Predictive Forwarding Rule Caching for Latency Reduction in Dynamic SDN
In mission-critical environments such as industrial and military settings, the use of unmanned vehicles is on the rise. These scenarios typically involve a ground control system (GCS) and nodes such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). The GCS and nodes exchange di...
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MDPI AG
2024-12-01
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| author | Doosik Um Hyung-Seok Park Hyunho Ryu Kyung-Joon Park |
| author_facet | Doosik Um Hyung-Seok Park Hyunho Ryu Kyung-Joon Park |
| author_sort | Doosik Um |
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| description | In mission-critical environments such as industrial and military settings, the use of unmanned vehicles is on the rise. These scenarios typically involve a ground control system (GCS) and nodes such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). The GCS and nodes exchange different types of information, including control data that direct unmanned vehicle movements and sensor data that capture real-world environmental conditions. The GCS and nodes communicate wirelessly, leading to loss or delays in control and sensor data. Minimizing these issues is crucial to ensure nodes operate as intended over wireless links. In dynamic networks, distributed path calculation methods lead to increased network traffic, as each node independently exchanges control messages to discover new routes. This heightened traffic results in internal interference, causing communication delays and data loss. In contrast, software-defined networking (SDN) offers a centralized approach by calculating paths for all nodes from a single point, reducing network traffic. However, shifting from a distributed to a centralized approach with SDN does not inherently guarantee faster route creation. The speed of generating new routes remains independent of whether the approach is centralized, so SDN does not always lead to faster results. Therefore, a key challenge remains: determining how to create new routes as quickly as possible even within an SDN framework. This paper introduces a caching technique for forwarding rules based on predicted link states in SDN, which was named the CRIMSON (Cashing Routing Information in Mobile SDN Network) algorithm. The CRIMSON algorithm detects network link state changes caused by node mobility and caches new forwarding rules based on predicted topology changes. We validated that the CRIMSON algorithm consistently reduces end-to-end latency by an average of 88.96% and 59.49% compared to conventional reactive and proactive modes, respectively. |
| format | Article |
| id | doaj-art-2a4672aafc7d435f8d2b2801451e0ec6 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-2a4672aafc7d435f8d2b2801451e0ec62025-01-10T13:21:03ZengMDPI AGSensors1424-82202024-12-0125115510.3390/s25010155Predictive Forwarding Rule Caching for Latency Reduction in Dynamic SDNDoosik Um0Hyung-Seok Park1Hyunho Ryu2Kyung-Joon Park3Department of Interdisciplinary Studies (Artificial Intelligence Major), Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Republic of KoreaDepartment of Electrical Engineering & Computer Science, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Republic of KoreaDepartment of Electrical Engineering & Computer Science, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Republic of KoreaDepartment of Electrical Engineering & Computer Science, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Republic of KoreaIn mission-critical environments such as industrial and military settings, the use of unmanned vehicles is on the rise. These scenarios typically involve a ground control system (GCS) and nodes such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). The GCS and nodes exchange different types of information, including control data that direct unmanned vehicle movements and sensor data that capture real-world environmental conditions. The GCS and nodes communicate wirelessly, leading to loss or delays in control and sensor data. Minimizing these issues is crucial to ensure nodes operate as intended over wireless links. In dynamic networks, distributed path calculation methods lead to increased network traffic, as each node independently exchanges control messages to discover new routes. This heightened traffic results in internal interference, causing communication delays and data loss. In contrast, software-defined networking (SDN) offers a centralized approach by calculating paths for all nodes from a single point, reducing network traffic. However, shifting from a distributed to a centralized approach with SDN does not inherently guarantee faster route creation. The speed of generating new routes remains independent of whether the approach is centralized, so SDN does not always lead to faster results. Therefore, a key challenge remains: determining how to create new routes as quickly as possible even within an SDN framework. This paper introduces a caching technique for forwarding rules based on predicted link states in SDN, which was named the CRIMSON (Cashing Routing Information in Mobile SDN Network) algorithm. The CRIMSON algorithm detects network link state changes caused by node mobility and caches new forwarding rules based on predicted topology changes. We validated that the CRIMSON algorithm consistently reduces end-to-end latency by an average of 88.96% and 59.49% compared to conventional reactive and proactive modes, respectively.https://www.mdpi.com/1424-8220/25/1/155software-defined networkingmobile unmanned swarm nodepredictive forwarding rule cachingdynamic network optimization |
| spellingShingle | Doosik Um Hyung-Seok Park Hyunho Ryu Kyung-Joon Park Predictive Forwarding Rule Caching for Latency Reduction in Dynamic SDN Sensors software-defined networking mobile unmanned swarm node predictive forwarding rule caching dynamic network optimization |
| title | Predictive Forwarding Rule Caching for Latency Reduction in Dynamic SDN |
| title_full | Predictive Forwarding Rule Caching for Latency Reduction in Dynamic SDN |
| title_fullStr | Predictive Forwarding Rule Caching for Latency Reduction in Dynamic SDN |
| title_full_unstemmed | Predictive Forwarding Rule Caching for Latency Reduction in Dynamic SDN |
| title_short | Predictive Forwarding Rule Caching for Latency Reduction in Dynamic SDN |
| title_sort | predictive forwarding rule caching for latency reduction in dynamic sdn |
| topic | software-defined networking mobile unmanned swarm node predictive forwarding rule caching dynamic network optimization |
| url | https://www.mdpi.com/1424-8220/25/1/155 |
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