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  1. NTU Theses and Dissertations Repository
  2. 管理學院
  3. 資訊管理學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71942
完整後設資料紀錄
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dc.contributor.advisor林永松
dc.contributor.authorShih-Ting Kuoen
dc.contributor.author郭士庭zh_TW
dc.date.accessioned2021-06-17T06:15:55Z-
dc.date.available2021-12-26
dc.date.copyright2018-12-26
dc.date.issued2018
dc.date.submitted2018-08-12
dc.identifier.citation[1] A. Checko, H.L. Christiansen, Y. Yan, L. Scolari, G. Kardaras, M.S. Berger, and L. Dittmann, “Cloud RAN for Mobile Networks—A Technology Overview,” IEEE Communications Surveys & Tutorials, Vol. 17, No. 1, Firstquarter, 2015, pp. 405–426.
[2] M. Chen, Y. Zhang, L. Hu, T. Taleb, and Z. Sheng, “Cloud-based Wireless Network: Virtualized, Reconfigurable, Smart Wireless Network to Enable 5G Technologies,” Mobile Networks and Applications, Vol. 20, No. 6, December, 2015, pp. 704–712.
[3] M. Peng, C. Wang, V. Lau, and H.V. Poor, “Fronthaul-constrained Cloud Radio Access Networks: Insights and Challenges,” IEEE Wireless Communications, Vol. 22, No. 2, pp. 152 – 160, April 2015.
[4] P. Rost, C.J. Bernardos, A.D. Domenico, M.D. Girolamo, M. Lalam, A. Maeder, D. Sabella, and D. Wübben, “Cloud Technologies for Flexible 5G Radio Access Networks,” IEEE Communications Magazine, Vol. 52, No. 5, pp. 68- 76, May 2014.
[5] G. Fettweis and E. Zimmermann, ‘‘ICT Energy Consumption-trends and Challenges,’’ in Proc. 11th Int. Symp. Wireless Pers. Multimedia Commun. (WPMC), Lapland, Finland, 2008, pp. 8–11.
[6] J. Luo, Q. Chen, and L. Tang, “Reducing Power Consumption by Joint Sleeping Strategy and Power Control in Delay-Aware C-RAN,” IEEE Access, Vol. 6, pp.14655–14667, April 2018.
[7] F. Tao, C. Li, T. W. Liao, and Y. Laili, 'BGM-BLA: A New Algo-rithm for Dynamic Migration of Virtual Machines in Cloud Computing,' IEEE Transactions on Services Computing, Vol. 9, No. 6, pp. 910-925, December 2016.
[8] Q. Wu, F. Ishikawa, Q. Zhu, and Y. Xia, 'Energy and Migration Cost-aware Dynamic Virtual Machine Consolidation in Heterogeneous Cloud Datacenters,' IEEE Transactions on Services Computing, Doi: 10.1109, October 2016.
[9] H. Yuan, J. Bi, W. Tan, and B. H. Li, 'CAWSAC: Cost-Aware Workload Scheduling and Admission Control for Distributed Cloud Data Centers,' IEEE Transactions on Automation Science and Engineering, Vol. 13, No. 2, pp. 976-985, April 2016.
[10] A. Bashar, 'BN-Based Approach for Predictive Admission Control of Cloud Services,' in the 2017 IEEE 7th International Advance Computing Conference (IACC), Hyderabad, 2017, pp. 59-64.
[11] J. Wang, Z. Lv, Z. Ma, L. Sun, and Y. Sheng, “i-Net: New Network Architecture for 5G Networks,” IEEE Communications Magazine, Vol. 53, No. 6, pp. 44–51, Jun. 2015.
[12] P. Rost, C.J. Bernardos, A.D. Domenico, M.D. Girolamo, M. Lalam, A. Maeder, D. Sabella, and D. Wübben, “Cloud Technologies for Flexible 5G Radio Access Networks,” IEEE Communications Magazine, Vol. 52, No. 5, pp. 68- 76, May 2014.
[13] V. Suryaprakash, P. Rost, and G. Fettweis, “Are Heterogeneous Cloud-Based Radio Access Networks Cost Effective?,” IEEE Journal on Selected Areas in Communications, Vol. 33, No. 10, Oct., 2015, pp. 2239–2251.
[14] R. Dechter, Constraint processing, Morgan Kaufmann, 2003.
[15] A. Silberschatz, P. B. Galvin, and G. Gagne, Operating System Principles, 7th Edition, John Wiley & Sons Inc., New York, 2005.
[16] H. K. Tang, P. Ramanathan, and K. Compton, “Combining Hard Periodic and Soft Aperiodic Real-Time Task Scheduling on Heterogeneous Compute Resources,” in the Proc. ICPP, Sept., 2011, pp. 753-762.
[17] Earliest Deadline First, Accessed March 12, 2018, [Online]. Available: https://en.wikipedia.org/wiki/Earliest_deadline_first_scheduling
[18] K. Cho, C. Tsai, and C. Yang, 'An Efficient Power-Saving Scheduling Algorithm,' Journal of Internet Technology, Vol. 99, No. 99, May. 2015, pp. 1-9.
[19] I. Song, J.Y. Kim, and S.B. Choi, 'A Performance-Oriented Resource Allocation Algorithm with Insertion and Duplication for IaaS Clouds,' Journal of Internet Technology, Vol. 99, No. 99, May. 2015, pp. 1-10.
[20] M. R. Garey and D. S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, W. H. Freeman & Co., 1979.
[21] Proportionally Fair, Accessed March 15, 2018, [Online]. Available: https://en.wikipedia.org/wiki/Proportionally_fair.
[22] T. Ding, Q.F. Hao, B. Zhang, T.G. Zhang, and L.T. Huai, “Scheduling Policy Optimization in Kernel-Based Virtual Machine,” in the Proc. CiSE, Wuhan, China, Dec., 2010, pp. 1-4.
[23] H. Yuan, J. Bi, W. Tan and B.H. Li, 'CAWSAC: Cost-Aware Workload Scheduling and Admission Control for Distributed Cloud Data Centers,' IEEE Transactions on Automation Science and Engineering, Vol. 13, No. 2, pp. 976-985, April 2016.
[24] A. Bashar, 'BN-Based Approach for Predictive Admission Control of Cloud Services,' in the 2017 IEEE 7th International Advance Computing Conference (IACC), Hyderabad, 2017, pp. 59-64.
[25] Y.J. Chiang, Y.C. Ouyang, and C.H. Hsu, “An Optimal Cost-Efficient Resource Provisioning for Multi-Servers Cloud Computing,” in the Proc. of the 2013 International Conference on Cloud Computing and Big Data (CloudCom-Asia 2013), Dec. 2013.
[26] N. Agata , A. Agata, and K. Nishimura, “A Design Algorithm for Ring Topology Centralized-Radio-Access-Network,” in the Proc. of the 17th International Conference on Optical Network Design and Modeling (ONDM 2013), April 2013.
[27] C. Ran and S. Wang, “Resource Allocation in Heterogeneous Cloud Radio Access Networks: A Workload Balancing Perspective,” in the Proc. of the 2015 IEEE Global Communications Conference (GLOBECOM 2015), Dec. 2015.
[28] B. Guan, X. Huang, G. Wu, C. Chan, M. Udayan, and C. Neelam, “A Pooling Prototype for the LTE MAC Layer Based on a GPP Platform,” in the Proc. of the 2015 IEEE Global Communications Conference (GLOBECOM 2015), Dec. 2015.
[29] A. Gupta and R.K. Jha, “A Survey of 5G Network: Architecture and Emerging Technologies,” IEEE ACCESS, Vol. 3, pp. 1206–1232, Aug. 2015.
[30] G. Lu, C. Liu, L. Li, and Q. Yuan, “A Dynamic Allocation Algorithm for Physical Carrier Resource in BBU Pool of Virtualized Wireless Network,” in the Proc. of the 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC 2015), Sept. 2015.
[31] L. Liu, J. Xu, H. Yu and X. Wei, 'Joint Admission Control and Provisioning for Virtual Machines,' in the 2015 IEEE International Conference on Communications (ICC), London, 2015, pp. 332-337.
[32] S. Muppala, G. Chen, and X. Zhou, “Multi-tier Service Differentiation by Coordinated Learning-based Resource Provisioning and Admission Control,” J. Parallel Distrib. Comput., Vol. 74, No. 5, pp. 2351–2364, May 2014.
[33] H. Khojasteh, J. Mišić and V. B. Mišić, “Task Filtering as a Task Admission Control Policy in Cloud Server Pools,” in the Proc. IWCMC, Dubrovnik, Croatia, August, 2015, pp. 727–732.
[34] Discrete-time Markov Chain, Accessed June 10, 2018, [Online]. Available: https://en.wikipedia.org/wiki/Markov_chain#Discrete-time_Markov_chain
[35] Markov Decision Process, Accessed June 10, 2018, [Online]. Available: https://en.wikipedia.org/wiki/Markov_decision_process
[36] Markov Decision Theory, Accessed June 30, 2018, [Online]. Available: https://cours.etsmtl.ca/mgr816/markov_apB.pdf
[37] Harrison, Peter G.; Patel, Naresh M. Performance Modelling of Communication Networks and Computer Architectures. Addison-Wesley. ISBN 0-201-54419-9.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71942-
dc.description.abstract在第五代行動通訊系統的環境下,即時的資源配置及管理方法對於網路運營商的重要性逐漸增加,5G系統架構將以雲端無線電接取網路為核心,其將基地台拆分為基頻單元及無線寬頻頭端,而其中負責基頻運算單元將以虛擬化技術將其集中至雲端無線電接取網路之伺服器群管理,本研究透過有效率的管理機制,包括基頻單元的移轉與伺服器開關機達成伺服器群利用率的提升及省電之功效,著重在於5G環境背景下各種類型任務的允入控制,可針對不同的任務類型及在不同的系統狀態下,同時考量系統的運算及操作成本及包含了伺服器電源能耗成本及基頻單元移轉成本,使用馬可夫決策過程尋求最佳的允入決策;綜合允入控制、內部資源配置及基頻單元操作移轉等成本因素,目標以找到長期時間下的系統淨獲利率最佳化。此研究模擬實驗了多個不同情境,包含不同的任務到達率及服務率、不同數量的伺服器及任務類型、不同的決策組合所能獲得的淨獲利和系統的作業成本相互影響決策的選擇至最後淨獲利的變化。zh_TW
dc.description.abstractIn fifth-generation (5G) radio access networks environment, resources allocation and management are more than more important for the network operator. The 5G system architecture have been proposed as a cloud architecture to provide a common connected resource pool for various applications and requirements. In this regard, that is a challenge to efficiently and effectively manage radio resources and allocate perspectives on the rapidly changing traffic load. Admission control mechanism is a key factor influencing the system performance with limited budget of the resource pool. Furthermore, the performance of system was also affected by several factors, such as rapidly increasing data traffic, power consumption of system, service reject penalty and operating cost of service. The problem is formulated as a mathematical programming problem, which was solved by the Markov decision process to determine a best strategy. The experimental results help operators to understand which type of tasks is to be admitted responding to the network state, to maximize net profit, and to achieve flexibility by leveraging cloud technology. In this research, we proposed several cases, including different situation, different cost of system operation, different number of types and number of servers, to experiment how the factors influence the net profit.en
dc.description.provenanceMade available in DSpace on 2021-06-17T06:15:55Z (GMT). No. of bitstreams: 1
ntu-107-R05725039-1.pdf: 1682027 bytes, checksum: c8c5ade6eae61436eef457cbf41fad5c (MD5)
Previous issue date: 2018
en
dc.description.tableofcontents論文摘要 i
Thesis Abstract ii
CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES viii
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 5
1.3 Paper Organization 7
Chapter 2 Literature Review 8
2.1 Resource Allocation and Migration 8
2.1.1 Network Operation Management 8
2.1.2 Resource Allocation and Scheduling 9
2.2 Admission Control 10
2.3 Power Consumption and System Profit in Cloud Service 11
2.4 Markov Decision Process and Optimal Policy 12
Chapter 3 Problem Formulation 13
3.1 Problem Description 13
3.2 Mathematical Formulation 15
Chapter 4 Solution Approach 21
4.1 Markov Chain 21
4.2 Markov Decision Process 22
4.2.1 Definition of Markov Decision Process 23
4.3 The Problem Modeled by Markov Decision Process 23
4.3.1 Value Iteration 25
4.3.2 Pseudo Code and Flowchart 25
Chapter 5 Computational Experiment 29
5.1 Environment 29
5.2 Performance Evaluation 33
5.2.1 Case I: 33
5.2.2 Case II: 35
5.2.3 Case III: 35
5.2.4 Case IV: 36
5.2.5 Case V: 37
5.2.6 Case VI: 38
5.2.7 Case VII: 39
5.2.8 Case VIII: 41
5.2.9 Case IX: 42
5.2.10 Time complexity and space complexity: 43
Chapter 6 Conclusions and Future Work 45
REFERENCES 47
dc.language.isoen
dc.subject允入控制 (Admission Control)zh_TW
dc.subject第五代行動通訊 (5G)zh_TW
dc.subject淨獲利率zh_TW
dc.subject最佳化zh_TW
dc.subject馬可夫決策過程 (Markov Decision Process)zh_TW
dc.subject雲端無線電接取網路 (C-RAN)zh_TW
dc.subjectOptimizationen
dc.subjectAdmission controlen
dc.subjectMarkov Decision Processen
dc.subject5Gen
dc.subjectNet profiten
dc.subjectC-RANen
dc.title於5G雲端接取網路中以馬可夫決策過程達成最佳解之允入控制策略zh_TW
dc.titleA Markov Decision Process Based Approach to Achieving Optimal Admission Control for 5G Cloud Radio Networksen
dc.typeThesis
dc.date.schoolyear107-1
dc.description.degree碩士
dc.contributor.oralexamcommittee呂俊賢,莊東穎,鍾順平,林宜隆
dc.subject.keyword雲端無線電接取網路 (C-RAN),允入控制 (Admission Control),馬可夫決策過程 (Markov Decision Process),第五代行動通訊 (5G),淨獲利率,最佳化,zh_TW
dc.subject.keywordC-RAN,Admission control,Markov Decision Process,5G,Net profit,Optimization,en
dc.relation.page51
dc.identifier.doi10.6342/NTU201803101
dc.rights.note有償授權
dc.date.accepted2018-08-13
dc.contributor.author-college管理學院zh_TW
dc.contributor.author-dept資訊管理學研究所zh_TW
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