Bandwidth and Round-trip propagation time (BBR) have shown progress, each faces limitations: BBR encounters difficulty in balancing responsiveness and fairness when rapid fluctuations in network conditions arise, which can lead to inefficient performance in shared environments. On the other hand, although SCReAM is designed for multimedia traffic, it struggles to dynamically adapt the congestion window to unpredictable or highly congested networks, leading to inefficient resource utilization. This paper proposes SACWOM, a hybrid congestion window optimization mechanism that combines our novel method with SCReAM’s rate control and BBR’s bandwidth-delay estimation to enhance throughput stability, fairness, and adaptability by dynamically adjusting the congestion window. Extensive simulations demonstrate SACWOM’s significant improvements over SCReAM in managing congestion window and bytes in flight under diverse network conditions. In Phase I, SACWOM achieved up to 11.96-13.27% increase in congestion window and bytes in flight by maintaining higher data flow. Phase II analysis shows up to 20.76-22.64% improvements with optimized configurations. Finally, Phase III, comprising 100 experiments, reveals SACWOM’s dynamic adaptability, achieving up to 50-70% improvements. These results highlight SACWOM as a robust mechanism suitable for various applications across diverse network scenarios. |
| format | Article |
| id | doaj-art-c9c9c17e52244729a3e38cb5d1252ef9 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
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| spelling | doaj-art-c9c9c17e52244729a3e38cb5d1252ef92025-02-06T00:00:23ZengIEEEIEEE Access2169-35362025-01-0113220862211710.1109/ACCESS.2025.353688410858148SACWOM: Synergistic Adaptive Congestion Window Optimization Mechanism for Self-Clocked AlgorithmHaider Dhia Zubaydi0https://orcid.org/0000-0002-4846-0546Ahmed Samir Jagmagji1https://orcid.org/0000-0001-5693-7757Sandor Molnar2https://orcid.org/0000-0002-1601-0815Department of Telecommunications and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, HungaryDepartment of Telecommunications and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, HungaryDepartment of Telecommunications and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, HungaryCongestion Control (CC) is essential in networked systems, especially in environments with strict delay and throughput requirements. While CC algorithms like Self-Clocked Rate Adaptation for Multimedia (SCReAM) and Bottleneck Bandwidth and Round-trip propagation time (BBR) have shown progress, each faces limitations: BBR encounters difficulty in balancing responsiveness and fairness when rapid fluctuations in network conditions arise, which can lead to inefficient performance in shared environments. On the other hand, although SCReAM is designed for multimedia traffic, it struggles to dynamically adapt the congestion window to unpredictable or highly congested networks, leading to inefficient resource utilization. This paper proposes SACWOM, a hybrid congestion window optimization mechanism that combines our novel method with SCReAM’s rate control and BBR’s bandwidth-delay estimation to enhance throughput stability, fairness, and adaptability by dynamically adjusting the congestion window. Extensive simulations demonstrate SACWOM’s significant improvements over SCReAM in managing congestion window and bytes in flight under diverse network conditions. In Phase I, SACWOM achieved up to 11.96-13.27% increase in congestion window and bytes in flight by maintaining higher data flow. Phase II analysis shows up to 20.76-22.64% improvements with optimized configurations. Finally, Phase III, comprising 100 experiments, reveals SACWOM’s dynamic adaptability, achieving up to 50-70% improvements. These results highlight SACWOM as a robust mechanism suitable for various applications across diverse network scenarios.https://ieeexplore.ieee.org/document/10858148/BBRcongestion controlcongestion windowhybridoptimizationrate adaptation |
| spellingShingle | Haider Dhia Zubaydi Ahmed Samir Jagmagji Sandor Molnar SACWOM: Synergistic Adaptive Congestion Window Optimization Mechanism for Self-Clocked Algorithm IEEE Access BBR congestion control congestion window hybrid optimization rate adaptation |
| title | SACWOM: Synergistic Adaptive Congestion Window Optimization Mechanism for Self-Clocked Algorithm |
| title_full | SACWOM: Synergistic Adaptive Congestion Window Optimization Mechanism for Self-Clocked Algorithm |
| title_fullStr | SACWOM: Synergistic Adaptive Congestion Window Optimization Mechanism for Self-Clocked Algorithm |
| title_full_unstemmed | SACWOM: Synergistic Adaptive Congestion Window Optimization Mechanism for Self-Clocked Algorithm |
| title_short | SACWOM: Synergistic Adaptive Congestion Window Optimization Mechanism for Self-Clocked Algorithm |
| title_sort | sacwom synergistic adaptive congestion window optimization mechanism for self clocked algorithm |
| topic | BBR congestion control congestion window hybrid optimization rate adaptation |
| url | https://ieeexplore.ieee.org/document/10858148/ |
| work_keys_str_mv | AT haiderdhiazubaydi sacwomsynergisticadaptivecongestionwindowoptimizationmechanismforselfclockedalgorithm AT ahmedsamirjagmagji sacwomsynergisticadaptivecongestionwindowoptimizationmechanismforselfclockedalgorithm AT sandormolnar sacwomsynergisticadaptivecongestionwindowoptimizationmechanismforselfclockedalgorithm |