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QoS Support End-to-End Path Selection with Optimized Link Utilization on Multi-domain SDN
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 노병희 | - |
| dc.contributor.author | 이규민 | - |
| dc.date.accessioned | 2022-11-29T03:01:10Z | - |
| dc.date.available | 2022-11-29T03:01:10Z | - |
| dc.date.issued | 2022-08 | - |
| dc.identifier.other | 32299 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/20833 | - |
| dc.description | 학위논문(박사)--아주대학교 일반대학원 :컴퓨터공학과,2022. 8 | - |
| dc.description.tableofcontents | I. Introduction 1 1.1 Motivation 1 1.2 Challenges 3 1.3 Contribution of the Dissertation 4 1.4 Chapter Organization 5 II. Background 6 2.1 Overview of the SDN 6 2.1.1 Comparison of traditional networks and SDN 6 2.1.2 SDN Architecture with OpenFlow 8 2.2 QoS of the SDN 11 2.2.1 QoS Support in Single-domain SDN 11 2.2.2 QoS Support in Multi-domain SDN 13 2.2.3 ML approach for support QoS on SDN 15 III. QoS Support Path Selection for Inter-Domain Flows 22 3.1 Overview 22 3.2 Preliminaries 24 3.3 System Model 27 3.4 Effective delay calculation in a local domain 29 3.5 Path selection of a ow using effective delay 36 3.6 Performance Evaluation 42 3.7 Summary 53 IV. Multi-Agent SAC Flow Utility optimization for Multi-Domain SDN 55 4.1 Overview 55 4.2 Existing Learning approach of the SDN 56 4.2.1 DDPG based learning Agent Design 56 4.2.2 Soft Actor Critic based learning Agent Design 58 4.3 Multi-agent Flow Utility optimization for Multi-Domain SDN 68 4.3.1 Problem Statement 68 4.3.2 Multi-agent SAC learning for ow utility optimization 69 4.4 Performance Evaluation 73 4.5 Summary 80 V. Conclusion 82 References 84 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | QoS Support End-to-End Path Selection with Optimized Link Utilization on Multi-domain SDN | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.department | 일반대학원 컴퓨터공학과 | - |
| dc.date.awarded | 2022. 8 | - |
| dc.description.degree | Doctoral | - |
| dc.identifier.localId | 1254154 | - |
| dc.identifier.uci | I804:41038-000000032299 | - |
| dc.identifier.url | https://dcoll.ajou.ac.kr/dcollection/common/orgView/000000032299 | - |
| dc.subject.keyword | Multi-domain | - |
| dc.subject.keyword | QoS | - |
| dc.subject.keyword | SDN | - |
| dc.description.alternativeAbstract | With the advancement of technology, the recent surge in multimedia applications such as video conferencing, Internet telephony, streaming and online gaming may have different sets of QoS requirements, but network domains may also have different policies and modes of implementing Quality of Service (QoS). In a multi-domain network, providing end-to-end QoS guarantees between all domains where QoS traffic intersects requires strong and satisfactory controls for all members of the network. However, the dynamic change of the network and the domain manager do not disclose information for reasons of security, etc., and there is a need for a method to provide QoS outside the management area due to the imbalance of device configuration information. We introduce existing studies considering single domain and multi-domain and introduced QoS support as a challenge in a multi-domain environment. In addition, we looked at cases where reinforcement learning was applied to SDN QoS. In this dissertation, to provide end-to-end QoS in an SDN environment, stocastic-based end-to-end effective delay is measured, and DAG is applied to determine a flow path, and QoS is applied to SAC-based reinforcement learning as a method to apply QoS in the absence of information. An optimization method was proposed and verified through simulation. Next, we identified problems when applying reinforcement learning in a multi-domain environment to support QoS in limited information in a multi-domain environment. We proposed a multi-agent SAC QoS and flow optimization structure to obtain flow optimization while satisfying QoS requirements by using multi-agent SAC that exchanges state between agents to support QoS only with limited information. Experimental results show that the proposed method can be used as a metric supporting QoS in a multi-domain environment, and can be applied to a QoS-supported learning agent multi-domain environment. | - |
- Appears in Collections:
- Graduate School of Ajou University > Department of Computer Engineering > 4. Theses(Ph.D)
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