Dynamic Adaptation of Software-defined Networks for IoT Systems: A Search-based Approach
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| Publicado en: | arXiv.org (May 15, 2020), p. n/a |
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| Autor principal: | |
| Otros Autores: | , , , , |
| Publicado: |
Cornell University Library, arXiv.org
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| Acceso en línea: | Citation/Abstract Full text outside of ProQuest |
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|---|---|---|---|
| 001 | 2232976536 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 024 | 7 | |a 10.1145/3387939.3391603 |2 doi | |
| 035 | |a 2232976536 | ||
| 045 | 0 | |b d20200515 | |
| 100 | 1 | |a Shin, Seung Yeob | |
| 245 | 1 | |a Dynamic Adaptation of Software-defined Networks for IoT Systems: A Search-based Approach | |
| 260 | |b Cornell University Library, arXiv.org |c May 15, 2020 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a The concept of Internet of Things (IoT) has led to the development of many complex and critical systems such as smart emergency management systems. IoT-enabled applications typically depend on a communication network for transmitting large volumes of data in unpredictable and changing environments. These networks are prone to congestion when there is a burst in demand, e.g., as an emergency situation is unfolding, and therefore rely on configurable software-defined networks (SDN). In this paper, we propose a dynamic adaptive SDN configuration approach for IoT systems. The approach enables resolving congestion in real time while minimizing network utilization, data transmission delays and adaptation costs. Our approach builds on existing work in dynamic adaptive search-based software engineering (SBSE) to reconfigure an SDN while simultaneously ensuring multiple quality of service criteria. We evaluate our approach on an industrial national emergency management system, which is aimed at detecting disasters and emergencies, and facilitating recovery and rescue operations by providing first responders with a reliable communication infrastructure. Our results indicate that (1) our approach is able to efficiently and effectively adapt an SDN to dynamically resolve congestion, and (2) compared to two baseline data forwarding algorithms that are static and non-adaptive, our approach increases data transmission rate by a factor of at least 3 and decreases data loss by at least 70%. | |
| 653 | |a Adaptive systems | ||
| 653 | |a Congestion | ||
| 653 | |a Internet of Things | ||
| 653 | |a Data loss | ||
| 653 | |a Changing environments | ||
| 653 | |a Emergency management | ||
| 653 | |a Emergency response | ||
| 653 | |a Data transmission | ||
| 653 | |a Rescue operations | ||
| 653 | |a Software engineering | ||
| 653 | |a Adaptive search techniques | ||
| 653 | |a Management systems | ||
| 653 | |a Disaster management | ||
| 653 | |a Adaptive algorithms | ||
| 653 | |a Software | ||
| 653 | |a Adaptation | ||
| 653 | |a Software-defined networking | ||
| 653 | |a Transmission rate (communications) | ||
| 653 | |a Networks | ||
| 653 | |a Configurable programs | ||
| 700 | 1 | |a Nejati, Shiva | |
| 700 | 1 | |a Sabetzadeh, Mehrdad | |
| 700 | 1 | |a Briand, Lionel C | |
| 700 | 1 | |a Arora, Chetan | |
| 700 | 1 | |a Zimmer, Frank | |
| 773 | 0 | |t arXiv.org |g (May 15, 2020), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2232976536/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/1905.12763 |