Doctoral thesis of telecommunications engineering: SDN-based energy-efficient networking in cloud computing environments

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Software-defined networking 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.

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Nội dung Text: Doctoral thesis of telecommunications engineering: SDN-based energy-efficient networking in cloud computing environments

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 SDNBASED ENERGYEFFICIENT 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 SDNBASED ENERGYEFFICIENT 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
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
CONTENTS
ABBREVIATIONS APCI Advanced Configuration & Power Interface APEX Capital expenditure ASIC Application specific integrated circuits BAU Businessasusual BFS Breadthfirst Search CAPEX Capital Expenditure DC Data center DCN Data center network DITG Distributed internet traffic generator EANV Energyaware network virtualization EAVDC Energyaware Virtual Data Center ECO Eco sustainable FM Full migration FPGA Field programmable gate arrays GH GreenHead HEAE Heuristic Energyaware VDC Embedding HEE Heuristic energyefficient IaaS Infrastructureasaservice ICT Information and communication technologies ISP Internet service provider MoA Migrate on arrival MST Minimum spanning tree NaaS Networkasaservice NFV Network function virtualization NV Network virtualization OLD OpenDayLight OPEX Operating expenses PaaS Platformasaservice PCS PowerControl System PM Partial migration POD Optimized data centers PSnEP Power scaling and energyprofileaware RMDEE Reducing middle node energy efficiency SaaS Softwareasaservice SDSN SoftwareDefined Substrate Network SN SecondNet SNMP Simple network management protocol TCAM Ternary contentaddressable memory VDC Virtual data center VDCE Virtual data center embedding VLiM Virtual link mapping VM Virtual Machine
VmM Virtual machine mapping VNE Virtual network embedding VNoM Virtual node mapping VNR Virtual network requests
LIST OF FIGURES LIST OF TABLES
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 Dec2013 to 51.7% in June2017 [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 cloudservices such as YouTube, Dropbox, elearning, 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 ITCsector 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 everincreasing cost of energy. Although network energy efficiency has recently attracted much attention from communities [4], there are still many issues in realization of the energyefficient network including inflexibility and the lack of an energyaware 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 redesign, reconfigure 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 energysaving levels of new energyefficient approaches in a network due to the lack of the centralized powercontrol 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 realtime. Energyaware 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 InfrastructureasaService (IaaS), StorageasaService (SaaS), PlatformasaService (PaaS), NetworkasaService (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 Softwaredefined 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 energyefficient approaches are based on the Softwaredefined Networking technology [11] [12] [13]. b) Research Methodology: the research methodology is used following the reference [14]. Step 1: Problem formulation: 1. Interrogative form 2. Describe relations among constructs Step 2: Hypothesis formulation: answering to problem statements Step 3: Research design: building research plan for a research process including survey, related work and experiments Step 4: Sampling and Data Collection Step 5: Data analysis Step 6: Manuscript Writing 3. Contributions and Structure of the Dissertation Recently, Softwaredefined 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, SDNbased energyefficient 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 energyefficient network approaches. The first contribution of this dissertation is resolving the lack of energyaware network in a DC by (1) proposing a SDNbased powercontrol 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 energyefficient 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 Networkasaservice (NaaS), Infrastructureasaservice (IaaS). The energyaware NV platform is necessary for network energy efficiency. Appropriately, (2) the SDNbased energyaware network
virtualization (EANV) platform is proposed in this dissertation. The platform is aware of power consumption of the network virtualization environment. Two novel energyefficient virtual network embedding algorithms are also proposed and implemented in this platform that focus on increasing the energysaving 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 energyaware virtual data center platform is deployed. On this system, novel energyaware algorithms are also proposed which focus on the following objectives: (1) resource 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 SDNbased network energyefficient approaches which are presented in five chapters as follows: The first chapter presents an overview of energyefficient network in cloud environments and their classification. The difficulties of the network’s energy efficiency area as well as the background of the Softwaredefined Networking technology are also described in details. In the second chapter, a SDNbased powercontrol system (PCS) of a data center network is proposed. Based on this platform, developers can propose, implement and evaluate several network energysaving algorithms. Two energyefficient 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 PowerManagement 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, “EnergyAware Routing based on Power Profile of Devices in Data Center Networks using SDN”, the 12th IEEE ECTICON conference 2015, HuaHin, Thailand Achieved a student Grant of ECTICON, Jun, 2015. Tran Manh Nam, Truong Thu Huong, Nguyen Huu Thanh, Pham Van Cong, Ngo Quynh Thu, Pham Ngoc Nam, “A Reliable Analyzer for EnergySaving 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 EnergyAware Data Center Network”, National Conference on Electronics and Communications (REV2013KC01)., Hanoi, Vietnam. The third chapter describes an energyaware network virtualization concept and its power monitoring and controlling abilities. The proposed concept is SDNbased which allows developers to implement several energyefficient virtual network embedding algorithms. Two energyefficient embedding algorithms, namely heuristic energyefficient node mapping and reducing middle node energy efficiency, are proposed in this section. The results and algorithms of this chapter are published in: 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 AsiaPacific Advanced Network Research Workshop (APANNRW), August 10th 14th 2015, Kuala Lumpur, Malaysia (best student paper award). Thanh Nguyen Huu, AnhVu Vu, DucLam Nguyen, VanHuynh Nguyen, ManhNam Tran, QuynhThu Ngo, ThuHuong Truong, TaiHung 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/10.1007/9783319490731_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 (ICIDB2018), Hanoi, Vietnam. (presented) SDNbased Energyaware 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 EnergyAware 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 EnergyEfficient 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.
CHAPTER 1. AN OVERVIEW OF ENERGY-EFFICIENT APPROACHES IN CLOUD COMPUTING ENVIRONMENTS This chapter provides an overview of the Internet status nowadays and the energy efficient 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 SoftwareDefined 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.¸ 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 Dec2013 to 51.7% in June2017 [1]. Table 1.: The Internet’s users in the world [1] Number of World population’s Date users percentage Dec, 2013 2,802 millions 39.0 % Dec, 2014 3,079 millions 42.4 % Dec, 2015 3,366 millions 46.4 % Dec. 2016 3,696 millions 49.5 % June. 2017 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:
Figure 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 ITCsector is remarkable (Figure 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 everincreasing cost of energy (Figure 1. and Figure 1.). Figure 1.: 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 energy efficient network has become a crucial matter for Telcos and ISPs towards greener cloud environments.
Figure 1.: 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 energyefficient approaches, which are currently undertaken, is also presented. 1.2.1 Energy consumption characteristics Table 1.: 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 energysaving 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.. 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 energysaving level of networks in the further researches.
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 energyefficient concepts is founded on few energysaving mechanisms and power management criteria that are already partially available in computing systems. These approaches, which are depicted in the Table 1., are classified as (1) reengineering; (2) dynamic adaptation; and (3) smart sleeping [4]. Table 1.: Classification of energy-efficient approaches of the future Internet [4] 1.2.2.1 Re-Engineering The reengineering 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 (energyefficient lasers for fiber channel, etc.). The approaches can be divided into two subapproaches as follows: (1) energyefficient 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 rewaking them up when the system
receives new activities. In detail, wakeup instants may be triggered by external events in a preemptive mode (e.g., “wakeonpacket”), and/or by a system internal scheduling process (e.g., the system wakes itself up every certain periods, and controls if there are new activities to process). 1.2.2.3 Sleeping/Standby Sleeping and standby approaches are founded on power management primitives, which allow devices or part of them to turn themselves almost completely off, and enter very low energy states, while all their functionalities are frozen [4]. Thus, sleeping/standby states can be thought as deeper idle states, characterized by higher energy savings and much larger wakeup times. In more detail, the applications and services of a device (or its part) stop working and lose their network connectivity [29] [30] when it goes sleeping. As a result, the sleeping device loses its network ”presence” since it cannot maintain network connectivity, and answer to application/servicespecific messages. Moreover, when the device wakes up, it has to reinitialize its applications and services by sending a non negligible amount of signaling traffic. 1.3 Software-defined Networking (SDN) technology Recently, the future Internet technologies in cloud computing environments such as Softwaredefined Networking [11]; Network Virtualization (NV) [6] [7]; 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 and transfer to the industrial market, the flexible network is mandatory. SDN technology with its characteristics including programmable, capable of centralized management will play very important role in the innovation of all other techniques. In this Section, the overview of the SDN technology is depicted. 1.3.1 SDN Architecture Softwaredefined Networking (SDN) [11] is an emerging networking paradigm that gives hope to change the limitations of current network infrastructures. First, it breaks the vertical integration by separating the network’s control logic (the control plane) from the underlying routers and switches that forward the traffic (the data plane) [33]. Second, with the separation of the control and data planes, network switches become simple forwarding devices and the control logic is implemented in a logically centralized controller (or network operating system1), simplifying policy enforcement and network reconfiguration and evolution. A simplified view of this architecture is shown in Figure 1.. It is important to emphasize that a logically centralized programmatic model does not postulate a physically centralized system. In fact, the need to guarantee adequate levels of performance, scalability, and
reliability would preclude such a solution. Instead, productionlevel SDN network designs resort to physically distributed control planes. The separation of the control plane and the data plane can be done by a welldefined programming interface between the switches and the SDN controller. The controller exercises direct control over the state in the data plane elements via this welldefined application programming interface (API), as depicted in Figure 1.. The most notable example of such an API is OpenFlow [34], [35]. Figure 1.: SDN Architecture 1.3.2 SDN Southbound API - OpenFlow Protocol OpenFlow [34] [35] is the first and also the most widely known SDN protocol for southbound API, it provides the communication protocol between the control plane on SDN controller and the forwarding planes on OpenFlow switches. OpenFlow specifies how these planes communicate and interact with each other since the connection is setup until the end. The OpenFlow protocol is layered above the Transmission Control Protocol, leveraging the use of Transport Layer Security (TLS). The default port number for controllers to listen on is 6653 for switches that want to connect. An OpenFlow switch has one or more tables of packet (Figure 1.) handling rules (flow table). Each rule matches a subset of the traffic and performs certain actions (dropping, forwarding, modifying, etc.) on the traffic. Depending on the rules installed by a controller application, an OpenFlow switch can be instructed by the controller behave like a router, switch, firewall, or perform other roles (e.g., load balancer, traffic shaper, and in general those of a middlebox). A flowtable contains several flow entries, each flow entry consists of three main parts: Match rule: this includes various fields to match on a packet: IP source address, IP destination address, MAC source address, MAC destination address, TCP source port address, etc. A field can be left empty, which means any packets can match with this field. Action: this action is applied to the match packet. Actions include forwarding packet to another port, drop packet, etc. Stats: this part records the number of packet and byte that has matched with this flow entry. It also records the duration from the starting time until current. This stats component is usually used for monitoring and in management functions. Figure 1.: OpenFlow controller and switches When a packet arrives, it will be paired with the first matching flow entry in the flow table. If the packet is not matched with any entries, the switch will send an OpenFlow PacketIn message to the controller which will take appropriate actions afterwards. After