系統識別號 | U0002-1907202111301500 |
---|---|
DOI | 10.6846/TKU.2021.00475 |
論文名稱(中文) | 支援加權公平佇列多頻道並預複製遷移之都會型網路研究 |
論文名稱(英文) | Weighted Fair Queueing Multi-channels and Pre-copy Migration in Metropolitan Area Network |
第三語言論文名稱 | |
校院名稱 | 淡江大學 |
系所名稱(中文) | 電機工程學系博士班 |
系所名稱(英文) | Department of Electrical and Computer Engineering |
外國學位學校名稱 | |
外國學位學院名稱 | |
外國學位研究所名稱 | |
學年度 | 109 |
學期 | 2 |
出版年 | 110 |
研究生(中文) | 楊堯強 |
研究生(英文) | Yao-Chiang Yang |
學號 | 801440081 |
學位類別 | 博士 |
語言別 | 英文 |
第二語言別 | |
口試日期 | 2021-07-02 |
論文頁數 | 101頁 |
口試委員 |
指導教授
-
李維聰(wtlee@mail.tku.edu.tw)
委員 - 侯廷偉(houtw@mail.ncku.edu.tw) 委員 - 林志敏(jimmy@fcu.edu.tw) 委員 - 周建興(chchou@mail.tku.edu.tw) 委員 - 吳庭育(tyw@niu.edu.tw) 委員 - 李維聰(wtlee@mail.tku.edu.tw) |
關鍵字(中) |
加權公平排程 多頻道下行 賽局理論 預複製 |
關鍵字(英) |
WFQ Multi-downstream Game Theory Pre-copy |
第三語言關鍵字 | |
學科別分類 | |
中文摘要 |
在都會型網路Metropolitan Area Network (MAN)中,網路範圍定義是介於廣域網路與區域網路之間。不一樣的應用在都會型網路裡,其中一項眾所皆知的即是有線電視(Cable Television)。它遵從著有線電纜資料服務介面規範Data Over Cable Service Interface Specification (DOCSIS)。此規範是由美國有線電視實驗室(Cable Television Laboratories, CableLabs)開發完成,且它支援雙向同時傳輸並應用網際網路協定(Internet Protocol, IP)。 “加權公平排程機制於有線電纜資料服務介面多頻道下行之研究”的主要目的是在有線電纜資料服務介面規範中除了支援服務品質保證(Quality of Service, QoS)技術與頻道捆綁(Channel Bonding)技術的同時, 再加入加權公平排程機制(Weighted Fair Queueing, WFQ), 使得整體服務兼顧品質保證與公平性,換句話說,如何兼顧這兩種特性, 是我們研究的課題。規範提供了五種服務品質保證與頻道捆綁技術機制, 透過纜線數據機(Cable Modem, CM)經由上行(Upstream)提出服務品質保證需求給纜線數據機終端系統(Cable Modem Termination System, CMTS), 纜線數據機終端系統來做相對應的品質服務排程, 在應用中, 最常見運用的排程機制為先來先處理排程(First-Come First-Served, FCFS Queue)與優先等級排程(Priority Queue), 在這些排程機制下, 先來先處理排程使得網路延遲時間整體加長, 優先等級排程也使得網路可能因為優先權過低的封包, 在佇列中一直無法被系統處理到, 因此, 我們於研究中, 加入其加權公平排程機制之方法, 使得封包在分配排程上獲得較公平, 且不會因為優先權過低而發生無限期的阻塞(Indefinite Blocking)或飢餓(Starvation)的問題。 在都會型網路, 還有另一個課題值得研究, 也就是雲端運算。雲端運算是最近很普遍受歡迎的應用,最近,雲端進而也延伸了另一種分散式運算架構的邊緣運算。DOCSIS也不例外,越來越多的網路服務業者提供如此便利的服務。一個原因為虛擬化技術的應用。它可以提供使用者於一台伺服器內架設很多虛擬機,便於各種資料的動態分配。並且,當任何一台伺服器可能因為節能省電需要進入睡眠模式,這時虛擬機將遷移至另一台伺服器繼續服務,使用者是不會有感覺中斷。這是仰賴於即時遷移(Live Migration)技術。 “運用賽局理論期望值對預複製預測之研究”會介紹到透過記憶體之修改機率預測,來決定是否記憶體在下一次修改時,即時遷移已不具效益,決定是否應該直接停止即時遷移,進入停機複製模式。在方法中我們將避免不必要的轉移減少整體遷移需要時間。之前的研究採取別種方法來預測記憶體變更的方法,但是一定要統計幾次疊代的歷史變化,才能準確預測記憶體修改的機率。在本研究中,我們將採用賽局理論模型,有效地減少預測髒頁(Dirty Page)數量來確定是否進入停止和直接複製階段,從而節省遷移所需的時間。 |
英文摘要 |
In Metropolitan Area Network (MAN), the network area size is defined between Wide Area Network (WAN) and Local Area Network (LAN). Different applications exist in MAN, one well-known application of them is Cable Television. The application complies with Data Over Cable Service Interface Specification (DOCSIS). DOCSIS was developed by Cable Television Laboratories (CableLabs). In DOCSIS network, it allows transparent Internet Protocol (IP) traffic in the system communication as well as a useful property of bidirectional transmission simultaneously. The aim of "A Weighted Fair Queueing (WFQ) Management on DOCSIS Multi-downstream Channels" is to develop a WFQ mechanism into DOCSIS network to effectively supply the downstream services. The work is based on Quality of Service (QoS) and Channel Bonding techniques, then implementing the mechanism of WFQ, overall service quality and fair distribution are considered. Totally five QoS models have been supported in DOCSIS. When one Cable Modem (CM) requests that support of QoS. At the end, CMTS will depend on the QoS request from CM to provide the relative service. With the service queues in pipes, general queues are First-Come First-Served (FCFS) Queue and Priority Queue, However, two defects exist. First, the total large latency will be obtained under FCFS Queue, that is because every packet is handled in queue one-by-one. Priority property is not supported in FCFS queue. Second, each packet in Priority Queue will be set with the order in priority, the packet with lower priority will be postponed when a packet with higher priority is sent at the same time. Even though an application of the priority queue can decrease the total latency compared to FCFS model, that also bring other issues, such as Indefinite Blocking or Starvation. According to above, we propose an approach of WFQ implementation in CMTS for solving the defects of FCFS and Priority Queue. The approach is not only to solve Indefinite Blocking or Starvation issue but also to make sure the lower latency is obtained based on the WFQ implementation. In MAN, the application of Cloud Computing is another popular technology in recent years. Recently, it became the approach further as to be distributed on Edge Computing. That idea did not exclude DOCSIS. More and more network applications provided clients more convenient experiences with the services. One of reasons is virtualization technology in Cloud Computing is applied, it improves the usage on the server and includes a characteristic dynamic data assignment. In Addition, any of servers for power-saving that entering Sleep Mode, which data needs to be migrated to another server for continuously operating, the user is not aware that the service has interrupted. That is because a technology of Live Migration will quickly backup the remaining data from the original server to another server. “An Expected Value Prediction of Game Theory for Pre-copy” introduces that is predicting the probability of memory modification to determine the dirty page whether to go the Stop-and-copy phase in the technology of live migration. This method can avoid data being unnecessary transferred over the network and reduce total time during the migration. The previous studies adopted other methods to predict the probability of memory modification. But it must be statistics with the historical changes at several iterations, to be able to accurately predict the probability of memory modification. In this study, we are going to adopt Game Theory model that effectively reduces the predicted numbers to determine the dirty page whether to go Stop-and-copy phase, thus saving time required for live migration. |
第三語言摘要 | |
論文目次 |
Table of Contents Abstract in Chinese II Abstract in English: IV Table of Contents VI List of Figures VIII List of Tables X Chapter 1 Introduction 1 1.1 Background 1 1.2 Objective 3 1.3 Thesis Organization 5 Chapter 2 Related Work 7 2.1 Overview of DOCSIS 7 2.1.1 DOCSIS Architecture 7 2.1.2 The Operation on CMTS and CM 9 2.1.3 Quality of Service Mechanism 10 2.2 Overview of Cloud Computing 14 Chapter 3 A WFQ Management on DOCSIS Multi-downstream Channels 17 3.1 Problem Statement 17 3.2 Weighted Fair Queueing (WFQ) 18 3.3 A Weighted Fair Queueing Management on DOCSIS 19 3.3.1 Single Downstream Channel in CMTS 19 3.3.2 Multiple Downstream Channels in CMTS 22 3.3.3 Multi-downstream Service Algorithm in CMTS 25 3.4 Simulation and Analysis 28 3.4.1 Simulation Parameter 28 3.4.2 Simulation and Analysis 30 3.4.2.1 Scenario 1: All Inputs Rise in Single Downstream Channel 30 3.4.2.2 Scenario 2: UGS/BE Rise in Single Downstream Channel 33 3.4.2.3 Scenario 3: All Inputs Rise in Dual Downstream Channels 38 3.4.2.4 Scenario 4: UGS/BE Input Rise in Dual Downstream Channels 41 3.4.2.5 Scenario 5: A Surge Input in UGS Queue 46 3.5 Summary 51 Chapter 4 An Expected Value Prediction of Game Theory for Pre-copy 52 4.1 Problem Statement 52 4.2 Overview of Prediction Model 59 4.2.1 Gilbert-Elliot (GE) Model 59 4.2.2 Game Theory Model 60 4.3 An Expected Value Prediction of Game Theory 62 4.4 Simulation and Analysis 70 4.4.1 Random Memory State under ε1 = 0.1 70 4.4.2 Random Memory State under ε1 = 0.5 74 4.4.3 Random Memory State under ε1 = 0.9 78 4.4.4 The Predicted Mth of Special Case 1 82 4.4.5 The Predicted Mth of Special Case 2 84 4.4.6 The Predicted Mth of Special Case 3 86 4.5 Summary 89 Chapter 5 Conclusion and Future Work 90 5.1 Contribution 93 5.2 Future Work 93 Reference. 94 List of Figures Figure 1.1: DOCSIS Network System 3 Figure 2.1: Multiple Downstream Channels in DOCSIS 8 Figure 2.2: Data Transmission Process between CMTS and CM 10 Figure 2.3: Five QoS Mechanisms of DOCSIS 11 Figure 2.4: Unsolicited Grant Service, UGS 12 Figure 2.5: Real-Time Polling Service , rtPS 12 Figure 2.6: Unsolicited Grant Service with Activity Detection, UGS/AD 13 Figure 2.7: The migration of Virtual Machine (VM) 15 Figure 2.8: Pre-copy and Post-copy process 16 Figure 3.1: The Network from CMTS to CM 19 Figure 3.2: CMTS with Single Downstream Channel 19 Figure 3.3: CMTS with Multiple Downstream Channels 23 Figure 3.4: Multi-downstream Service Algorithm(MDSA) 26 Figure 3.5: All Inputs Rise in Single Downstream Channel 30 Figure 3.6: Average Queueing Length with α=0.1, 0.05, 0.01, 0.001 31 Figure 3.7: Average Delay Time with α=0.1, 0.05, 0.01, 0.001 32 Figure 3.8: UGS/BE Rise in Single Downstream Channel 34 Figure 3.9: UGS Variation in Single Downstream Channel 35 Figure 3.10: BE Variation in Single Downstream Channel 36 Figure 3.11: All Inputs Rise in Dual Downstream Channels 38 Figure 3.12: Two Downstream Channels Adjustment with α=0.05, 0.01 39 Figure 3.13: One Downstream Channel Adjustment with α=0.05, 0.01 40 Figure 3.14: UGS/BE Input Rise in Dual Downstream Channels 42 Figure 3.15: UGS Variation in Dual Downstream Channels 43 Figure 3.16: BE Variation in Dual Downstream Channel 45 Figure 3.17: A Surge Input in UGS Queue 47 Figure 3.18: Queueing Length with β = 2.5, 3.0, 3.5, 4.0 in 10000 rounds 47 Figure 3.19: Delay Time with β = 2.5, 3.0, 3.5, 4.0 in 10000 rounds 48 Figure 4.1: Edge Computing Architecture 53 Figure 4.2: An Example of Edge Computing in DOCSIS network 54 Figure 4.3: The Flowchart of Sun et al Predicted Method 55 Figure 4.4: The Flowchart of Wu et al Memory Prediction 57 Figure 4.5: The Flowchart of Lin et al Memory Prediction 58 Figure 4.6: The Gilbert-Elliot(GE) Model 59 Figure 4.7: The Flowchart of Utilizing Game Theory 63 Figure 4.8: The Probability under ε1 = 0.1 and ε2 = 0.1 71 Figure 4.9: The Probability under ε1 = 0.1 and ε2 = 0.3 71 Figure 4.10: The Probability under ε1 = 0.1 and ε2 = 0.5 72 Figure 4.11: The Probability under ε1 = 0.1 and ε2 = 0.7 72 Figure 4.12: The Probability under ε1 = 0.1 and ε2 = 0.9 73 Figure 4.13: The Probability under ε1 = 0.5 and ε2 = 0.1 75 Figure 4.14: The Probability under ε1 = 0.5 and ε2 = 0.3 76 Figure 4.15: The Probability under ε1 = 0.5 and ε2 = 0.5 76 Figure 4.16: The Probability under ε1 = 0.5 and ε2 = 0.7 77 Figure 4.17: The Probability under ε1 = 0.5 and ε2 = 0.9 77 Figure 4.18: The Probability under ε1 = 0.9 and ε2 = 0.1 79 Figure 4.19: The Probability under ε1 = 0.9 and ε2 = 0.3 80 Figure 4.20: The Probability under ε1 = 0.9 and ε2 = 0.5 80 Figure 4.21: The Probability under ε1 = 0.9 and ε2 = 0.7 80 Figure 4.22: The Probability under ε1 = 0.9 and ε2 = 0.9 81 Figure 4.23: The Probability of Special Case 1 83 Figure 4.24: The Probability of Special Case 2 85 Figure 4.25: The Probability of Special Case 3 88 List of Tables Table 3.1: Simulation Parameter 29 Table 3.2: The latest kth value of wi,j with α=0.05 32 Table 3.3: UGS Variation with α=0.05 and 0.01 34 Table 3.4: BE Variation with α=0.05 and 0.01 35 Table 3.5: All Input Rise in α=0.05 with One/Two Channels Adjustment 40 Table 3.6: UGS Rise in α=0.05 with One/Two Channels Adjustment 42 Table 3.7: BE Rise in α=0.05 with One/Two Channels Adjustment 44 Table 3.8: The kth value of wUGS,j with β = 2.5, 3.0, 3.5, 4.0 48 Table 3.9: Average Queueing Length and Delay Time with β = 2.5, 3.0, 3.5, 4.0 49 Table 4.1: The Strategies of Player: R and Player: C 61 Table 4.2: The Probability of The M(N+1)th and MNth Iteration 64 Table 4.3: The List of Memory Page State 66 Table 4.4: The List of Memory State with Default Value “1” 67 Table 4.5: The Preliminary Probability List 68 Table 4.6: The Probability List of M0 and Default 68 Table 4.7: The Secondary Probability List 69 Table 4.8: Parameter List under ε1 = 0.1 70 Table 4.9: Parameter List under ε1 = 0.5 75 Table 4.10: Parameter List under ε1 = 0.9 79 Table 4.11: Parameter List of Special Case 1 82 Table 4.12: The Memory List of Special Case 1 83 Table 4.13: Parameter List of Special Case 2 84 Table 4.14: The Memory List of Special Case 2 85 Table 4.15: Parameter List of Special Case 3 87 Table 4.16: The Memory List of Special Case 3 87 |
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