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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:
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 MƠI TRƯỜNG ĐIỆN TỐN ĐÁM MÂY SDN-BASED ENERGY-EFFICIENT NETWORKING IN CLOUD COMPUTING ENVIRONMENTS DOCTORAL THESIS OF TELECOMMUNICATIONS ENGINEERING HANOI - 2018 CONTENTS LIST OF FIGURES viii LIST OF TABLES x INTRODUCTION CHAPTER AN OVERVIEW OF ENERGY-EFFICIENT APPROACHES IN CLOUD COMPUTING ENVIRONMENTS 1.1 Today's Internet 1.1.1 Cloud Computing Services and Infrastructures 1.1.2 Energy consumption problems 1.2 An Overview of Energy-Efficient Approaches 1.2.1 Energy consumption characteristics 1.2.2 Energy-Efficient Approaches' Classification 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 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 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 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 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] 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] 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] 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 = 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] Table 1.2: Estimated power consumption sources in a generic platform of IP router Table 1.3: Classification of energy-efficient approaches of the future Internet [4] 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 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, - 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 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] Next, the average power consumption of a VDC is evaluated, which is calculated by dividing the total power consumption of the physical DC by the number of served VDCs As can be seen in the Figure 4.15, when the system load increases: - - (1) the consumed power per each VDC is decreasing; (2) the consumed power of the proposed algorithms is much the same and is less than both SN and GN; (3) power consumption of the proposed algorithms decreases very slowly, which implies that power consumption of the physical DC stays nearly linear to the number of embedded VDCs, following the energy proportional property (see Sec 4.1.3); and (4) although the total power consumption of GH is the lowest (Figure 4.14), its average power consumption per VDC is the highest one due to the fact that GH can host only a very limited number of VDCs (Figure 4.15) Figure 4.15: Average consumed power per serving VDC 4.5.2.4 Complexity The average number of migrations for each strategy under different load situation is used as the metric to evaluate complexity As the migration of a virtual machine to a physical server takes time, a high number of migrations reduces the system performance significantly As expected, the number of migrations in FM under highly loaded situation (90%) can be as high as 9000 times (Figure 4.16) while the number of migrations in PM is under 100 times, independent of the load as only underutilized servers are required to consolidate 89 Figure 4.16: Number of migrations for different strategies 4.5.2.5 Discussions Finally, general comparison for the aforementioned strategies can be drawn As shown in the above numerical results, SecondNet performs well in terms of resource efficiency but it does not satisfy the requirement on energy-efficiency In contrast, GreenHead is energy-efficient in some sense, however, its utilization is very low in comparison to other methods Figure 4.17: Comparison of embedding - migration strategies On the other hand, the three new approaches PM, MoA and FM perform well in terms of both resource and energy efficiency Moreover, PM is simpler as the number of required migrations is much less, independent of system load when compared with MoA and FM The radar graphs in Figure 4.17 and Figure 4.18 illustrates this comparative analysis In general, 90 PM can be the most suitable strategy as it performance is nearly as good as FM and MoA and is much simpler 4.18: Different embedding-magrition strategies: (a) GreenHead, (b) SecondNet, (c) Partial Migration, (d) Migration on Arrival, (e) Full Migration 4.6 Conclusion This work firstly analyzes the resource fragmentation problem occurred when virtual data centers continuously join and leave the physical data center and its impact on the efficiency of VDC embedding algorithms Different joint VDC mapping and VM migration strategies are proposed to tackle that dynamic problem In comparison to some previous resource and energy-aware VDC embedding algorithms, the new strategies can remarkably improve both resource utilization and energy efficiency of the data center, while the complexity is kept at an acceptable level 91 CHAPTER CONCLUSION AND FUTURE WORK Many Telcos, Internet Service Providers (ISPs) and enterprises, have significantly employed large network infrastructures for the Internet services A large system consumes a huge energy volume, so that the network energy efficiency problem is very important recently Resolving energy-saving problems brings many advantages including: - Environmentally, it reduces the large amount of carbon emission from ICT sector; Economically, reducing energy consumption of the ICT data centers leads to reducing the costs of maintaining system Consequently, the Internet services’ cost will be reduced 5.1 Major contributions By using Software-defined Networking, energy-efficient approaches are studied in the network in several cloud DC environments such as: (1) in data center network that uses the promising DC topology, namely Fat-tree; (2) in the network virtualization concept; and (3) in the data center virtualization technology The contributions of this research are summarized as follows In the second chapter, the proposed SDN-based power-control system is presented This PCS platform allows administrators to monitor, control, and apply several energy-efficient algorithms This second chapter also presents two main energyefficient approaches including: (1) energy-aware routing algorithm, namely power scaling and energy-profile-aware (PSnEP) algorithm, which is based on the power scaling algorithm and the power profile of a network device; and (2) topology-aware VM migration algorithm which migrates servers with two objectives: (a) minimizing the number of physical servers; and (b) reducing the number of switches for interconnecting these physical servers in order to turn-off more devices for energy efficiency The main advantage of this algorithm is that the migration process performs energy saving of servers as other common migration strategy, known as first-fit, while reducing the energy consumption of the network devices The experimental results show that the consumed power of the network devices can be saved up to 46% while remaining the energy-saving level of the servers An energy-aware network virtualization concept is described in the next chapter with its power monitoring and controlling abilities for cloud environments The proposed concept is based on the SDN technology and allows researchers to develop several energy-efficient virtual network embedding algorithms Two proposed energy-efficient embedding algorithms are proposed, namely heuristic energy-efficient node mapping and reducing middle node energy efficiency, with their experimental results of performance The SDN-based Energy-aware VDC approaches for cloud environments is presented in the fourth chapter The VDC technology is described in detail with its main problem, namely VDC embedding By integrating with VM consolidation technique, the joint VDC embedding and VM migration algorithms is successfully deployed These algorithms with their experimental results are described in this chapter in details 92 5.2 Future research directions Although network energy efficiency has been attracted much attention from the research community, there are many difficulties to realize these technologies and transfer to the industrial market So that in the future work, we are going to establish the following tasks: - Realizing the energy efficiency of a data center network by using new cloud platform – OpenStack OpenStack software controls large pools of compute, storage, and networking resources throughout a datacenter, managed through a dashboard or via the OpenStack API OpenStack works with popular enterprise and open source technologies making it ideal for heterogeneous infrastructure [90] The OpenStack Platform contains the SDN controller - OpenDayLight and the compute management, which manage the VM provisioning and migration process - Developing the network virtualization and data center virtualization for detailed estimating the delay and packet loss In the near future, the Internet services with their characteristics will be embedded into the system Based on the services’ demand as well as their parameters such as downtime, latency and reliability, the system allocates its resources reasonably 93 LIST OF PUBLICATIONS Journals Thanh Nguyen Huu, Anh-Vu Vu, Duc-Lam Nguyen, Van-Huynh Nguyen, ManhNam Tran, Quynh-Thu Ngo, Thu-Huong Truong, Tai-Hung Nguyen, Thomas Magedanz (2015) “A Generalized Resource Allocation Framework in Support of MultiLayer Virtual Network Embedding based on SDN”, Elsevier - Computer Networks, 2015 - https://doi.org/10.1016/j.comnet.2015.09.042 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 - https://doi.org/10.1016/j.comnet.2017.06.007 Book Chapter 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/10.1007/978-3319-49073-1_54 Conferences Nguyen Huu Thanh, Bui Dinh Cuong, To Duc Thien, Pham Ngoc Nam, Ngo Quynh Thu, Truong Thu Huong, and Tran Manh Nam (2013) “ECODANE: A Customizable Hybrid Testbed for Green Data Center Networks”, The International Conference on Advanced Technologies for Communications 2013 (ATC'13) - IEEE, Hanoi, Vietnam Tran Manh Nam, Tran Hoang Vu, Vu Quang Trong, Nguyen Huu Thanh, Pham Ngoc Nam (2013) “Implementing Rate Adaptive Algorithm in Energy-Aware Data Center Network”, National Conference on Electronics and Communications (REV2013-KC01)., Hanoi, Vietnam Tran Manh Nam, Truong Thu Huong, Nguyen Huu Thanh, Pham Van Cong, Ngo Quynh Thu, Pham Ngoc Nam (2014) “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, Nguyen Huu Thanh, Ngo Quynh Thu and Hoang Trung Hieu, Stefan Covaci (2015) “Energy-Aware Routing based on Power Profile of Devices 94 in Data Center Networks using SDN”, 12th Electrical Engineering/Electronics, Computer, Telecommunications And Information Technology Conference (ECTICON) - 2015, Hua Hin, Thailand Tran Manh Nam, Nguyen Huu Thanh, Nguyen Hong Van, Kim Bao Long, Nguyen Van Huynh, Nguyen Duc Lam, Nguyen Van Ca (2015) “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) Tran Manh Nam, Nguyen Huu Thanh, Doan Anh Tuan (2016) “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 Van Huynh, Le Quang Dai, Nguyen Huu Thanh (2016) “An Energy-Aware Embedding Algorithm for Virtual Data Centers”, ITC28 International Teletraffic Congress, Sep - 2016, Wurzburg, Germany 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) 95 REFERENCES [1] http://www.internetworldstats.com/stats.htm, "Usage and Population Statistics," [Online] [2] "Global Action Plan, An Inefficient Truth, Global 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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... Network Function Virtualization (NFV) [31]; Virtual Data Center (VDC) [32] are booming and are strongly implemented in cloud environments [8] [9] [10] On the way to realize these technologies... while running well below capacity most of the time [42] Consequently, the performance of a DCN strongly depends on the topology optimizing and traffic routing This property also helps improving