A Machine Learning Based Approach to Mobile Network Analysis
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- Oswin Quinn
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1 A Machine Learning Based Approach to Mobile Network Analysis Zengwen Yuan 1, Yuanjie Li 1, Chunyi Peng 2, Songwu Lu 1, Haotian Deng 2, Zhaowei Tan 1, Taqi Raza 1 1 2
2 Overview 2
3 Overview 2 Background
4 Overview 2 Background Why machine learning for mobile network analysis
5 Overview 2 Background Why machine learning for mobile network analysis Mobile network analysis: state-of-the-a; and our approach
6 Overview 2 Background Why machine learning for mobile network analysis Mobile network analysis: state-of-the-a; and our approach Case study: analyzing latency for mobile networks How mobile apps work over LTE How to breakdown app-perceived latency Challenges and ML scheme Primary results from crowdsourcing
7 Overview 2 Background Why machine learning for mobile network analysis Mobile network analysis: state-of-the-a; and our approach Case study: analyzing latency for mobile networks How mobile apps work over LTE How to breakdown app-perceived latency Challenges and ML scheme Primary results from crowdsourcing Conclusion
8 Ubiquitous cellular networks connect everyone, everything 3
9 The race to 5G opens many new oppocunities 4
10 Yet, access to mobile network analytics is barred 5 What s going on in the 3G/4G/5G network?? User space Cellular network (4G LTE) Researcher (you) Mobile Apps Chipset/Mobile OS Application Stack Mobile OS TCP/IP stack Baseband?? Internet Web server No privilege no talk, sorry! LTE interface Operators Oh we cannot tell you unless you sign an NDA
11 Yet, access to mobile network analytics is barred 5 What s going on in the 3G/4G/5G network?? Researcher (you) User space Mobile Apps Application Stack Mobile OS TCP/IP stack Baseband LTE interface Cellular network (4G LTE) 3G/4G operations remain closed both in device chipsets Chipset/Mobile OS No privilege no talk, sorry! and network infrastructures :(?? Internet Web server Operators Oh we cannot tell you unless you sign an NDA
12 Plus, mobile networks are complex & distributed 6 More complex functions on both control and data planes Operations are distributed across layers User space Mobile Apps Application Stack Session Management (ESM) Mobility Management (EMM) Radio Resource Control (RRC) Application Protocols TCP/IP Protocol Stack Mobile OS TCP/IP stack Baseband LTE interface Packet Convergence (PDCP) Radio Link Control (RLC) Medium Access Control (MAC) Physical Layer Protocols (PHY) Signaling Control Plane Data Plane
13 Moreover, analytics tasks are app specinc 7
14 Moreover, analytics tasks are app specinc 7 Analytics for mobile networks is problem-specicc, for example:
15 Moreover, analytics tasks are app specinc 7 Analytics for mobile networks is problem-specicc, for example: Web browsers: Why the time-to-first-byte (TTFB) is so long? What s the major component of latency?
16 Moreover, analytics tasks are app specinc 7 Analytics for mobile networks is problem-specicc, for example: Web browsers: Why the time-to-first-byte (TTFB) is so long? What s the major component of latency? Instant message apps: Does the recipient read my message? Is my message delivered in time?
17 State-of-the-aC mobile network analytics 8
18 State-of-the-aC mobile network analytics 8 Current 4G network analytics is primarily infrastructure-based :
19 State-of-the-aC mobile network analytics 8 Current 4G network analytics is primarily infrastructure-based : User space Cellular network (4G LTE) Mobile Apps Application Stack Mobile OS Base stations Mobility controller User profile server Web server TCP/IP stack Baseband LTE interface Gateway Gateway Internet
20 State-of-the-aC mobile network analytics 8 Current 4G network analytics is primarily infrastructure-based : User space Cellular network (4G LTE) Mobile Apps Application Stack Mobile OS Base stations Mobility controller User profile server Web server TCP/IP stack Baseband LTE interface Gateway Gateway Internet
21 State-of-the-aC mobile network analytics 8 Current 4G network analytics is primarily infrastructure-based : Not Scalable User space Cellular network (4G LTE) Mobile Apps Application Stack Mobile OS Base stations Mobility controller User profile server Web server TCP/IP stack Baseband LTE interface Gateway Gateway Internet
22 State-of-the-aC mobile network analytics 8 Current 4G network analytics is primarily infrastructure-based : Not Scalable Incomplete View User space Cellular network (4G LTE) Mobile Apps Application Stack Mobile OS Base stations Mobility controller User profile server Web server TCP/IP stack Baseband LTE interface Gateway Gateway Internet
23 State-of-the-aC mobile network analytics 8 Current 4G network analytics is primarily infrastructure-based : Not Scalable Incomplete View Opaqueness User space Cellular network (4G LTE) Mobile Apps Application Stack Mobile OS Base stations Mobility controller User profile server Web server TCP/IP stack Baseband LTE interface Gateway Gateway Internet
24 ML-based approach is a must-have feature for mobile network analytics 9 Not Scalable Incomplete View Opaqueness User space Mobile Apps User space Application Stack Mobile OS Mobile Apps Application Stack Mobile OS TCP/IP stack TCP/IP stack Baseband Baseband LTE interface User space Mobile Apps Application Stack Mobile OS TCP/IP stack Baseband LTE interface LTE interface
25 ML-based approach is a must-have feature for mobile network analytics 9 Device-centric ML approach brings new hope Not Scalable Incomplete View Opaqueness User space Mobile Apps User space Application Stack Mobile OS Mobile Apps Application Stack Mobile OS TCP/IP stack TCP/IP stack Baseband Baseband LTE interface User space Mobile Apps Application Stack Mobile OS TCP/IP stack Baseband LTE interface LTE interface
26 ML-based approach is a must-have feature for mobile network analytics 9 Device-centric ML approach brings new hope Not Scalable Incomplete View Opaqueness Scalability User space Mobile Apps User space Application Stack Mobile OS Mobile Apps Application Stack Mobile OS TCP/IP stack TCP/IP stack Baseband Baseband LTE interface User space Mobile Apps Application Stack Mobile OS TCP/IP stack Baseband LTE interface LTE interface
27 ML-based approach is a must-have feature for mobile network analytics 9 Device-centric ML approach brings new hope Not Scalable Incomplete View Opaqueness Scalability Device QoE View User space Mobile Apps User space Application Stack Mobile OS Mobile Apps Application Stack Mobile OS TCP/IP stack TCP/IP stack Baseband Baseband LTE interface User space Mobile Apps Application Stack Mobile OS TCP/IP stack Baseband LTE interface LTE interface
28 ML-based approach is a must-have feature for mobile network analytics 9 Device-centric ML approach brings new hope Not Scalable Incomplete View Opaqueness Scalability Device QoE View Availability User space Mobile Apps User space Application Stack Mobile OS Mobile Apps Application Stack Mobile OS TCP/IP stack TCP/IP stack Baseband Baseband LTE interface User space Mobile Apps Application Stack Mobile OS TCP/IP stack Baseband LTE interface LTE interface
29 It is probably true that machine learning is a must-have approach, rather than a nice-to-have one, to our Celd for mobile network analysis
30 Our proposal: two-level device-centric ML approach 11 User space Mobile Apps User space Application Stack Mobile OS Mobile Apps Application Stack Mobile OS TCP/IP stack TCP/IP stack Baseband Baseband LTE interface User space Mobile Apps Application Stack Mobile OS TCP/IP stack Baseband LTE interface LTE interface
31 Our proposal: two-level device-centric ML approach 11 Local level: sensing mobile network data inside each sma;phone Via hardware-software coordination (e.g. MobileInsight [ACM MobiCom 16]) Via higher-layer (application/transport/ip) and lower-layer (cellular-specific) integration Via ML-assisted data plane prediction from control plane protocol reconstruction User space Mobile Apps User space Application Stack Mobile OS Mobile Apps Application Stack Mobile OS TCP/IP stack TCP/IP stack Baseband Baseband LTE interface User space Mobile Apps Application Stack Mobile OS TCP/IP stack Baseband LTE interface LTE interface
32 Our proposal: two-level device-centric ML approach 11 Local level: sensing mobile network data inside each sma;phone Via hardware-software coordination (e.g. MobileInsight [ACM MobiCom 16]) Via higher-layer (application/transport/ip) and lower-layer (cellular-specific) integration Via ML-assisted data plane prediction from control plane protocol reconstruction Global level: Crowdsourcing-based dataset User space Cloud-synthesized insights Mobile Apps User space Application Stack Mobile OS Mobile Apps Application Stack Mobile OS TCP/IP stack TCP/IP stack Baseband Baseband LTE interface User space Mobile Apps Application Stack Mobile OS TCP/IP stack Baseband LTE interface LTE interface
33 Local analysis 12 User space Mobile Apps Application Stack Mobile OS TCP/IP stack Baseband LTE interface
34 Local analysis 12 Step 1: open up the black-box operations At/above IP network data: TCPDUMP Below IP network data: MobileInsight User space Mobile Apps Application Stack Mobile OS TCP/IP stack Baseband LTE interface
35 Local analysis 12 Step 1: open up the black-box operations At/above IP network data: TCPDUMP Below IP network data: MobileInsight User space Mobile Apps Application Stack Mobile OS TCP/IP stack Baseband LTE interface
36 Local analysis 12 Step 1: open up the black-box operations At/above IP network data: TCPDUMP Below IP network data: MobileInsight User space Step 2: automated data preprocessing Data cleansing and integration of two sources Mobile Apps Application Stack Mobile OS TCP/IP stack Baseband LTE interface
37 Local analysis 12 Step 1: open up the black-box operations At/above IP network data: TCPDUMP Below IP network data: MobileInsight User space Step 2: automated data preprocessing Data cleansing and integration of two sources Preprocessing Mobile Apps Application Stack Mobile OS TCP/IP stack Baseband LTE interface
38 Local analysis 12 Step 1: open up the black-box operations At/above IP network data: TCPDUMP Below IP network data: MobileInsight User space Step 2: automated data preprocessing Data cleansing and integration of two sources Step 3: local ML-based analysis Control plane for protocol operations Preprocessing Mobile Apps Application Stack Mobile OS TCP/IP stack Baseband LTE interface Data plane for performance
39 Local analysis 12 Step 1: open up the black-box operations At/above IP network data: TCPDUMP User space Below IP network data: MobileInsight Step 2: automated data preprocessing Preprocessing ML analysis Mobile Apps Application Stack Data cleansing and integration of two sources Mobile OS TCP/IP stack Step 3: local ML-based analysis Baseband Control plane for protocol operations LTE interface Data plane for performance
40 Global analysis 13 Enabled by cloud-based crowdsourcing (e.g. cnicloud [HotWireless 17]) Analytical Insights for: Geographical location Fine-grained logging & sharing Operators Phone models Efficient Data Management Structured Query SQL Query SQL Response
41 Case study: latency analysis in mobile networks
42 Every millisecond of latency masers! 15 Mobile network users want fast access 1 second latency in page response 7% reduction in PageView [KissMetrics 2011] Developers lose revenue due to long latency Every 100 ms costs Amazon 1% ($1.6 bn) in sales An extra 400 ms latency drops daily Google searches per user by 0.6% Latency does mazer a lot!
43 Background: how do mobile apps work over 4G LTE? 16 What happens under the hood? How LTE impacts perceived latency on mobile web/im app? Safari WhatsApp mobile OS Application (HTTP/DNS) TCP/IP stack modem chipset LTE interface Internet Web server
44 Background: how do mobile apps work over 4G LTE? 16 What happens under the hood? How LTE impacts perceived latency on mobile web/im app? Safari WhatsApp mobile OS Application (HTTP/DNS) TCP/IP stack modem chipset LTE interface Internet Web server
45 Background: how do mobile apps work over 4G LTE? 16 What happens under the hood? How LTE impacts perceived latency on mobile web/im app? Safari WhatsApp mobile OS Application (HTTP/DNS) TCP/IP stack modem chipset LTE interface? Internet Web server
46 Background: how do mobile apps work over 4G LTE? 16 What happens under the hood? How LTE impacts perceived latency on mobile web/im app? Safari WhatsApp mobile OS Application (HTTP/DNS) TCP/IP stack modem chipset LTE interface? Internet Web server
47 Background: how do mobile apps work over 4G LTE? 16 What happens under the hood? How LTE impacts perceived latency on mobile web/im app? Safari WhatsApp mobile OS Application (HTTP/DNS) TCP/IP stack modem chipset LTE interface Internet Web server
48 Background: how do mobile apps work over 4G LTE? 16 What happens under the hood? How LTE impacts perceived latency on mobile web/im app? Safari WhatsApp mobile OS Application (HTTP/DNS) TCP/IP stack modem chipset LTE interface Internet Web server
49 Background: how do mobile apps work over 4G LTE? 16 What happens under the hood? How LTE impacts perceived latency on mobile web/im app? Cellular network (4G LTE) Safari WhatsApp mobile OS Application (HTTP/DNS) TCP/IP stack modem chipset LTE interface Internet Web server
50 Background: how do mobile apps work over 4G LTE? 16 What happens under the hood? How LTE impacts perceived latency on mobile web/im app? Cellular network (4G LTE) Safari WhatsApp mobile OS Application (HTTP/DNS) TCP/IP stack modem chipset LTE interface Base stations Mobility controller User profile server Gateway Internet Web server Gateway
51 Background: how do mobile apps work over 4G LTE? 16 What happens under the hood? How LTE impacts perceived latency on mobile web/im app? Cellular network (4G LTE) Safari WhatsApp (a) (b) mobile OS Application (HTTP/DNS) TCP/IP stack modem chipset LTE interface Base stations Mobility controller User profile server Gateway Internet Web server Gateway
52 Background: how do mobile apps work over 4G LTE? 16 What happens under the hood? How LTE impacts perceived latency on mobile web/im app? Cellular network (4G LTE) Safari WhatsApp (a) (b) mobile OS Application (HTTP/DNS) TCP/IP stack modem chipset LTE interface (c) Base stations (c) Mobility controller (c) User profile server Gateway Internet Web server Gateway
53 Background: how do mobile apps work over 4G LTE? 16 What happens under the hood? How LTE impacts perceived latency on mobile web/im app? Cellular network (4G LTE) Safari WhatsApp (a) (b) mobile OS Application (HTTP/DNS) TCP/IP stack modem chipset LTE interface (c) (d) Base stations (c) Mobility controller Gateway (c) User profile server Gateway (d) Internet (d) Web server
54 Background: how do mobile apps work over 4G LTE? 16 What happens under the hood? How LTE impacts perceived latency on mobile web/im app? Cellular network (4G LTE) Safari WhatsApp (a) (b) (f) mobile OS Application (HTTP/DNS) TCP/IP stack (e) modem chipset LTE interface (c) (d) Base stations (c) Mobility controller Gateway (c) User profile server Gateway (d) Internet (d) Web server
55 Background: how do mobile apps work over 4G LTE? 16 What happens under the hood? How LTE impacts perceived latency on mobile web/im app? Cellular network (4G LTE) Safari WhatsApp (a) LTE control-plane mobile operations OS pose sizable server latency on mobile apps (b) (f) Application (HTTP/DNS) TCP/IP stack (e) modem chipset LTE interface (c) (d) Base stations (c) Mobility controller Gateway (c) User profile Gateway (d) Internet (d) Web server
56 Timing breakdown of control plane operations 17
57 Timing breakdown of control plane operations 17 P1a: RRC connection setup request
58 Timing breakdown of control plane operations 17 P1a: RRC connection setup request (Random Access)
59 Timing breakdown of control plane operations 17 P1a: RRC connection setup request (Random Access) P1b: RRC connection setup
60 Timing breakdown of control plane operations 17 P1a: RRC connection setup request (Random Access) P1b: RRC connection setup P1c: RRC connection setup complete
61 Timing breakdown of control plane operations 17 P1a: RRC connection setup request (Random Access) P1b: RRC connection setup P1c: RRC connection setup complete P2. Service request
62 Timing breakdown of control plane operations 17 P1a: RRC connection setup request (Random Access) P1b: RRC connection setup P1c: RRC connection setup complete P2. Service request P3. Authentication (non-mandatory)
63 Timing breakdown of control plane operations 17 P1a: RRC connection setup request (Random Access) P1b: RRC connection setup P1c: RRC connection setup complete P2. Service request P3. Authentication (non-mandatory) P4. Initial Context Setup
64 Timing breakdown of control plane operations 17 P1a: RRC connection setup request (Random Access) P1b: RRC connection setup P1c: RRC connection setup complete P2. Service request P3. Authentication (non-mandatory) P4. Initial Context Setup P5a. Security mode command
65 Timing breakdown of control plane operations 17 P1a: RRC connection setup request (Random Access) P1b: RRC connection setup P1c: RRC connection setup complete P2. Service request P3. Authentication (non-mandatory) P4. Initial Context Setup P5a. Security mode command P5b. Security mode complete
66 Timing breakdown of control plane operations 17 P1a: RRC connection setup request (Random Access) P1b: RRC connection setup P1c: RRC connection setup complete P2. Service request P3. Authentication (non-mandatory) P4. Initial Context Setup P5a. Security mode command P5b. Security mode complete P6a. RRC connection reconfig
67 Timing breakdown of control plane operations 17 P1a: RRC connection setup request (Random Access) P1b: RRC connection setup P1c: RRC connection setup complete P2. Service request P3. Authentication (non-mandatory) P4. Initial Context Setup P5a. Security mode command P5b. Security mode complete P6a. RRC connection reconfig P6b RRC connection reconfig complete
68 Timing breakdown of control plane operations 17 P1a: RRC connection setup request (Random Access) P1b: RRC connection setup P1c: RRC connection setup complete P2. Service request P3. Authentication (non-mandatory) P4. Initial Context Setup P5a. Security mode command P5b. Security mode complete P6a. RRC connection reconfig P6b RRC connection reconfig complete Data bearer Data bearer
69 Timing breakdown of control plane operations 17 P1a: RRC connection setup request (Random Access) P1b: RRC connection setup P1c: RRC connection setup complete P2. Service request P3. Authentication (non-mandatory) P4. Initial Context Setup P5a. Security mode command P5b. Security mode complete P6a. RRC connection reconfig P6b RRC connection reconfig complete UL data Data bearer Data bearer
70 Timing breakdown of control plane operations 17 P1a: RRC connection setup request (Random Access) P1b: RRC connection setup P1c: RRC connection setup complete P2. Service request P3. Authentication (non-mandatory) P4. Initial Context Setup P5a. Security mode command P5b. Security mode complete P6a. RRC connection reconfig P6b RRC connection reconfig complete UL data Data bearer Data bearer
71 Timing breakdown of control plane operations 17 P1a: RRC connection setup request (Random Access) P1b: RRC connection setup P1c: RRC connection setup complete P2. Service request P3. Authentication (non-mandatory) P4. Initial Context Setup P5a. Security mode command P5b. Security mode complete P6a. RRC connection reconfig P6b RRC connection reconfig complete UL data Data bearer Data bearer
72 Timing breakdown of control plane operations 17 P1a: RRC connection setup request (Random Access) T P1b: RRC connection setup P1c: RRC connection setup complete P2. Service request P3. Authentication (non-mandatory) P4. Initial Context Setup P5a. Security mode command P5b. Security mode complete P6a. RRC connection reconfig P6b RRC connection reconfig complete UL data Data bearer Data bearer
73 Timing breakdown of control plane operations 17 P1a: RRC connection setup request (Random Access) T P1b: RRC connection setup P1c: RRC connection setup complete P2. Service request P3. Authentication (non-mandatory) P4. Initial Context Setup P5a. Security mode command P5b. Security mode complete P6a. RRC connection reconfig P6b RRC connection reconfig complete UL data Data bearer Data bearer
74 Timing breakdown of control plane operations 17 P1a: RRC connection setup request (Random Access) T T P1b: RRC connection setup P1c: RRC connection setup complete P2. Service request P3. Authentication (non-mandatory) P4. Initial Context Setup P5a. Security mode command P5b. Security mode complete P6a. RRC connection reconfig P6b RRC connection reconfig complete UL data Data bearer Data bearer
75 Learning latency: latency data sensing 18 Three-tiered timing data collection: App-specific semantic timing (e.g. Navigation Timing API, IM timing model) TCP/IP stack timing (from TCPDUMP) LTE stack timing (from MobileInsight) unloadeventstart fetchstart domainlookupstart domainlookupend connectstart connectend requeststart dominteractive responsestart responseend loadeventend OS overhead DNS query TCP connection HTTP request HTTP transmission Page rendering TCP layer TCP SYN SYN ACK TCP data LTE control plane Data access request LTE data plane LTE data
76 Challenge: timestamp alignment 19 How to align timestamps at these layers? Domain-specific event tracing and mapping Machine-learning assisted Tapp App log App network request T tcpdump tcpdump log TCP/IP LTE chipset T LTE MobileInsight log LTE control plane msgs LTE data plane pkts Base station Server s ACK Web server network response
77 Pinpoint latency bosleneck in LTE: An example 20
78 Pinpoint latency bosleneck in LTE: An example 20 Run a small webpage (4 KB) in Chrome on Android User is static, under good 4G LTE signal (-95 dbm), T-Mobile
79 Pinpoint latency bosleneck in LTE: An example 20 Run a small webpage (4 KB) in Chrome on Android User is static, under good 4G LTE signal (-95 dbm), T-Mobile Total Latency: 473 msec Clicking URL page loading complete, Steps (a) (f)
80 Pinpoint latency bosleneck in LTE: An example 20 Run a small webpage (4 KB) in Chrome on Android User is static, under good 4G LTE signal (-95 dbm), T-Mobile Total Latency: 473 msec Clicking URL page loading complete, Steps (a) (f) Pinpointing the latency bozleneck How to breakdown?
81 Control-plane latency breakdown: local analysis I 21
82 Control-plane latency breakdown: local analysis I Major component from Navigation Timing API: DNS lookup, 250 ms out of 473 ms 21
83 Control-plane latency breakdown: local analysis I Major component from Navigation Timing API: DNS lookup, 250 ms out of 473 ms 21 Is the DNS server slow to handle connection?
84 Control-plane latency breakdown: local analysis I Major component from Navigation Timing API: DNS lookup, 250 ms out of 473 ms 21 Is the DNS server slow to handle connection? Fu;her breakdown: LTE service request takes 172 ms before the DNS setup
85 Control-plane latency breakdown: local analysis I Major component from Navigation Timing API: DNS lookup, 250 ms out of 473 ms 21 Is the DNS server slow to handle connection? Fu;her breakdown: LTE service request takes 172 ms before the DNS setup Queueing Stalled DNS Lookup Initial Connection Request Sent Waiting (TTFB) Content Download LTE service request LTE Data Access Latency 8.98 ms 2.97 ms ms ms 0.36 ms ms ms
86 Data-plane latency breakdown: local analysis II 22 Fu;her zoom in and breakdown the remaining LTE data access latency (291 ms): DNS-Wait Grant DNS (IPv6) DNS-Wait Grant DNS (IPv4) 26 ms 17 ms 12 ms 16 ms APP-OS overhead TCP SYN-Wait Grant TCP SYN-Send Data 2.02 ms 11 ms 18 ms TCP ACK (local processing) HTTP GET (send request) HTTP GET-Wait Grant HTTP GET-req sent HTTP-server RTT+ DL latency LTE-to-TCP overhead HTTP page DL transmission HTTP DL retransmission 0.02 ms 0.36 ms 12 ms 8 ms First bit of HTTP response 110 ms 6.1 ms 40 ms 3 ms
87 Latency mapping for failures: local analysis III 23 Example: data plane suspension due to radio reconnection and head-of-line blocking during handover
88 Latency mapping for failures: local analysis III 23 Example: data plane suspension due to radio reconnection and head-of-line blocking during handover Blocking Request Sent Waiting Grant Uplink Transmission Handover Disruption No data Handover Disruption Duplicate recv d data Waiting (TTFB, due to parallel TCP connection) Content Download 5.05 ms 0.58 ms 4 ms 130 ms 263 ms 36 ms 275 ms ms
89 Machine learning scheme 24 We leverage domain-specicc knowledge for ML-based predictions Control plane: predict handover using a decision tree classicer Features from 3GPP standards Predicts handover 100ms before it occurs with >99% accuracy Data plane: predict NACK/ACK hip at MAC layer
90 Synthesizer: global crowdsourcing analysis 25
91 Synthesizer: global crowdsourcing analysis 25 Four US carriers + Google Project Fi
92 Synthesizer: global crowdsourcing analysis 25 Four US carriers + Google Project Fi 23 phone models, 95,057 data sessions
93 Synthesizer: global crowdsourcing analysis 25 Four US carriers + Google Project Fi 23 phone models, 95,057 data sessions Overall latency: ms in 500K samples
94 Synthesizer: global crowdsourcing analysis 25 Four US carriers + Google Project Fi Average Latency by LTE Data 23 phone models, 95,057 data sessions Access Setup (no mobility) Overall latency: ms in 500K samples Varies among different mobile carriers AT&T T-mobile Sprint VerizonProject Fi
95 Synthesizer: global crowdsourcing analysis 25 Four US carriers + Google Project Fi 23 phone models, 95,057 data sessions 3,000 Overall latency: ms in 500K samples Varies among different mobile carriers Insensitive to varying radio link quality Total Latency (ms) 1, Signal Strength (dbm)
96 LTE data access latency: how frequent? 26 Frequent data access setup operations every 58.8 sec (median); sec (average) cause: frequently entering power-saving mode Sho1-lived Radio connectivity lifetime every 10.8 sec (median); 17.3 sec (average) cause: inactivity timer (regulated by standards)
97 LTE data access latency: how frequent? 26 Frequent data access setup operations every 58.8 sec (median); sec (average) cause: frequently entering power-saving mode Sho1-lived Radio connectivity lifetime every 10.8 sec (median); 17.3 sec (average) cause: inactivity timer (regulated by standards)
98 LTE data access latency: how frequent? 26 Frequent data access setup operations every 58.8 sec (median); sec (average) cause: frequently entering power-saving mode Sho1-lived Radio connectivity lifetime every 10.8 sec (median); 17.3 sec (average) cause: inactivity timer (regulated by standards)
99 Overall latency and breakdown for major carriers 27 T-Mobile AT&T Verizon Sprint Project Fi
100 Findings Summary 28 Tradio: Radio connectivity setup It contributes ms of the overall LTE access latency. On average, it contributes 39.7%, 44.0%, 61.9%, 64.2% and 43.7% of total latency in T-Mobile, AT&T, Verizon, Sprint and Project-Fi, respectively. Tctrl: Connectivity state transfer It contributes ms to ms of the overall LTE access latency. On average, it contributes 60.3%, 56.0%, 38.1%, 35.8% and 56.3% of total latency in T-Mobile, AT&T, Verizon, Sprint and Project-Fi, respectively.
101 Impact on mobile Web app: Chrome 29 Average page loading time for tested webpage: 411 ms LTE data access setup: 174 ms 42.3% total latency perceived Similar results for Safari latency on ios unloadeventstart fetchstart OS overhead TCP layer DNS query LTE control plane LTE data plane domainlookupstart domainlookupend connectstart connectend TCP connection TCP SYN Data access request SYN ACK requeststart dominteractive responsestart responseend loadeventend HTTP request LTE data HTTP transmission TCP data Page rendering Latency (ms) Total latency LTE latency Normalized sorted sample (%)
102 Impact on instant-messaging: WhatsApp 30 Average time Crst data packet being ACKed: 341 ms LTE data access setup: 175 ms 51.4% total latency perceived App init OS overhead TCP layer DNS query LTE control plane LTE data plane App connect w/ server TCP connection SYN idle SYN ACK New message Latency SSL Data Data access request Server ACK TCP ACK SSL Data TCP data LTE data Next message TCP ACK Latency (ms) Total latency LTE latency Normalized sorted sample (%)
103 Discussion: reducing LTE latency 31 Data plane walk-arounds Mask the data setup latency by waking device in connected mode in advance Control plane acceleration Speed up connectivity state transfer between the base station and the mobility controller (e.g. DPCM [ACM MobiCom 17]) Handover prediction Other issues Extending to other network metrics (e.g. loss, throughput, ) Theoretical bounds Privacy issues
104 Conclusion: ML-based analysis for next-gen mobile networks 32 Mobile networks are successful and will continue to prosper (5G, self driving, ) Mobile network analysis: paradigm shin to device-centric, ML-based scheme Device-centric: unveil the tightly-guided operation issues over 4G/5G mobile networks Two-tiered approach: a more open solution approach for the research community
105 Q & A
106 Backup
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