COMPUTING ON
JETSTREAM:
STREAMING ANALYTICS IN
THE WIDE-AREA
Matvey Arye
Joint work with: Ari Rabkin, Sid Sen,
Mike Freedman and Vivek Pai
THE RISE OF GLOBAL
DISTRIBUTED SYSTEMS
Image shows
CDN
TRADITIONAL ANALYTICS
Centralized
Database
Image shows
CDN
BANDWIDTH IS EXPENSIVE
Price Trends 2005-2008
CPU(16x)
Storage(10x)
Bandwidth(2.7x)
[Above the Clouds, Armbrust et. al.]
BANDWIDTH TRENDS
[TeleGeography's Global Bandwidth Research Service]
BANDWIDTH TRENDS
20%
[TeleGeography's Global Bandwidth Research Service]
20%
BANDWIDTH COSTS
• Amazon EC2 bandwidth: $0.05 per GB
• Wireless broadband: $2 per GB
• Cell phone broadband (ATT/Verizon): $6 per GB
– (Other providers are similar)
• Satellite Bandwidth $200 - $460 per GB
– May drop to ~$20
THIS APPROACH IS NOT
SCALABLE
Centralized
Database
Image shows
CDN
THE COMING FUTURE:
DISPERSED DATA
Dispersed
Databases
Dispersed
Databases
Dispersed
Databases
Dispersed
Databases
WIDE-AREA COMPUTER
SYSTEMS
• Web Services
–
–
–
–
CDNs
Ad Services
IaaS
Social Media
• Infrastructure
– Energy Grid
• Military
–
–
–
–
Global Network
Drones
UAVs
Surveillance
NEED QUERIES ON A
GLOBAL VIEW
• CDNs:
– Popularity of websites globally
– Tracking security threats
• Military
– Threat “chatter” correlation
– Big picture view of battlefield
• Energy Grid
– Wide-area view of energy production and
expenditure
SOME QUERIES ARE EASY
Server Crashed
Alert me when
servers crash
OTHERS ARE HARD
Requests
Requests
Requests
CDN
Requests
Requests
Requests
Requests
CDN
Requests
How popular are all of
my domains? Urls?
BEFORE JETSTREAM
Bandwidth
Needed for backhaul
95% Level
Time [two days]
Analyst’s remorse:
not enough data
wasted bandwidth
Buyers’s remorse:
system overload or
overprovisioning
Latency
Bandwidth
WHAT HAPPENS DURING
OVERLOAD?
Needed for backhaul
Available
??????
Time [one day]
Queue size grows
without bound!
Time
THE JETSTREAM VISION
Bandwidth
Needed for backhaul
Used by
JetStream
Available
Time [two days]
JetStream lets programs adapt to shortages
and backfill later.
Need new abstractions for programmers
SYSTEM ARCHITECTURE
…
worker node
Optimized query
JetStream API
Query graph
…
…
Planner
Library
Coordinator
Daemon
Control plane
Data plane
…
stream source
compute resources (several sites)
AN EXAMPLE QUERY
File Read
Operator
Parse Log
File
Local
Storage
Query
Every 10 s
Site A
Central
Storage
Site B
File Read
Operator
Parse Log
File
Local
Storage
Query
Every 10 s
Site C
ADAPTIVE DEGRADATION
Local Data
Dataflow
Operators
Summarized or
Approximated
Data
Network
Feedback control
 Feedback control to decide when to degrade
 User-defined policies for how to degrade data
Dataflow
Operators
MONITORING AVAILABLE
BANDWIDTH
Data
Data
Data
Time
Marker
Data
Sources insert time markers into the data stream every k seconds
Network monitor records time it took to process interval – t
=>
k/t estimates available capacity
WAYS TO DEGRADE DATA
 Can coarsen a dimension
 Can drop low-rank values
AN INTERFACE FOR
DEGRADATION (I)
Incoming
data
Coarsening
Operator
Sampled
Data
Network
Sending 4x too much
• First attempt: policy specified by choosing an operator.
• Operators read the congestion sensor and respond.
COARSENING REDUCES DATA
VOLUMES
01:01:01
foo.com/a
1
01:01:02
foo.com/a
2
01:01:01
foo.com/b
10
01:01:02
foo.com/b
15
01:01:01
foo.com/c
5
01:01:02
foo.com/c
20
01:01:*
foo.com/a
3
01:01:*
foo.com/b
25
01:01:*
foo.com/c
25
BUT NOT ALWAYS
01:01:01
foo.com/a
1
01:01:02
bar.com/a
2
01:01:01
foo.com/b
10
01:01:02
bar.com/b
15
01:01:01
foo.com/c
5
01:01:02
bar.com/c
20
01:01:*
foo.com/a
1
01:01:*
foo.com/b
10
01:01:*
foo.com/c
5
01:01:*
bar.com/a
2
01:01:*
bar.com/b
15
01:01:*
bar.com/c
20
DEPENDS ON LEVEL OF
COARSENING
256
Compression factor
128
Domains
URLs
64
32
16
8
4
2
1
5s
minute
5m
hour
Aggregation time period
Data from CoralCDN logs
day
GETTING THE MOST DATA
QUALITY FOR THE LEAST BW
Issue
Some degradation techniques result in good quality
but have unpredictable savings.
Solution
Use multiple techniques
– Start off with technique that gives best quality
– Supplement with other techniques when BW scarce
=> Keeps latency bounded; minimize analyst’s remorse
ALLOWING COMPOSITE
POLICIES
Incoming
data
Coarsening
Operator
Sampling
Operator
Network
Sending 4x too much
• Chaos if two operators are simultaneously
responding to the same sensor
• Operator placement constrained in ways that don’t
match degradation policy.
INTRODUCING A
CONTROLLER
Incoming
data
Coarsening
Operator
Sampling
Operator
Network
Controller
Drop 75% of data!
Sending 4x too much
• Introduce a controller for each network connection
that determines which degradations to apply
• Degradation policies for each controller
• Policy no longer constrained by operator topology
DEGRADATION
Type
Mergeability Errors
Predictable
Size Savings
Dimension
Coarsening
Consistent
Sampling
Yes*
Resolution
No
Yes
Sampling
Yes
Sampling
None
Yes
No
Depends
Depends
Local Filtering No
Multi-round
No
Filtering
Aggregate
Depends
Approx.
MERGEABILITY IS
NONTRIVIAL
Every 5
01 - 05
06 - 10
11 - 15
16 - 20
21 - 25
26 - 30
??????
Every 6
Every 10
Every 30??
01 - 06
01 - 10
07 - 12
13 - 18
11 - 20
19 - 24
25 - 30
21 - 30
01 - 30
• Can’t cleanly unify data at arbitrary degradation
• Degradation operators need to have fixed levels
INTERFACING WITH THE
CONTROLLER
Incoming
data
Sampling
Operator
Coarsening
Operator
Network
Controller
Sending 4x too much
Operator
Shrinking data by 50%
Possible levels:
[0%, 50%, 75%, 95%, …]
Go to level 75%
Controller
A PLANNER FOR POLICY
Query planners:
Query + Data Distribution => Execution Plan
Why not do this for degradation policy?
What is the Query?
For us the policy affects the data ingestion
=> Effects all subsequent Queries
Planning
All Potential Queries + Data Distribution => Policy
EXPERIMENTAL SETUP
Princeton
80 nodes on VICCI testbed in
US and Germany
Policy: Drop data if
insufficient BW
WITHOUT ADAPTATION
Bandwidth
Shaping
WITH ADAPTATION
Bandwidth Shaping
Bandwidth (Mbits/sec)
COMPOSITE POLICIES
No degradation
Max window 5
Max window 10
8
Max window 5 + Threshold
Max window 5 + Sampling
6
4
2
0
0
50
100
150
200
250
Experiment time (sec)
300
350
OPERATING ON DISPERSED
DATA
Dispersed
Databases
Dispersed
Databases
Dispersed
Databases
Dispersed
Databases
URL
foo.com/r
foo.com/q
bar.com/n
bar.com/m
Time
CUBE DIMENSIONS
CUBE AGGREGATES
bar.com/m
01:01:01
Time
bar.com/*
URL
foo.com/r
foo.com/q
bar.com/n
bar.com/m
CUBE ROLLUP
foo.com/*
FULL HIERARCHY
URL: *
Time: 01:01:01
(37,199)
(29,199)
foo.com/r
URL
foo.com/q
bar.com/n
bar.com/m
Time
(8,90)
RICH STRUCTURE
Cell
URL
Time
A
bar.com/*
01:01:01
B
*
01:01:01
C
foo.com/*
01:01:01
D
foo.com/r
01:01:*
E
foo.com/*
01:01:*
TWO KINDS OF
AGGREGATION
1. Rollups – Across Dimensions
2. Inserts – Across Sources
The data cube model constrains the system to use
the same aggregate function for both.
Constraint: no queries on tuple arrival order
Makes reasoning easier!
AN EXAMPLE QUERY
File Read
Operator
Parse Log
File
Local
Storage
Query
Every 10 s
Site A
Central
Storage
Site B
File Read
Operator
Parse Log
File
Local
Storage
Query
Every 10 s
Site C
SUBSCRIBERS
File Read
Operator
Parse Log
File
File Read
Operator
Parse Log
File
Local
Storage
Query
Every 10 s
Site A
• Extract data from cubes to send downstream
• Control latency vs completeness traeoff
SUBSCRIBER API
• Notified of every tuple inserted into cube
• Can slice and rollup cubes
Possible policies:
• Wait for all upstream nodes to contribute
• Wait for a timer to go off
FUTURE WORK
• Reliability
• Individual queries
– Statistical methods
– Multi-round protocols
• Currently working on improving top-k
• Fairness that gives best data quality
Thanks for listening!