Tài liệu Các phương pháp tiết kiệm năng lượng sử dụng công nghệ mạng điều khiển bằng phần mềm trong môi trường điện toán đám mây

MINISTRY OF EDUCATION AND TRAINING HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY TRAN MANH NAM CÁC PHƯƠNG PHÁP TIẾT KIỆM NĂNG LƯỢNG SỬ DỤNG CÔNG NGHỆ MẠNG ĐIỀU KHIỂN BẰNG PHẦN MỀM TRONG MÔI TRƯỜNG ĐIỆN TOÁN ĐÁM MÂY SDN-BASED ENERGY-EFFICIENT NETWORKING IN CLOUD COMPUTING ENVIRONMENTS DOCTORAL THESIS OF TELECOMMUNICATIONS ENGINEERING HANOI - 2018 MINISTRY OF EDUCATION AND TRAINING HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY TRAN MANH NAM CÁC PHƯƠNG PHÁP TIẾT KIỆM NĂNG LƯỢNG SỬ DỤNG CÔNG NGHỆ MẠNG ĐIỀU KHIỂN BẰNG PHẦN MỀM TRONG MÔI TRƯỜNG ĐIỆN TOÁN ĐÁM MÂY SDN-BASED ENERGY-EFFICIENT NETWORKING IN CLOUD COMPUTING ENVIRONMENTS Specialization: Telecommunications Engineering Code No: 62520208 DOCTORAL THESIS OF TELECOMMUNICATIONS ENGINEERING Supervisor: Assoc.Prof. Nguyen Huu Thanh HANOI - 2018 PREFACE I hereby assure that the results presented in this dissertation are my work under the guidance of my supervisor. The data and results presented in the dissertation are completely honest and have not been disclosed in any previous works. The references have been fully cited and in accordance with the regulations. Tôi xin cam đoan các kết quả trình bày trong luận án là công trình nghiên cứu của tôi dưới sự hướng dẫn của giáo viên hướng dẫn. Các số liệu, kết quả trình bày trong luận án là hoàn toàn trung thực và chưa được công bố trong bất kỳ công trình nào trước đây. Các kết quả sử dụng tham khảo đều đã được trích dẫn đầy đủ theo đúng quy định. Hà Nội, Ngày 19 tháng 01 năm 2018 Tác giả Trần Mạnh Nam ii ACKNOWLEDGEMENTS First and foremost, I would like to thank my advisor, Associate Prof. Dr. Nguyen Huu Thanh, for providing an excellent researching atmosphere, for his valuable comments, constant support and motivation. His guidance helped me in all the time and also in writing this dissertation. I could not have thought of having a better advisor and mentor for my PhD. Moreover, I would like to thank Associate Prof. Dr. Pham Ngoc Nam, Dr. Truong Thu Huong for their advices and feedbacks, also for many educational and inspiring discussions. My sincere gratitude goes to the members (present and former) of the Future Internet Lab, School of `Electronics and Telecommunications, Hanoi University of Science and Technology. Without their support and friendship it would have been difficult for me to complete my PhD studies. Finally, I would like to express my deepest gratitude to my family. They are always supporting me and encouraging me with their best wishes, standing by me throughout my life. Hanoi, 19th Jan 2018 iii CONTENTS LIST OF FIGURES ...................................................................... viii LIST OF TABLES ........................................................................... x INTRODUCTION ............................................................................ 1 CHAPTER 1. AN OVERVIEW OF ENERGY-EFFICIENT APPROACHES IN CLOUD COMPUTING ENVIRONMENTS ........ 6 1.1 Today's Internet ................................................................................ 6 1.1.1 Cloud Computing Services and Infrastructures ............................ 6 1.1.2 Energy consumption problems ..................................................... 6 1.2 An Overview of Energy-Efficient Approaches................................ 8 1.2.1 Energy consumption characteristics ............................................. 8 1.2.2 Energy-Efficient Approaches' Classification ................................. 9 1.3 Software-defined Networking (SDN) technology ......................... 10 1.3.1 SDN Architecture ....................................................................... 10 1.3.2 SDN Southbound API - OpenFlow Protocol ............................... 11 1.3.3 SDN Controllers ......................................................................... 12 1.4 Difficulties on Network Energy Efficiency and Motivations ........ 13 1.5 Dissertation’s Contributions.......................................................... 14 1.5.1 Proposing an energy-aware and flexible data center network that is based on the SDN technology. .................................................................... 14 1.5.2 Proposing energy-efficient approaches in a network virtualization for cloud environments. ................................................................................ 14 1.5.3 Proposing an energy-aware data center virtualization for cloud environments. .............................................................................................. 15 CHAPTER 2. NETWORK SDN-BASED ENERGY-AWARE DATA CENTER 16 2.1 Background Technologies ............................................................. 16 2.1.1 DCN technique and architecture ................................................ 16 2.1.2 Existing system .......................................................................... 22 2.2 Power-Control System of a DC Network ....................................... 22 2.2.1 Energy modeling of a network .................................................... 23 2.2.2 The Diagram of the Power-Control System ................................ 25 2.3 Energy-Aware Routing based on Power Profile of Devices in Data Center Networks using SDN.......................................................................... 29 2.3.1 Energy-Aware Routing and Topology Optimization Algorithm.... 30 2.3.2 Performance evaluation ............................................................. 36 2.4 Green Data Center using centralized Power-control of the Network and servers ..................................................................................................... 39 2.4.1 Extended Power-Control System ............................................... 40 2.4.2 Use case .................................................................................... 41 2.4.3 Topology-aware VM migration algorithm .................................... 43 2.4.4 VM Migration cost and Power modeling of a Server .................. 45 2.4.5 Experimental Results ................................................................. 45 2.5 Conclusion ...................................................................................... 48 CHAPTER 3. ENERGY-EFFICIENT NETWORK VIRTUALIZATION FOR CLOUD ENVIRONMENTS..................... 49 iv 3.1 Network Virtualization and Virtual Network Embedding ............. 51 3.2 Constructing Energy-Aware SDN-based Network Virtualization System 51 3.2.1 System’s Diagram ...................................................................... 52 3.2.2 System’s workflow ...................................................................... 53 3.3 Modeling and Problem Formulation .............................................. 54 3.3.1 VNE Modeling ............................................................................ 54 3.3.2 Objective and Constraints .......................................................... 55 3.3.3 Time-based Embedding Strategies ............................................ 57 3.4 Energy-efficient VNE algorithms ................................................... 58 3.4.1 Energy-cost Coefficient of Capacity ........................................... 58 3.4.2 Virtual Node Mapping algorithms ............................................... 59 3.4.3 Virtual Link Mapping (VLiM) Algorithm ....................................... 62 3.5 Performance Evaluation ................................................................. 63 3.6 Conclusion ...................................................................................... 67 CHAPTER 4. AN ENERGY-AWARE DATA CENTER VIRTUALIZATION FOR CLOUD ENVIRONMENTS..................... 68 4.1 Virtual DC Technologies ................................................................ 69 4.1.1 Virtual data center embedding ................................................... 69 4.1.2 Virtual machine migration and server consolidation ................... 71 4.1.3 Discussion .................................................................................. 71 4.2 Design Objectives........................................................................... 73 4.3 Problem Formulation ...................................................................... 74 4.3.1 Data Center Modeling ................................................................ 74 4.3.2 Energy Modeling of DC Components ......................................... 75 4.3.3 Energy-Efficient Problem Formulation ........................................ 76 4.4 A New Concept for VDC Embedding ............................................. 77 4.4.1 Energy-aware VDC architecture................................................. 77 4.4.2 Energy-aware VDC embedding algorithm .................................. 78 4.4.3 Joint VDC Embedding and VM Migration Algorithms ................. 81 4.5 Performance Evaluation ................................................................. 84 4.5.1 Performance criteria ................................................................... 84 4.5.2 Numerical results ....................................................................... 85 4.6 Conclusion ...................................................................................... 91 CHAPTER 5. CONCLUSION AND FUTURE WORK .................. 92 5.1 5.2 Major contributions ........................................................................ 92 Future research directions ............................................................ 93 LIST OF PUBLICATIONS ............................................................ 94 REFERENCES ............................................................................. 96 v ABBREVIATIONS APCI APEX ASIC BAU BFS CAPEX DC DCN D-ITG EA-NV EA-VDC ECO FM FPGA GH HEA-E HEE IaaS ICT ISP MoA MST NaaS NFV NV OLD OPEX PaaS PCS PM POD PSnEP RMD-EE SaaS SDSN SN Advanced Configuration & Power Interface Capital expenditure Application specific integrated circuits Business-as-usual Breadth-first Search Capital Expenditure Data center Data center network Distributed internet traffic generator Energy-aware network virtualization Energy-aware Virtual Data Center Eco sustainable Full migration Field programmable gate arrays GreenHead Heuristic Energy-aware VDC Embedding Heuristic energy-efficient Infrastructure-as-a-service Information and communication technologies Internet service provider Migrate on arrival Minimum spanning tree Network-as-a-service Network function virtualization Network virtualization OpenDayLight Operating expenses Platform-as-a-service Power-Control System Partial migration Optimized data centers Power scaling and energy-profile-aware Reducing middle node energy efficiency Software-as-a-service Software-Defined Substrate Network SecondNet vi SNMP TCAM VDC VDCE VLiM VM VmM VNE VNoM VNR Simple network management protocol Ternary content-addressable memory Virtual data center Virtual data center embedding Virtual link mapping Virtual Machine Virtual machine mapping Virtual network embedding Virtual node mapping Virtual network requests vii LIST OF FIGURES Figure 1.1: Estimate of the global carbon footprint of ICT (including PCs, telcos’ networks and devices, printers and datacenters) [15].......................................................................... 7 Figure 1.2: Energy consumption estimation for the European telcos’ network infrastructures in the”Business-As-Usual” (BAU) and in the Eco-sustainable (ECO) scenarios, and cumulative energy savings between the two scenarios [16]. ................................................ 7 Figure 1.3: Operating Expenses (OPEX) estimation related to energy costs for the European telcos’ network infrastructures in the ”Business-As-Usual” (BAU) and in the Ecosustainable (ECO) scenarios, and cumulative savings between the two scenarios [17] ...... 8 Figure 1.4: SDN Architecture ............................................................................................. 11 Figure 1.5: OpenFlow controller and switches................................................................... 12 Figure 2.1: DCN Architecture [43] ..................................................................................... 18 Figure 2.2: Three-tier DCN Architecture [45] ................................................................... 18 Figure 2.3: Fat-tree DCN Topology ................................................................................... 19 Figure 2.4: Dcell DCN Architecture [53] ........................................................................... 19 Figure 2.5: BCube DCN Architecture [54] ......................................................................... 20 Figure 2.6: Fat-tree architecture with k = 4 ....................................................................... 21 Figure 2.7: Diagram of the ElasticTree system [57] .......................................................... 22 Figure 2.8: Energy – Utilization relation of a network [58] .............................................. 23 Figure 2.9: Power-control System of a Network ................................................................. 26 Figure 2.10: Fat-tree topology with Minimum Spanning Tree ........................................... 28 Figure 2.11: Power Scaling Algorithm ............................................................................... 32 Figure 2.12: Power Scaling and Energy-Profile-Aware - PSnEP algorithm (Proposed Algorithm 1). The flowchart describes the process between Edge and Aggregation switches ............................................................................................................................................. 34 Figure 2.13: use-case with PSnEP algorithm in a DCN ..................................................... 35 Figure 2.14: PSnEP vs Power scaling (PS) with k=6 Fat-tree, mix scenario .................... 38 Figure 2.15: Energy-saving level ratio of the PSnEP algorithm to the PS algorithm in different sizes ....................................................................................................................... 39 Figure 2.16: Extended Power-Control system (Ext-PCS) ................................................... 40 Figure 2.17: Example .......................................................................................................... 42 Figure 2.18: First-fit Migration [67] Algorithm ................................................................. 42 viii Figure 2.19: Topology-Aware Placement Algorithm .......................................................... 43 Figure 2.20: K=8, comparison with full mesh scenario ..................................................... 46 Figure 2.21: K=16, comparison with full mesh scenario ................................................... 47 Figure 2.22: K=8, comparison with Honeyguide ............................................................... 47 Figure 2.23: K=16, comparison with Honeyguide ............................................................. 48 Figure 3.1: FlowVisor – Hypervisor-like Network Layer [71] ........................................... 50 Figure 3.2: Example of a virtual network on top of a physical network ............................. 51 Figure 3.3: Energy-Aware Network Virtualization system’s Diagram ............................... 52 Figure 3.4: Online VNE mapping method ........................................................................... 57 Figure 3.5: Online using Time Window method.................................................................. 58 Figure 3.6: The GUI of an Energy-aware network virtualization platform........................ 64 Figure 3.7 AR– Online ........................................................................................................ 65 Figure 3.8: AR – Online using Time Windows .................................................................... 65 Figure 3.9: Percentage of Power Consumption to Full State in Online Strategy ............... 65 Figure 3.10 Percentage of Power Consumption to Full State in OuTW Strategy ............. 65 Figure 3.11: Comparison of comsumed energy between Online and OuTW strategies ..... 66 Figure 3.12: Comparison of acceptance ratio between Online and OuTW strategies ....... 66 Figure 4.1: Traditional cloud service provider vs NaaS ..................................................... 68 Figure 4.2: Embedding virtual data center requests on a physical data center ................. 70 Figure 4.3: Virtual data center embedding - Static mapping; ............................................ 72 Figure 4.4: Virtual data center embedding - Dynamic mapping ........................................ 72 Figure 4.5: Energy proportional property of energy-aware data centers .......................... 73 Figure 4.6: Energy-Aware VDC Architecture ..................................................................... 78 Figure 4.7: VDC Embedding Flowchart ............................................................................. 79 Figure 4.8: Flowchart of Partial Migration (PM) .............................................................. 83 Figure 4.9: Migration on Arrival ........................................................................................ 84 Figure 4.10: Fluctuation of system utilization (SecondNet)................................................ 86 Figure 4.11: DC Utilization per Load ................................................................................. 87 4.12: Acceptance Ratio per VM ........................................................................................... 87 Figure 4.13: Acceptance Ratio per VDC............................................................................. 88 ix Figure 4.14: Total power consumption of the physical DC ................................................ 88 Figure 4.15: Average consumed power per serving VDC................................................... 89 Figure 4.16: Number of migrations for different strategies ................................................ 90 Figure 4.17: Comparison of embedding - migration strategies .......................................... 90 4.18: Different embedding-magrition strategies: (a) GreenHead, (b) SecondNet, (c) Partial Migration, (d) Migration on Arrival, (e) Full Migration .................................................... 91 LIST OF TABLES Table 1.1: The Internet’s users in the world [1] ................................................................... 6 Table 1.2: Estimated power consumption sources in a generic platform of IP router ......... 8 Table 1.3: Classification of energy-efficient approaches of the future Internet [4] ............. 9 Table 2.1: Power Summary For A 48-Port Pronto 3240 .................................................... 30 Table 2.2: Energy consumption of NetFPGA-Based OpenFlow Switch ............................. 31 Table 2.3: Energy-saving ratio of PSnEP to Power scaling algorithm in different topology’s sizes...................................................................................................................................... 39 Table 2.4: Traffic demand ................................................................................................... 41 Table 2.5: Power profile of server Dell PowerEdge R710.................................................. 46 Table 3.1: Virtual Network Embedding Terminology ......................................................... 54 Table 3.2: Acceptance ratio and power consumption of the system under different window size in OuTW........................................................................................................................ 67 Table 4.1: Standard deviation of system utilization ............................................................ 86 x INTRODUCTION 1. Overview of Network Energy Efficiency in Cloud Computing Environments The advances in Cloud Computing services as well as Information and Communication Technologies (ICT) in the last decades have massively influenced economy and societies around the world. The Internet infrastructure and services are growing day by day and play a considerable role in all aspects including business, education as well as entertainment. In the last four years, the percentage of people using Internet witnesses an annual growth of 3.5%, from 39% world population’s percentage in Dec-2013 to 51.7% in June-2017 [1]. To support the demand of cloud network infrastructure and Internet services in the rapid growth of users, it is necessary for the Internet providers to have a large number of devices, complex design and architecture that have the capacity to perform increasingly number of operations for a scalability. Consequently, many huge cloud infrastructures have been employed by Telcos, Internet Service Providers (ISPs) and enterprises for the exploded demand of various applications and data cloud-services such as YouTube, Dropbox, e-learning, cloud office etc. To meet the requirements of these booming services all around the world, cloud network infrastructures have been built up in a very large scale, even geographically distributed data centers with a huge number of network devices and servers. In addition, the maintenance of the systems with high availability and reliability level requires a notable redundancy of devices such as routers, switches, links etc. As a result, having such a large infrastructure consumes a huge volume of energy, which leads to consequent environmental and economic issues: - - Environmentally, the amount of energy consumption and carbon footprint of the ITC-sector is remarkable. The manufacture of ICT equipment is estimated its use and disposal account for 2% of global CO2 emissions, which is equivalent to the contributions from the aviation industry [2]. The networking devices and components estimate around 37% of the total ICT carbon emission [3]; Economically, the huge consumed power leads to the costs sustained by the providers/operators to keep the network up and running at the desired service level and their need to counterbalance ever-increasing cost of energy. Although network energy efficiency has recently attracted much attention from communities [4], there are still many issues in realization of the energy-efficient network including inflexibility and the lack of an energy-aware network. The main difficulties of the network energy efficiency as well as its research motivations are shortly described as follows: - Inflexible network: first, one important point the network in cloud data centers (DC) nowadays is the inflexibility issue. For changing the processing algorithm and the control plane of a network, its administrators should carefully re-design, 1 - re-configure and migrate the network for a long time. In many cases, there is a technical challenge for an administrator to apply new approaches and evaluate their efficiency. Consequently, the flexible and programmable network is strictly necessary. Secondly, there are difficulties in evaluating the energy-saving levels of new energy-efficient approaches in a network due to the lack of the centralized power-control system. This system allows administrators and developers to monitor, control and managing the working states as well as power consumption of all network devices in real-time. Energy-aware networking for virtualization technologies in cloud environments: cloud computing has emerged in the last few years as a promising paradigm that facilitates such new service models as Infrastructure-as-a-Service (IaaS), Storageas-a-Service (SaaS), Platform-as-a-Service (PaaS), Network-as-a-Service (NaaS). For such kinds of cloud services, virtualization techniques including network virtualization [5] [6] [7] and data center virtualization [8] [9] [10] have quickly developed and attracted much attention of research and industrial communities. Currently, research in virtualization technologies mainly focuses on the resource optimization and resource provisioning approaches [8] [9]. There are very few works focusing on the energy efficiency of a network. With the benefits of flexible controlling and resource management of virtualization technologies as well as new network technologies such as Software-defined Networking (SDN) [11] [12] [13], researching in network energy efficiency in virtualization is an important and promising approach. Additionally, the SDN technology, the emergence of new trends in networking technology, provides new way to realize and optimize network energy efficiency. Softwaredefined networking [11] aims to change the inflexible state networking, by breaking vertical integration, separating the network’s control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. Consequently, SDN is an important key for resolving aforementioned difficulties. 2. Research Scope and Methodology a) Research Scope The scope of this research focuses on the network energy efficiency in cloud computing environments, including: (1) energy efficiency in centralized data center network; (2) energy efficiency in network virtualization; and (3) energy efficiency in data center virtualization. The proposed energy-efficient approaches are based on the Software-defined Networking technology [11] [12] [13]. b) Research Methodology: the research methodology is used following the reference [14]. 2 o Step 1: Problem formulation: ▪ Interrogative form ▪ Describe relations among constructs o Step 2: Hypothesis formulation: answering to problem statements o Step 3: Research design: building research plan for a research process including survey, related work and experiments o Step 4: Sampling and Data Collection o Step 5: Data analysis o Step 6: Manuscript Writing 3. Contributions and Structure of the Dissertation Recently, Software-defined Networking technology [5] is likely an evolutionary step in Internet technologies that makes networking become more flexible and programmable. SDN is an important key to resolving the difficulties of energy efficiency. This technology also can quickly realize the virtualization technologies including network virtualization and data center virtualization. Consequently, SDN-based energy-efficient networking approaches in cloud environments are focused on this dissertation with the following contributions: - - - The SDN technology is used as core technology in this dissertation for proposing energy-efficient network approaches. The first contribution of this dissertation is resolving the lack of energy-aware network in a DC by (1) proposing a SDN-based power-control system (PCS) of a network. The proposed system allows the administrator of a network to flexibly control and monitor the state of network devices and the energy consumption of the whole network infrastructure. Thanks to the flexibility and availability of this PCS system, several energy-efficient algorithms are proposed and evaluated on it successfully. The network virtualization (NV) technology in cloud environments becomes more popular and plays an important role for such cloud services including Network-asa-service (NaaS), Infrastructure-as-a-service (IaaS). The energy-aware NV platform is necessary for network energy efficiency. Appropriately, (2) the SDN-based energy-aware network virtualization (EA-NV) platform is proposed in this dissertation. The platform is aware of power consumption of the network virtualization environment. Two novel energy-efficient virtual network embedding algorithms are also proposed and implemented in this platform that focus on increasing the energy-saving level and maintaining the reasonable resource optimization of a network. Virtual data center technology is a concept of network virtualization in cloud environments that allows creating multiple separated virtual data centers (VDC) on top of the physical data center [8] [9] [10]. In consequence, (3) an energy-aware virtual data center platform is deployed. On this system, novel energy-aware algorithms are also proposed which focus on the following objectives: (1) resource 3 efficiency that deals with efficient mapping of virtual resources on substrate resources in terms of CPU, memory and network bandwidth; and (2) energy efficiency that deals with minimizing energy consumption of the virtual data center while meeting virtual data center mapping demands. The above contributions of this dissertation are organized as the collection of several SDN-based network energy-efficient approaches which are presented in five chapters as follows: - - The first chapter presents an overview of energy-efficient network in cloud environments and their classification. The difficulties of the network’s energy efficiency area as well as the background of the Software-defined Networking technology are also described in details. In the second chapter, a SDN-based power-control system (PCS) of a data center network is proposed. Based on this platform, developers can propose, implement and evaluate several network energy-saving algorithms. Two energy-efficient approaches, which are applied onto the PCS system, are also proposed with their results and algorithms published in: ✓ Tran Manh Nam, Nguyen Huu Thanh, Doan Anh Tuan “Green Data Center Using Centralized Power-Management Of Network And Servers”, The 15th international Conference on Electronics, Information, and Communication (IEEE - ICEIC), Jan 2016, Da Nang, Vietnam ✓ Tran Manh Nam, Nguyen Huu Thanh, Ngo Quynh Thu and Hoang Trung Hieu, Stefan Covaci, “Energy-Aware Routing based on Power Profile of Devices in Data Center Networks using SDN”, the 12th IEEE ECTI-CON conference - 2015, Hua-Hin, Thailand - Achieved a student Grant of ECTI-CON, Jun, 2015. ✓ Tran Manh Nam, Truong Thu Huong, Nguyen Huu Thanh, Pham Van Cong, Ngo Quynh Thu, Pham Ngoc Nam, “A Reliable Analyzer for Energy-Saving Approaches in Large Data Center Networks”, IEEE ICCE - The International Conference on Communications and Electronics - 2014, Da Nang, Vietnam ✓ Tran Manh Nam, Tran Hoang Vu, Vu Quang Trong, Nguyen Huu Thanh, Pham Ngoc Nam, “Implementing Rate Adaptive Algorithm in Energy-Aware Data Center Network”, National Conference on Electronics and Communications (REV2013-KC01)., Hanoi, Vietnam. - The third chapter describes an energy-aware network virtualization concept and its power monitoring and controlling abilities. The proposed concept is SDN-based which allows developers to implement several energy-efficient virtual network embedding algorithms. Two energy-efficient embedding algorithms, namely heuristic energy-efficient node mapping and reducing middle node energy efficiency, are proposed in this section. The results and algorithms of this chapter are published in: 4 ✓ Tran Manh Nam, Nguyen Huu Thanh, Nguyen Hong Van, Kim Bao Long, Nguyen Van Huynh, Nguyen Duc Lam, Nguyen Van Ca, “Constructing EnergyAware Software-Defined Network Virtualization”, Proceedings of Asia-Pacific Advanced Network Research Workshop (APAN-NRW), August 10th - 14th 2015, Kuala Lumpur, Malaysia - (best student paper award). ✓ Thanh Nguyen Huu, Anh-Vu Vu, Duc-Lam Nguyen, Van-Huynh Nguyen, Manh-Nam Tran, Quynh-Thu Ngo, Thu-Huong Truong, Tai-Hung Nguyen, Thomas Magedanz. “A Generalized Resource Allocation Framework in Support of Multilayer Virtual Network Embedding based on SDN”, Elsevier - Computer Networks, 2015. ✓ Nam T.M., Huynh N.V., Thanh N.H. (2016). “Reducing Middle Nodes Mapping Algorithm for Energy Efficiency in Network Virtualization”. In: Advances in Information and Communication Technology, ICTA 2016. Advances in Intelligent Systems and Computing, vol 538. Springer, Cham. https://doi.org/10.1007/978-3-319-49073-1_54. ✓ Tran Manh Nam, Nguyen Tien Manh, Truong Thu Huong, Nguyen Huu Thanh (2018). “Online Using Time Window Embedding Strategy in Green Network Virtualization”, International Conference on Information and Communication Technology and Digital Convergence Business (ICIDB-2018), Hanoi, Vietnam. (presented) - SDN-based Energy-aware Virtual Data Center (VDC) approach is presented in the fourth chapter. The VDC technology and its main problems, namely VDC embedding problems, are described in details. Three Joint VDC Embedding and VM migration strategies are successfully proposed and evaluated on top of this SDNbased VDC concept. The experimental results and detailed algorithms of this chapter are published in: ✓ Tran Manh Nam, Nguyen Van Huynh, Le Quang Dai, Nguyen Huu Thanh, “An Energy-Aware Embedding Algorithm for Virtual Data Centers”, ITC28 International Teletraffic Congress, Sep - 2016, Wurzburg, Germany. ✓ Tran Manh Nam, Nguyen Huu Thanh, Hoang Trung Hieu, Nguyen Tien Manh, Nguyen Van Huynh, Tuan Hoang. (2017). “Joint Network Embedding and Server Consolidation for Energy-Efficient Dynamic Data Center Virtualization”, Elsevier - Computer Networks, 2017 - doi.org/10.1016/j.comnet.2017.06.007 - In the last chapter, the conclusion of the dissertation and its future work are presented. 5 CHAPTER 1. AN OVERVIEW OF ENERGY-EFFICIENT APPROACHES IN CLOUD COMPUTING ENVIRONMENTS This chapter provides an overview of the Internet status nowadays and the energyefficient approaches in cloud computing environments, on which the networking community is focusing currently. The chapter also addresses the difficulties and motivations on network energy efficiency and the future Internet technologies in cloud computing environments including the Software-Defined Networking technology, network virtualization technology and data center virtualization technology. In a nutshell, the research approaches and contributions of this dissertation are summarized in this chapter. 1.1 Today's Internet 1.1.1 Cloud Computing Services and Infrastructures The advances in Information and Communication Technologies (ICT) in the last decades have massively influenced economy and societies around the world. The Internet services as well as cloud computing services are growing day by day and play a considerable role in all aspects including education, business and entertainment. As we can see in the Table 1.1¸ in the last four years, the percentage of people using Internet witnesses an annual growth of 3.5%, from 39% world population’s percentage in Dec-2013 to 51.7% in June-2017 [1]. Table 1.1: The Internet’s users in the world [1] Date Dec, 2013 Dec, 2014 Dec, 2015 Dec. 2016 June. 2017 Number of users World population’s percentage 2,802 millions 39.0 % 3,079 millions 42.4 % 3,366 millions 46.4 % 3,696 millions 49.5 % 3,885 millions 51.7 % To meet this booming of cloud services such as IaaS, NaaS, SaaS, cloud computing environments with their large network infrastructures have been deployed in a very large scale, even geographically distributed data centers with a huge number of devices. These large infrastructures consumes the high volume of energy which leads to many environmental and economical problems. 1.1.2 Energy consumption problems Although the benefits of having that infrastructure are considerable, such a large system consumes the high volume of energy and leads to consequent issues: 6 Figure 1.1: Estimate of the global carbon footprint of ICT (including PCs, telcos’ networks and devices, printers and datacenters) [15]. - - Environmentally, the amount of energy consumption and carbon footprint of the ITC-sector is remarkable (Figure 1.1). Gartner Company, the ICT research and advisory company, estimates that the manufacture of ICT equipment, its use and disposal account for 2% of global CO2 emissions, which is equivalent to the contributions from the aviation industry [2]. The networking devices and components eliminate around 37% of the total ICT carbon emission [3]; Economically, the huge consumed power leads to the costs sustained by the providers/operators to keep the network up and running at the desired service level and leads to their need of counterbalancing ever-increasing cost of energy (Figure 1.2 and Figure 1.3). Figure 1.2: Energy consumption estimation for the European telcos’ network infrastructures in the”Business-As-Usual” (BAU) and in the Eco-sustainable (ECO) scenarios, and cumulative energy savings between the two scenarios [16]. Because of these issues, the requirement of designing a high performance and energyefficient network has become a crucial matter for Telcos and ISPs towards greener cloud environments. 7 Figure 1.3: Operating Expenses (OPEX) estimation related to energy costs for the European telcos’ network infrastructures in the ”Business-As-Usual” (BAU) and in the Eco-sustainable (ECO) scenarios, and cumulative savings between the two scenarios [17] 1.2 An Overview of Energy-Efficient Approaches In this section, first, the most significant part of energy consumption of network device is characterized with its existing researches. Secondly, the taxonomy energy-efficient approaches, which are currently undertaken, is also presented. 1.2.1 Energy consumption characteristics Table 1.2: Estimated power consumption sources in a generic platform of IP router Efficient energy use, sometimes simply called energy efficiency concept, is far from being new in a computing system. To the best of our knowledge, the first support of power management system was published in 1999, namely “Advanced Configuration & Power Interface” (ACPI) standard [18]. Thenceforth, more energy-saving mechanisms were developed and introduced, especially in hardware enhancement with the new CPUs, which could be more efficient and consumed less energy. Tucker [19] and Neilson [20] estimated on IP routers that the control plane weighs 11%, data plane for 54% and power and heat management for 35%. Tucker and Neilson also broke out the energy consumption of data plane in more detail as described in Table 1.2. From 54% energy consumption of data plane, the buffer management weighs 5%, the packet processing weighs about 32%; the network interfaces weigh about 7%; and the switching fabric for about 10%. This estimation work provides a clear indication for developers in order to increase the energy-saving level of networks in the further researches. 8 1.2.2 Energy-Efficient Approaches' Classification From the general point of view, existing approaches are founded on few basic concepts. As shown in surveys of Raffaele Bolla et al. [4] and Aruna Banzino et al. [21], the largest part of undertaken energy-efficient concepts is founded on few energy-saving mechanisms and power management criteria that are already partially available in computing systems. These approaches, which are depicted in the Table 1.3, are classified as (1) re-engineering; (2) dynamic adaptation; and (3) smart sleeping [4]. Table 1.3: Classification of energy-efficient approaches of the future Internet [4] 1.2.2.1 Re-Engineering The re-engineering approaches focus on introducing and designing more energy-efficient elements inside network equipment architectures. Novel technologies mainly consist of new silicon (ex: for Application Specific Integrated Circuits (ASICs) [22], Field Programmable Gate Arrays (FPGAs) [23], etc.) and memory technologies (ex: Ternary ContentAddressable Memory (TCAM), etc.) for packet processing engines, and novel network media technologies (energy-efficient lasers for fiber channel, etc.). The approaches can be divided into two sub-approaches as follows: (1) energy-efficient silicon which focuses on developing new silicon technologies [24]; and (2) complexity reduction which focuses on reducing equipment complexity in terms of header processing, buffer size, switching fabric speedup and memory access bandwidth speedup [25] [26]. 1.2.2.2 Dynamic Adaptation The dynamic adaptation approaches of network resources are aimed at modulating capacities of devices (working speeds, computational capabilities of packet processing…) according to the current traffic demand [4]. These approaches are founded on two main kinds of power management capabilities provided by the hardware level, namely power scaling and idle logic. Power scaling capabilities allow dynamically reducing the working rate of processing engines or of link interfaces [27] [28]. This is usually accomplished by tuning the clock frequency and/or the voltage of processors, or by throttling the CPU clock (i.e., the clock signal is gated or disabled for some number of cycles at regular intervals). On the other hand, idle logic allows reducing power consumption by rapidly turning off sub-components when no activities are performed, and by re-waking them up when the system receives new activities. In detail, wake-up instants may be triggered by external events in a pre-emptive 9