wiscape - uw computer sciences user pagespages.cs.wisc.edu/~yoonj/wiscape-talk.pdfmotivation • one...
TRANSCRIPT
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WiScapeA Case for Client-Assisted Approach
to Monitoring Wide-Area Wireless Networks
Sayandeep Sen, Jongwon Yoon, Joshua Hare,Justin Ormont, and Suman Banerjee
University of Wisconsin-Madison
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Motivation
• One way to monitor wireless performance
- Carrying out drive-by measurement test
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Labor intensive
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Motivation
• One way to monitor wireless performance
- Carrying out drive-by measurement test
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Does not scale efficientlyfor large-area networks
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Motivation
• One way to monitor wireless performance
- Carrying out drive-by measurement test
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Client Assisted Approach
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Client-Assisted Monitoring
• Collecting measured samples from multiple clients
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Client-Assisted Monitoring
• Collecting measured samples from multiple clients
MeasurementRequests
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Server
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Client-Assisted Monitoring
• Collecting measured samples from multiple clients
MeasuredSamples
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Benefits
• Leverages multiple users
- Covers more area more frequently
• Captures client experiences
• At locations clients care about
• Helps network operators
- Improves network performance
- Detects connectivity holes
→ in turn helps users
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Challenges of Client-Assisted Monitoring
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Challenges of Client-Assisted Monitoring
Measurements discrete in space
Aggregate in SPACE
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Measurements discrete in space
in SPACE(Zone)?
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Challenges of Client-Assisted Monitoring
Battery
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Data Usage(Mbytes)
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Challenges of Client-Assisted Monitoring
Aggregate in TIME(Epoch)?
Measurements discrete in time
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Challenges of Client-Assisted Monitoring
Aggregate in TIME(Epoch)?
Measurements discrete in time
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Can we compose
measurements?
Measurements not from a single client
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Challenges of Client-Assisted Monitoring
Aggregate in TIME
Measurements discrete
Measurements discrete in space
Aggregate in SPACE(Zone)?
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Aggregate in TIME(Epoch)?
Measurements discrete in time
Can we compose
measurements?
Measurements not from a single client
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WiScape• Characterize the Wireless landscape using client-assistance
• Using small and infrequent amounts of measurement collected by different users
80 packets/ 1hr
100 packets/ 75mins
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80 packets/ 1hr
120 packets/ 2hrs
90 packets/ 75mins
90 packets/ 80mins
100 packets/ 75mins
WiScapeServer
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WiScape• Characterize the Wireless landscape using client-assistance
• Using small and infrequent amounts of measurement collected by different users
Corollary:
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Corollary: Client-assisted monitoring can capture coarse grained (~1hr) network property
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Contributions
• Present a framework for coarse grained monitoring system
• Present applications of coarse grained monitoring of wide-area networksmonitoring of wide-area networks
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Outline
• Motivation
• Dataset
• Low overhead client-assisted monitoring
– Aggregation in space– Aggregation in space
– Aggregation in time
– Composing of measurements
• Applications
• Related work & Conclusion
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Outline
• Dataset
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Data Collection Methodology
Measurement Metrics1) TCP/UDP
Throughput2) Jitter
Net A
Net B
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CellularBase Stations
Client
Throughput2) Jitter3) UDP Loss rate4) LatencyNet C
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Snapshot of TCP performance
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- City-wide area, NetB. Circle represents 1.1㎢ area
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Vehicular Dataset
155 ㎢ city-widearea in MadisonWI
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- Madison Metro buses for 11 month- Net B and C
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Vehicular Dataset
240 km road stretch, Madison, WI to Chicago, IL
- Collected for 6 month- Net B and C
Madison, WI
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Chicago, IL
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Vehicular Dataset
20 km Road stretch, Madison, WI
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- Active measurement. Collected over 3 month- Net A,B and C
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Static & Proximity Dataset
New Brunswick, NJ
Princeton, NJ
Madison, WI
: Static
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: Proximate
Static: 5 Locations in WI, 2 Locations in NJProximity: Vicinity of the static locations- 5 month in WI using Net A,B and C1 month in NJ using Net B and C
: Static
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Dataset
Group Span Months Nets Location
Static 5 locations2 locations
51
A, B, CB, C
Madison, WINew Brunswick, Princeton, NJ
Proximity Vicinity of the static locations
51
A, B, CB, C
Madison, WINew Brunswick,
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locations 1 B, C New Brunswick, Princeton, NJ
Vehicular 155 ㎢ city-wide 240 Km road155 ㎢ city-wide20km road stretch
6
123
B, C
BA, B, C
Madison, WIMadison-ChicagoMadison, WIMadison, WI
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Outline
• Low overhead client-assisted monitoring
– Aggregation in space– Aggregation in space
– Aggregation in time
– Composing of measurements
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Challenges of Client-Assisted Monitoring
Aggregate in TIME
Measurements discrete
Measurements discrete in space
Aggregate in SPACE(Zone)?
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Aggregate in TIME(Epoch)?
Measurements discrete in time
Can we compose
measurements?
Measurements not from a single client
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Aggregating in SPACE (Zone)
Zone: a region which is small enough to ensure we have similar performance
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to ensure we have similar performance but big enough to ensure sufficientmeasurements can be collected
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Aggregating in SPACE (Zone)
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Aggregating in SPACE (Zone)
Aggregate in SPACE(Zones)?
What zone size should be used?
Yes
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Aggregating in SPACE (Zone)
250 meter zone radius 97% of the zone have relative std. dev. < 8%
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97% of the zone have relative std. dev. < 8%
- TCP throughput for NetB, collected 155 ㎢ Madison(200~10000 measurements were collected per week at all zones)- Relative Standard Deviation = standard dev. of samples / mean of samples
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WiScape Framework
Aggregate in SPACE(Zones)?
What zone size should be used?
Aggregate When to re-estimate
Yes
E.g., 250m radius
Yes
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Aggregate in TIME(Epoch)?
When to re-estimate the metric?
Yes
Measurement Metrics1) TCP/UDP throughput2) Jitter3) UDP Loss rate4) Latency
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Aggregating in TIME (Epoch)
• 30 min bins (Coarse time scale)
- TCPthroughputWI
- Jitter, WI
Small degree of variation
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- UDPThroughputWI
-UDP loss rateWI
• Static-WI/NJ, Net A, B and C
Small degree of variation at coarse time (30min) scale
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Aggregating in TIME (Epoch)
• 10 sec bins (Fine time scale)
- Standard Deviation for 10sec/30min time bins
Net B - WI Net C - WI Net B - NJ Net C – NJCoarse30min
Fine10sec
Coarse30min
Fine10sec
Coarse30min
Fine10sec
Coarse30min
Fine10sec
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30min 10sec 30min 10sec 30min 10sec 30min 10sec
TCP(Kbps)
33 102 36 96 126 408 167 414
UDP(Kbps)
39 82 38 94 153 429 182 365
Jitter(msec)
1.3 2.1 0.7 1.6 0.5 1.6 0.5 1.0
Loss(%)
~0 ~0 ~0 ~0 ~0 ~0 ~0 ~0
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Aggregating in TIME (Epoch)
• 10 sec bins (Fine time scale)
- Standard Deviation for 10sec/30min time bins
Net B - WI Net C - WI Net B - NJ Net C – NJCoarse30min
Fine10sec
Coarse30min
Fine10sec
Coarse30min
Fine10sec
Coarse30min
Fine10sec
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30min 10sec 30min 10sec 30min 10sec 30min 10sec
TCP(Kbps)
33 102 36 96 126 408 167 414
UDP(Kbps)
39 82 38 94 153 429 182 365
Jitter(msec)
1.3 2.1 0.7 1.6 0.5 1.6 0.5 1.0
Loss(%)
~0 ~0 ~0 ~0 ~0 ~0 ~0 ~0High degree of variation at fine time (10sec) scale
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Aggregating in TIME (Epoch)
• 10 sec bins (Fine time scale)
- Standard Deviation for 10sec/30min time bins
Net B - WI Net C - WI Net B - NJ Net C – NJCoarse30min
Fine10sec
Coarse30min
Fine10sec
Coarse30min
Fine10sec
Coarse30min
Fine10sec
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30min 10sec 30min 10sec 30min 10sec 30min 10sec
TCP(Kbps)
33 102 36 96 126 408 167 414
UDP(Kbps)
39 82 38 94 153 429 182 365
Jitter(msec)
1.3 2.1 0.7 1.6 0.5 1.6 0.5 1.0
Loss(%)
~0 ~0 ~0 ~0 ~0 ~0 ~0 ~0Degree of variation is zone specific
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Aggregating in TIME (Epoch)
• Zone specific epochs
- Coherence interval of a metric (e.g. TCP throughput,
Jitter, etc.) in a given zone.
- We re-estimate the metric once every time epoch
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Aggregating in TIME (Epoch)
• Allan deviation
- Determining intervals for over which given metric is
most stable
- Finding the time (coherence) interval at which the difference between bins is minimumdifference between bins is minimum
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Aggregating in TIME (Epoch)
• Allan deviation
Time duration with the lowest Allan dev.
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Time duration with the lowest Allan dev. determines the Zone Specific Epoch
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Aggregating in TIME (Epoch)
• Zone specific epochs
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- Proximate-WI/NJ, UDP throughput, Net B
75 min, WI 15 min, NJ
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WiScape Framework
Aggregate in SPACE(Zones)?
What zone size should be used?
Aggregate What time epoch
Yes
e.g., 250m radius
Yes
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Aggregate in TIME(Epoch)?
What time epoch should be used?
How many client-sourced measurements?
Zone specific, O(10s of min)
Can we compose
measurements?
Yes
Yes
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WiScape Framework
Aggregate in SPACE(Zones)?
What zone size should be used?
Aggregate What time epoch
Yes
e.g., 250m radius
Yes
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Aggregate in TIME(Epoch)?
What time epoch should be used?
Zone specific, O(10s of min)
Can we compose
measurements?
Yes
Yes
Zone specific: 100s of packets/ epoch (~10s of min)/zone
[details in paper]
How many client-sourced measurements?
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WiScape Framework
Aggregate in SPACE(Zones)?
What zone size should be used?
Aggregate What time epoch
Yes
1) Radius 2) For each zone:
calculate
Zone specific
Yes
e.g., 250m radius
Jongwon Yoon WiScape / IMC 2011
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Aggregate in TIME(Epoch)?
What time epoch should be used?
Zone specific, O(10s of min)
calculate time epoch
3) For each zone:calculate packet quantity
Yes
Zone specific: 100s of packets/ epoch (~10s of min)/zone
How many client-sourced measurements?
Can we compose
measurements?Yes
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Outline
• Applications
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• Multi Network Interface Systems
Improving Multi-interface Applications
NetA,B and C
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TCP T
hro
ughput
(kbps)
Zone1 2
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• Multi Network Interface Systems
Improving Multi-interface Applications
NetB?NetA,B and C
NetA?
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TCP T
hro
ughput
(kbps)
Zone1 2
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Observations
TCP T
hro
ughput
NetA
NetB NetB
NetC
Persistently better performance
5%
95%
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TCP T
hro
ughput
(kbps)
Zone1 2
NetB
NetC
NetA
NetB
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Observations
• Persistently better performance for a zone
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- 20 km road stretchMadison, WI
- Vehicular tracesMadison-Chicago
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• Multi Network Interface Systems
Improving Multi-interface Applications
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TCP T
hro
ughput
(kbps)
Zone1 2
NetA
NetB
NetCNetA
NetB
NetC
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Improving Multi-interface Applications
• Multi Network Interface Systems
Proportionally use NetA more
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TCP T
hro
ughput
(kbps)
Zone1 2
NetA
NetB
NetCNetA
NetB
NetC
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Improving Multi-interface Applications
• Multi Network Interface Systems
Proportionally use NetC more
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TCP T
hro
ughput
(kbps)
Zone1 2
NetA
NetB
NetCNetA
NetB
NetC
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Improving Multi-interface Applications
• Multi Network Interface Systems
Multisim-WiScape and MAR-WiScape
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TCP T
hro
ughput
(kbps)
Zone1 2
NetA
NetB
NetCNetA
NetB
NetC
Multisim-WiScape and MAR-WiScape(~30-40% improvement)
[details in paper]
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Observations
• Identifying locations with variable performance
• Detecting flash crowd
- Game day,
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- Game day, Camp Randall, Madison WI
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Application for Network Operators
• Identifying regions of high overload quickly
• Detecting flash crowd
- Game day,
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- Game day, Camp Randall, Madison WI
Detecting overall conditions quickly with small number of measurements
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Outline
• Related work & Conclusions
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Related Work
• Monitoring Cellular Networks
- Mark the Spot (AT&T)
- 3G Test (University of Michigan)
- RootMetrics
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Future Work
• Full scalable deployment
• Monitoring dense deployments (NYC, LA, etc.)
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Conclusion
• Present a framework for coarse grained monitoring of wide-area network
- Validate it with data (datasets is available at
www.cs.wisc.edu/~yoonj/wiscape/IMC11_Data.html)
• Applications
- Improving client performance
- Helping operators
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Thank You!
• Questions?
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Backup slides
• Backup
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Aggregating in TIME (Epoch)
TCP T
hro
ughput
(kbps)
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TCP T
hro
ughput
(kbps)
Timebin size
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Aggregating in TIME (Epoch)
TCP T
hro
ughput
(kbps)
Average, Std.
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TCP T
hro
ughput
(kbps)
Timebin size
30 min bins: Coarse time scale10 sec bins: Fine time scale
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Aggregating in TIME (Epoch)
• Allan Deviation
TCP T
hro
ughput
10 min
5 5 5
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5 10 15 20 25 30
TCP T
hro
ughput
(Mbps)
Time (min)
5 5
5 5
5
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Aggregating in TIME (Epoch)
• Allan Deviation
TCP T
hro
ughput
5 min
5 5 5
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5 10 15 20 25 30
TCP T
hro
ughput
(Mbps)
Time (min)
5 5 5
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Aggregating in TIME (Epoch)
• Allan Deviation
TCP T
hro
ughput
15 min
5 10
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5 10 15 20 25 30
TCP T
hro
ughput
(Mbps)
Time (min)
510