博碩士論文 101582604 詳細資訊



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姓名 賓拿雅(Binayak Kar)  查詢紙本館藏   畢業系所 資訊工程學系在職專班
論文名稱 運用軟體定義網路和虛擬化網路技術完成軟體導向網路的成本最佳化
(Cost Optimization of Network Softwarization with Software Defined Networking and Network Function Virtualization)
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摘要(中) 網絡軟件化是基於SDN (Software Defined Networking)和NFV (Network Function Vir-tualization)形式的雲端計算原理的一種範式轉變，目的是即時地提供依照需求、低成本和服務導向的網絡。雖然SDN通過其邏輯集中式架構帶來更多的轉發靈活性，但NFV為其虛擬化技術處理帶來更多靈活性。本文提出了三種不同的解決使用SDN和NFV的網絡軟件成本問題的方法。我們的第一種方法是基於SDN，其他兩種基於NFV。
儘管在SDN網絡中傳輸調度較為單一，但由於分佈式傳統網絡的安裝基數較大，因此SDN節點可能需要與傳統節點共存以形成混合網絡。儘管混合網絡比純粹的傳統網絡具有更好的性能，但由於預算有限，仍然存在SDN快速升級的問題。為了評估混合網絡中成本和覆蓋範圍之間的關係，我們將路徑中SDN節點和至少有一個SDN節點的路徑的最小百分比分別定義為跳數覆蓋和路徑覆蓋。為了評估成本和覆蓋範圍之間的關係，我們將SDN節點選擇分別作為四個跳轉/路徑覆蓋和成本作為目標和約束的優化問題，反之亦然。我們為路徑和跳都制定了SDN覆蓋問題，並提出了兩種算法：1）最大未覆蓋路徑數（MUcPF）和2）最小覆蓋路徑優先數最大值（MMHcPF）。 MATLAB實驗結果表明，與其他現有算法相比，我們提出的算法在覆蓋範圍，成本和效率方面具有更高的性能。
NFV通過虛擬化網絡功能（VNF）鏈將虛擬化擴展到網絡，以便在運行時按需提供所需的功能。這些VNF對其所放置的物理機（PM）的功耗有直接影響。 PM和這些機器的不同負載及其利用率是網絡能耗的關鍵問題。為了解決這個能源問題，我們設計了一個採用NFV技術的動態節能模型，並製定了能源成本優化問題。我們提出VNF鏈（DPVC）算法的動態放置作為解決方案。結果表明，與其他算法相比，我們的算法性能更好，節省更多能源。
電信運營商將其中央辦公室（CO）和移動基站重新設計為NFV數據中心（DC），稱為邊緣數據中心（EDC），可幫助網絡運營商加速部署並降低成本。早些時候NFV的使用被限制在DC內。最近有許多研究正在跨DC使用NFV，即DC間。然而，這些NFV的DC間架構限制了DC之間的通信，無論是水平連接還是垂直連接。我們提出了這些邊緣NFV數據中心的通用體系結構，包括水平和垂直連接，並製定成本優化問題以證明DC之間在通信和計算方面的垂直和水平連接的後果。我們通過估計DC之間的流量調度速率來提出一個帶有延遲和容量約束的成本優化問題，並提出一種垂直 - 水平通信（VHC）啟發式解決方案。所獲得的結果表明，垂直連接與水平連接相比有助於顯著降低計算成本。但是，垂直和水平通信一起可以幫助降低通信成本和總成本，而不是僅垂直或水平通信。
摘要(英) Network softwarization is a paradigm shift based on cloud computing principles in the forms of SDN (Software Defined Networking), and NFV (Network Function Virtualization), in order to provide on-demand, cost-efficient and service-oriented networks on-the-fly. While SDN brings more flexibility in forwarding by its logically centralized architecture, NFV brings more flexibility in processing with its virtualization technology. This thesis presents three different approaches to address the cost issue of network softwarization using SDN and NFV. Our first approach is based on SDN and other two are based on NFV.
Despite having smatter transmission scheduling in SDN network, SDN nodes may require to coexist with legacy nodes to form hybrid networks due to the large installed base of distributed legacy networks. Although, a hybrid network has better performance than a pure legacy network still rapid up-gradation to SDN is an issue due to the limited budget. To evaluate the relationship between cost and coverage in the hybrid network, we define the minimum percentage of SDN nodes in a path, and paths with at least one SDN node, as the hop coverage and path coverage, respectively. To evaluate the relationship between cost and coverage, we formulate SDN node selection as four optimization problems with hop/path coverage and cost as objectives and constraints, respectively, and vice-versa. We formulate SDN coverage problem both for the path and hop and propose two algorithms: 1) maximum number of uncovered path first (MUcPF) and 2) maximum number of minimum hop covered path first (MMHcPF). MATLAB experiment results demonstrate that our proposed algorithms have greater performance in terms of coverage, cost, and efficiency compared to other existing algorithms.
NFV extends the virtualization to networking by virtualized network function (VNF) chaining to provide the required functionality at runtime on demand. These VNFs have a direct impact on power consumption of the physical machines (PM) on which they have placed. The PMs and varying load to these machines and their utilization are the critical issues of the energy consumption in the network. To address this energy issue, we designed a dynamic energy saving model with NFV technology and formulate an energy-cost optimization problem. We propose a Dynamic placement of VNF chains (DPVC) algorithm as a solution. The results show our algorithm performs better and saves more energy compared to other algorithms.
Telecommunication carriers are re-architecting their central offices (CO) and mobile base stations as NFV data centers (DC), known as edge data center (EDC) that help network operators to speed deployment and reduce cost. Earlier the use of NFV was limited to within the DC. Recently there are numerous studies going on the use of NFV across DCs i.e., inter-DC. However, these NFV inter-DC architectures have limited the communication among DCs to either horizontal connectivity or vertical connectivity. We propose a generic architecture of these edge NFV data centers, with both horizontal and vertical connectivity and formulate a cost optimization problem to demonstrate the consequence of both vertical and horizontal connectivity between DCs in terms of communication and computing. We formulate a cost optimization problem with latency and capacity as constraints by estimating the traffic dispatch rate among DCs and propose a vertical-horizontal communication (VHC) heuristic solution. The obtained results show that the vertical connectivity helps to reduce the computing cost significantly compare to horizontal connectivity. However, both the vertical and horizontal communication together can help to reduce the communication cost and total cost compared to only vertical or horizontal communication.
關鍵字(中) ★ 軟體定義網路
★ 混合式軟體定義網路
★ 網路功能虛擬化
★ 虛擬網路功能部署
★ 服務鏈
★ 成本
★ 最佳化		 關鍵字(英) ★ SDN
★ Hybrid SDN
★ NFV
★ VNF placement
★ Service chaining
★ Cost
★ Optimization
論文目次 摘要 VII
ABSTRACT IX
LIST OF FIGURES XVII
LIST OF TABLES XIX
CHAPTER 1. INTRODUCTION 1
1.1. OVERVIEW OF NETWORK SOFTWARIZATION 1
1.2. RESEARCH ROADMAP 2
1.3. SOFTWARE DEFINED NETWORKING 3
1.3.1. Partially Deployed SDN 3
1.4. ENERGY CONSUMPTION AND NFV 6
1.4.1. Energy Consumption Issue 6
1.4.2. Network Function Virtualization 6
1.5. EDGE NFV DATA CENTERS 8
1.5.1. Inter-DC Connectivity 9
1.5.2. Inter-DC Service Chaining 10
1.6. THESIS ORGANIZATION 10
CHAPTER 2. BUDGETED MAXIMUM SDN COVERAGE 11
2.1. OVERVIEW AND MOTIVATION 11
2.2. RELATED WORKS ON HYBRID SDN 13
2.3. BMC PROBLEM FORMULATION 15
2.3.1. Variable Declaration 15
2.3.2. Problem Formulation 16
2.3.2.1. Cost-Constraint Optimization Problem 17
2.3.2.1.1. Maximize the Pcoverage with Cost Constraint 17
2.3.2.1.2. Maximize the Hcoverage with Cost Constraint 18
2.3.2.2. Cost-Objective Optimization Problem 18
2.3.2.2.1. Minimize the Cost with Pcoverage Constraint 19
2.3.2.2.2. Minimize the Cost with Hcoverage Constraint 19
2.3.3. Problem Analysis 20
2.4. SOLUTIONS 22
2.4.1. MUcPF Algorithm 23
2.4.2. MMHcPF Algorithm 25
2.5. RESULTS 27
2.5.1. Pcoverage Performance 28
2.5.1.1. Pcoverage Intermediate Results 30
2.5.2. Hcoverage Performance 32
2.5.2.1. Hcoverage Intermediate Results 33
2.6. SUMMARY 34
CHAPTER 3. ENERGY COST OPTIMIZATION IN NFV 36
3.1. OVERVIEW AND MOTIVATION 36
3.2. RELATED WORKS ON VIRTUALIZATION AND ENERGY CONSUMPTION 36
3.2.1. Energy Saving using Virtual Machines 36
3.2.2. Virtualized Network Function Placement 37
3.3. OIA STATE TRANSITION DESIGN AND MODELING 39
3.3.1. OIA State Transition Diagram 39
3.3.2. M/M/c Queueing Network Model 40
3.4. DPVC PROBLEM FORMULATION 47
3.4.1. Variable Declaration 48
3.4.2. Objective Functions and Constraints 49
3.4.3. Problem Analysis 52
3.4.3.1. Virtual Network Embedding Problem 52
3.5. SOLUTION 54
3.5.1. DPVC Algorithm 56
3.5.1.1. Placement Algorithm 57
3.5.1.1.1. RDFST Algorithm 58
3.6. PERFORMANCE EVALUATION 60
3.6.1. Experiment Setup 60
3.6.2. Results Analysis 62
3.6.2.1. Results Total Energy Cost Consumption over Time 62
3.6.2.2. Utilization of Active Nodes 64
3.6.2.3. Performance Changes with Different Default Energy Consumption in the IDLE State 65
3.6.2.4. Performance Changes with Different Flow Sharing Limit 66
3.6.2.5. Utilization of Active Nodes Performance Changes with Different minCap Value 68
3.6.2.6. Intermediate Results Analysis 68
3.7. SUMMARY 69
CHAPTER 4. EDGE NFV DATA CENTERS CONNECTIVITY 70
4.1. INTRODUCTION 70
4.2. GENERIC INTER-DC ARCHITECTURE 71
4.3. RELATED WORKS 73
4.4. SYSTEM MODEL AND PROBLEM FORMULATION 75
4.4.1. System Model 75
4.4.1.1. Example of Traffic Flow across DCs 77
4.4.2. Inter-DC Traffic Estimation 78
4.4.3. Objective Function and Constraints 80
4.4.4. Problem Analysis 81
4.5. SOLUTION APPROACH 83
4.5.1. Vertical-Horizontal Communication Algorithm 84
4.6. RESULTS 85
4.6.1. Experiment Setup 85
4.6.2. Performance Analysis 86
4.6.2.1. Total Cost Analysis 86
4.6.2.2. Communication Cost vs. Computing Cost 87
4.6.2.3. Communication Cost Analysis 88
4.6.2.4. Computing Cost Analysis 90
4.6.2.5. Flow-drop with Hop Constraints 92
4.7. SUMMARY 93
CHAPTER 5. CONCLUSIONS AND FUTURE WORK 94
5.1. CONCLUSIONS 94
5.2. FUTURE WORK 96
BIBLIOGRAPGY 97
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指導教授 吳曉光 林盈達(Eric Hsiao-Kuang Ying-Dar Lin) 審核日期 2018-6-7
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