NFV and SDN: The enablers for elastic networks

Transcription

1 NFV and SDN: The enablers for elastic networks Kashif Mahmood, Next Generation Network Technology (NGNT) Group, Telenor Research, Norway UNIK Guest Lecture, 8 th May 2014

2 Outline Challenges What is coming The Network of today Elastic Networks Way forward Enablers for Elastic networks NFV / SDN Use cases vcran vepc vcdn Conclusion and Outlook 2

3 Outline Challenges What is coming The Network of today Elastic Networks Way forward Enablers for Elastic networks NFV / SDN Use cases vcran vepc vcdn Conclusion and Outlook 3

4 Challenges: Video dominated exponential data growth Voice to data *Source : Ericson mobility report 4 *Traffic does not include DVB-H, Wi-Fi, or Mobile WiMax. Voice does not include VoIP.

5 Challenges: Video dominated exponential data growth Voice to data Exponential data growth *Source : Ericson mobility report 5 *Traffic does not include DVB-H, Wi-Fi, or Mobile WiMax. Voice does not include VoIP.

6 Challenges: Video dominated exponential data growth Voice to data Exponential data growth Mobile video traffic 69% of data traffic by the end of 2018 ~CVI Roughly a quarter of Netflix subscribers already streaming video to their smartphones, ~RCR Wireless *Source : Ericson mobility report 6 *Traffic does not include DVB-H, Wi-Fi, or Mobile WiMax. Voice does not include VoIP.

7 Challenges: Traffic growth Video dominated exponential data growth Time Diverse traffic 7

8 Challenges: Diverse traffic Traffic type : Emergency services, Machine to Machine, IoT Signaling storm Always on nature of mobile networks 8

9 Challenges: Diverse traffic Traffic type : Emergency services, Machine to Machine, IoT Signaling storm Always on nature of mobile networks 9

10 Challenges: Traffic growth Video dominated exponential data growth Time Diverse traffic Ultra dense heterogeneous networks *Soure: Prof Panagiotis in Expert workshop, Stuttgart 10

11 Challenges: Ultra dense heterogeneous networks *Soure: Prof Panagiotis in Expert workshop, Stuttgart 3-10 or more small cells per macro cells cells shrinking (from macro to no cell concept) 11

12 Challenges: Traffic growth Video dominated exponential data growth Time Diverse traffic Ultra dense heterogeneous networks *Soure: Prof Panagiotis in Expert workshop, Stuttgart No more competition on the basis of network coverage but on the basis of features and services. 12

13 Outline Challenges What is coming The Network of today Elastic Networks Way forward Enablers for Elastic networks NFV / SDN Use cases vcran vepc vcdn Conclusion and Outlook 13

14 The Network of today HSS S1-c UE enb X2 S1-u MME S11 PCRF S5 SGi-LAN IP network enb SGW PGW Access core 14

15 The Network of today HSS S1-c RRM UE UE enb X2 enb S1-u MME S11 SGW S5 Radio S5 PGW PCRF SGi-LAN Layer :1 Physical layer SGi-LAN IP network Layer : 3 RRC Layer :2 PDCP RLC MAC Access core 15

16 The Network of today Controls flow-based charging functionalities in PGW Ensures compliance with users subscription HSS S1-c UE enb X2 S1-u MME S11 PCRF S5 SGi-LAN IP network enb SGW PGW Access Core 16

17 The Network of today HSS UL and DL service level charging (input from PCRF, DPI) IP address allocation for UE QoS enforcement Mobility anchor for non-3gpp technologies (CDMA, WiMAX) S1-c UE enb X2 S1-u MME S11 PCRF S5 SGi-LAN IP network enb SGW PGW Access core 17

18 The Network of today HSS Local Mobility anchor when UE moves between enbs Temporarily buffers data when MME initiates paging of UE Mobility anchor for GPP technologies (GPRS, UMTS) S1-c UE enb X2 S1-u MME S11 PCRF S5 SGi-LAN IP network enb SGW PGW Access core 18

19 The Network of today S1-c HSS Processes signaling between UE and core Coordination and scheduling of resources related to location of UE Establishment, maintenance and release of the bearers Functions related to inter-working with other networks e.g. to handle voice calls, CSFB (Circuit switch fall back) Signaling security UE enb X2 S1-u MME S11 PCRF S5 SGi-LAN IP network enb SGW PGW Access core 19

20 The Network of today HSS Users subscription data such as QoS profile Information about which PDNs can it connect to Authentication S1-c UE enb X2 S1-u MME S11 PCRF S5 SGi-LAN IP network enb SGW PGW Access core 20

21 The Network of today HSS S1-c UE enb X2 S1-u MME S11 PCRF UE S5 SGi-LAN IP network UE enb SGW PGW Access core UE UE UE Access TCP Opt Video opt. DPI core Firewall 21

22 Characteristic of the Network of today Topologically fixed network nodes Many redundant functions repeated in each black box Takes a long tome to roll out new services New services means new equipment NOT application aware. policy auth auth policy policy Statistics of the network difficult to obtain due to the distributed nature of network Control logic in each device difficult to upgrade Designed for maximum load but traffic load is fluctuating 80% of base stations (BS) capacity and upto half of core networks capacity unused,~softepc, ICC 13 22

23 Characteristic of the Network of today Topologically fixed network nodes Many redundant functions repeated in each black box Takes a long tome to roll out new services New services means new equipment NOT application aware. Statistics of the network difficult to obtain due to the distributed nature of network Control logic in each device difficult to upgrade Designed for maximum load but traffic load is fluctuating 80% of base stations (BS) capacity and upto half of core networks capacity unused,~softepc, ICC 13 23

24 Characteristic of the Network of today Topologically fixed network nodes Many redundant functions repeated in each black box Takes a long tome to roll out new services New services means new equipment NOT application aware. Statistics of the network difficult to obtain due to the distributed nature of network Control logic in each device difficult to upgrade Designed for maximum load but traffic load is fluctuating 80% of base stations (BS) capacity and upto half of core networks capacity unused,~softepc, ICC 13 24

25 Characteristic of the Network of today Topologically fixed network nodes Many redundant functions repeated in each black box Takes a long tome to roll out new services New services means new equipment NOT application aware. Statistics of the network difficult to obtain due to the distributed nature of network Control logic in each device difficult to upgrade Designed for maximum load but traffic load is fluctuating 80% of base stations (BS) capacity and upto half of core networks capacity unused,~softepc, ICC 13 25

26 Characteristic of the Network of today Topologically fixed network nodes Many redundant functions repeated in each black box Takes a long tome to roll out new services New services means new equipment NOT application aware. Statistics of the network difficult to obtain due to the distributed nature of network Control logic in each device difficult to upgrade Designed for maximum load but traffic load is fluctuating 80% of base stations (BS) capacity and upto half of core networks capacity unused,~softepc, ICC 13 26

27 Characteristic of the Network of today Topologically fixed network nodes Many redundant functions repeated in each black box Takes a long tome to roll out new services New services means new equipment NOT application aware. Statistics of the network difficult to obtain due to the distributed nature of network Control logic in each device difficult to upgrade Designed for maximum load but traffic load is fluctuating 80% of base stations (BS) capacity and upto half of core networks capacity unused,~softepc, ICC 13 Traffic and revenue 27

28 Traffic and Revenue : Traditional Networks Traffic Voice Era Data Era Time *Trend from unstrung Pyramid research 28

29 Traffic and Revenue : Traditional Networks Traffic Voice Era Gap widening revenue Data Era Time *Trend from unstrung Pyramid research 29

30 Traffic and Revenue : Traditional Networks Traffic Voice Era revenue Data Era Time *Trend from unstrung Pyramid research 30

31 Traffic and Revenue : Elastic networks Enablers? Traffic revenue Voice Era Data Era Time *Trend from unstrung Pyramid research 31

32 Outline Challenges What is coming The Network of today Elastic Networks Way forward Enablers for Elastic networks NFV / SDN Use cases vcran vepc vcdn Conclusion and Outlook 32

33 Traditional networks Elastic Networks HSS MME PCRF Elastic network enb IP SGW PGW Traditional network 33 33

34 Traditional networks Elastic Networks HSS MME PCRF Elastic network Can scale up and down as well as move resources enb IP HSS SGW PGW Traditional network MME PCRF enb SGW PGW tcp.opt v.opt FW IP DPI 34 34

35 Traditional networks Elastic Networks HSS MME PCRF Elastic network Can scale up and down as well as move resources enb IP HSS SGW PGW Traditional network MME PCRF enb SGW PGW PGW tcp.opt v.opt FW FW FW IP DPI 35 35

36 Traditional networks Elastic Networks HSS MME PCRF Elastic network Can scale up and down as well as move resources enb IP HSS SGW PGW Traditional network MME PCRF enb SGW PGW PGW tcp.opt v.opt FW FW FW IP DPI Enablers: NFV, SDN

37 Outline Challenges What is coming The Network of today Elastic Networks Way forward Enablers for Elastic networks NFV / SDN Use cases vcran vepc vcdn Conclusion and Outlook 37

38 Enablers: NFV and SDN NFV SDN Traffic revenue Voice Era Data Era Time *Trend from unstrung Pyramid research 38

39 Network function Virtualization (NFV) Network functions are implemented on virtual networks VNFs Almost the same features but more flexibility. Uses custom of the shelf (COTS) equipment VNF 1 VNF 2 PCRF SGW VNF N MME Architecture NFV 39

40 Architecture of network virtualization Virtual Network Functions (VNFs) VNF VNF VNF VNF VNF *source : ETSI 40

41 Architecture of network virtualization Virtual Network Functions (VNFs) VNF VNF VNF VNF VNF NFV Infrastructure (NFVI) *source : ETSI 41

42 Architecture of network virtualization Virtual Network Functions (VNFs) VNF VNF VNF VNF VNF NFV Infrastructure (NFVI) Virtual Compute Virtual Storage Virtual Network *source : ETSI 42

43 Architecture of network virtualization Virtual Network Functions (VNFs) VNF VNF VNF VNF VNF NFV Infrastructure (NFVI) Virtual Compute Virtual Storage Virtual Network Compute Storage Network Hardware resources *source : ETSI 43

44 Architecture of network virtualization Virtual Network Functions (VNFs) VNF VNF VNF VNF VNF NFV Infrastructure (NFVI) Virtual Compute Virtual Storage Virtualization Layer Virtual Network Compute Storage Network Hardware resources *source : ETSI 44

45 Architecture of network virtualization Virtual Network Functions (VNFs) VNF VNF VNF VNF VNF Virtual Compute NFV Infrastructure (NFVI) Virtual Storage Virtualization Layer Virtual Network NFV Management and Orchestration Compute Storage Network Hardware resources 45

46 Network service VNF VNF VNF VIM/NW controller Virtual Network Functions (VNFs) VNF VNF VNF VNF VNF Customer self service portal Virtual Compute NFV Infrastructure (NFVI) Virtual Storage Virtualization Layer Virtual Network NFV Management and Orchestration Compute Storage Network Hardware resources 46

47 Network service VNF VNF VNF Virtual Network Functions (VNFs) VIM Network controller VNF VNF VNF VNF VNF Customer self service portal Virtual Compute NFV Infrastructure (NFVI) Virtual Storage Virtualization Layer Virtual Network NFV Management and Orchestration Compute Storage Network Hardware resources One of the ways to control the network? 47

48 Network service VNF VNF VNF Virtual Network Functions (VNFs) VIM SDN controller VNF VNF VNF VNF VNF Customer self service portal Virtual Compute NFV Infrastructure (NFVI) Virtual Storage Virtualization Layer Virtual Network NFV Management and Orchestration Compute Storage Network Hardware resources SDN : One of the ways to control the network 48

49 Enabler: Software Defined Networking (SDN) Separate the data plane and the control plane API API API North bound interface (REST) SDN Controller South bound interface (OpenFlow) Data plane 49

50 SDN at work! Speaking at the Open Networking Summit in April 2012, Urs Holzle, senior vice-president of technical infrastructure at Google said The company can now prioritize certain traffic, such as Gmail backups, to ensure they get through in a timely manner Perhaps most impressively, Holzle told the conference that, in using SDN to intelligently manage the flow of traffic through its internal network, Google will eventually hit 100% network utilization. In an industry where 30% to 40% is considered standard, that is a huge performance increase. 50

51 Outline Challenges What is coming The Network of today Elastic Networks Way forward Enablers for Elastic networks NFV / SDN Use cases vcran vepc vcdn Conclusion and Outlook 51

52 vcran EPC Internet Backhaul BBU pool BBU pool VNF 1 VNF 1 VNF 1 VNF 1 VNF N VNF N x2 VNF N VNF N Fronthaul CPRI* CWDM, Microwave (mm) micro RRH RRH macro RRH pico pico macro micro BBU Base Band Unit RRH Remote Radio Head CPRI Common Public Radio Interface 52

53 vcran Why A generic network, supporting different tech 3G, WiFi, 4G, XG and different applications Allocating spectrum resources dynamically 53

54 vcran Why A generic network, supporting different tech 3G, WiFi, 4G, XG and different applications Allocating spectrum resources dynamically LTE-A will use network MIMO coordinated scheduling Interference management CRAN 54

55 vcran Why A generic network, supporting different tech 3G, WiFi, 4G, XG and different applications Allocating spectrum resources dynamically LTE-A will use network MIMO coordinated scheduling Interference management CRAN Liberty of using the slice specific to the type of application and technology 55

56 vcran Why A generic network, supporting different tech 3G, WiFi, 4G, XG and different applications Allocating spectrum resources dynamically LTE-A will use network MIMO coordinated scheduling Interference management CRAN Liberty of using the slice specific to the type of application and technology RAN sharing Active Only cell sites Passive In addition to cell sites radio spectrum and baseband processing unit is shared 56

57 vcran Why A generic network, supporting different tech 3G, WiFi, 4G, XG and different applications Allocating spectrum resources dynamically LTE-A will use network MIMO coordinated scheduling Interference management CRAN Liberty of using the slice specific to the type of application and technology RAN sharing Active Only cell sites Passive In addition to cell sites radio spectrum and baseband processing unit is shared Age old algorithms on user mobility and traffic patterns can be put into practice leveraging global view and efficient monitoring tools of SDN CRAN Green RAN 57

58 vcran, Challenges For centralized CRAN architecture High BW requirement to carry baseband I/Q between BBU and RRH Fairness in power, spectrum or product of two Fully centralized architecture: requires high capacity fronthaul Integration with backhaul. Backhaul CPRI CWDM, Microwave? Coordination Fronthaul Low latency and near zero jitter Virtualized solution vs DSP 58

59 Outline Challenges What is coming The Network of today Elastic Networks Way forward Enablers for Elastic networks NFV / SDN Use cases vcran vepc vcdn Conclusion and Outlook 59

60 60 vepc

61 vcdn VoIP Internet core GW GW GW CDN backhaul Access 61

62 vcdn VoIP Internet core GW GW GW backhaul CDN Access CDN 62

63 In a Nutshell Reduction in CAPEX and OPEX. Cost effective Commodity hardware a plentiful and scalable resource. Address new requirements through new software not new equipment. Fast service delivery Bandwidth on demand services Integrated RAN Each slice for different traffic type and technology. Selected by SDN controller. The slice can be scaled up/down NFV Resource pool in the cloud on VMs NFV New abstraction view of networks possible ~ courtesy SDN Challenge: Cost and Performance analysis of virtualizing network functions 63

64 In a Nutshell Reduction in CAPEX and OPEX. Cost effective Commodity hardware a plentiful and scalable resource. Address new requirements through new software not new equipment. Fast service delivery Bandwidth on demand services Integrated RAN Each slice for different traffic type and technology. Selected by SDN controller. The slice can be scaled up/down NFV Resource pool in the cloud on VMs NFV New abstraction view of networks possible ~ courtesy SDN Challenge: Cost and Performance analysis of virtualizing network functions 64

65 Cost and Performance Analysis : Expected Outcome MGW CDN Cost Benefit IMS MME DPI Voice Service PCRF Performance Contributors

66 Performance Analysis Low level variable Link utilization Description Percentage of total link utilization between two points in the network. VNFs can be located at different locations in the network Impacts link utilization. Strategic Results variable Cost, performance Resource utilization Energy consumption NFV has the potential to reduce resource utilization since multiple VNFs can be installed on COTS hardware. How will the virtualisation of the function impact utilization of the hardware, i.e. router, switches, servers and storage? Can be reduced by consolidating equipment and exploit power management features in standard servers and storage, as well as workload consolidation and location optimization. Cost, performance Cost, performance 66

67 Performance Analysis Low level variable Link utilization Description Percentage of total link utilization between two points in the network. VNFs can be located at different locations in the network Impacts link utilization. Strategic Results variable Cost, performance Resource utilization Energy consumption NFV has the potential to reduce resource utilization since multiple VNFs can be installed on COTS hardware. How will the virtualisation of the function impact utilization of the hardware, i.e. router, switches, servers and storage? Can be reduced by consolidating equipment and exploit power management features in standard servers and storage, as well as workload consolidation and location optimization. Cost, performance Cost, performance Portability Elasticity Concerns the capabilities to load, execute and move software functions across different but standard data centres and network locations. Concerns the capabilities to provide an easier way to scale up/down and in/out hardware and software resources as traffic demands increase/decreases. Cost Cost 67

68 Performance Analysis Low level variable Link utilization Description Percentage of total link utilization between two points in the network. VNFs can be located at different locations in the network Impacts link utilization. Strategic Results variable Cost, performance Resource utilization Energy consumption NFV has the potential to reduce resource utilization since multiple VNFs can be installed on COTS hardware. How will the virtualisation of the function impact utilization of the hardware, i.e. router, switches, servers and storage? Can be reduced by consolidating equipment and exploit power management features in standard servers and storage, as well as workload consolidation and location optimization. Cost, performance Cost, performance Portability Elasticity Concerns the capabilities to load, execute and move software functions across different but standard data centres and network locations. Concerns the capabilities to provide an easier way to scale up/down and in/out hardware and software resources as traffic demands increase/decreases. Cost Cost Realtime properties Security Delay, jitter, latency etc. How will the transition from physical to virtual network functions impact security? The role of SDN as a centralized controller could have an effect on security Performance Performance 68

69 Performance Analysis : Expected Outcome CCCCCCCCCCC MMM = w MMM CB,LL. LLLL UUUUUUUUUUU + w MMM CC,PPP. PPPPPPPPPPP + PPPPPPPPPPP MMM = w MMM PPP,LU. LLLL UUUUUUUUUUU + w MMM PPP,EE. EEEEEEEEEEEEEEEEE + CCCCCCCCCCC PPPP = w PPPP CC,LL. LLLL UUUUUUUUUUU + w PPPP CC,PPP. PPPPPPPPPPP + PPPPPPPPPPP PPPP = w PPPP PPP,LL. LLLL UUUUUUUUUUU + w PPPP PPP,EE. EEEEEEEEEEEEEEEEE + MGW CDN Cost Benefit IMS MME DPI Voice Service PCRF Performance

70 Final-line Challenges What is coming The Network of today Elastic Networks Way forward Enablers for Elastic networks NFV / SDN Use cases vcran vepc vcdn Conclusion 70

71 Thank You Next Generation Network Technology Group, Telenor research, Norway