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Mission Statement
1. federated clouds = diversification2. many DCs and/or cloud providers
3. we care mostly about performance4. practical solutions are needed
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Example: BizStore
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BizStore: One DC is Not Enough
• rememberJune 2013?• most services today use vertical intergration -- no diversity
• Hitachi does not share DCs with NEC
• regional diversity of one provider is bad◦ how many Amazon DCs in Japan?
.(the only possible) Solution..
.
... is to sign contracts with multiple DCs and manage on
client side◦ to be officially presented/released in April 01
01 myself+0 "High Availability Cloud Storage ... Social Graph ... Smart Distribution" NS研 (April 2014)
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BizStore: One DC is Not Enough
Kansai
DC1
Okinawa Locations
Data Centers
DC2
Kyushu
Osaka Office DC1
DC1 DC2 Naha Office
Network distance
Network distance
storage network
Employee A …. Content / Social Metadata High Availability Data Store DC1 DC2 ….
DC1 DC2 Business trip
Store APIs
Proposed Software
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BizStore: Store Diversification
• in software: not a priority list -- optimization engine!• realtime performance monitoring, read/write optimization, etc.
• sub-file data unit -- chunks
SSD Growing network
distance User
HDD DC1 DC2 …
Network
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BizStore: Socially Aware Store• content relevance based on
social graph• relevance is a distribution• individual redundancy based on distribution
• other link types: same time, location,filetype, ...
• link strengh != 1Descending
order
Relevance
Distribution
Redundancy (user setting)
Physical limit of redundancy
End of content
There is a link
When a file is …
Between Created Viewed Edited Deleted
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Example: Cloud Streaming
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Cloud Streaming: Fixing Problems
Traditionalstreaming
P2Pstreaming
Cloud streaming
Adaptivestreaming
• Congestion(Flash Crowds)
• Unreliable throughput
• Unreliable sources
• Unreliable throughput
• Congestion
Fixed Fixed
Fixed
Fixed
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Cloud Streaming: Design
VMpopulation
CurrentSources
ServiceProvider
(SP)
Tracker
ServiceProvider
(SP)
Parentpeers
P2Pstreaming
Cloudstreaming
ClientClient
02 myself+0 "Multi-Source Stream Aggregation in the Cloud" Wiley Book on ACDN, Chapter 10 (2014)
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Practical Solutions for FederatedClouds
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A Shortlist of (S)olutions
1. S1: Nextgen traffic processors at DCs
2. S2: QoS Context and Performance Visualization at DCs
3. S3: Performance Modeling for Federated Clouds
4. S4: Client Side Traffic Boostings5. .... definitely not a complete list
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Solution (S) 1:Nextgen Traffic Processors at DCs(work in progress)
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S1: Multicore Packet Capture
Global Networks
Data Center Internals
Gateway Switch
Capture Manager
CPU CPU CPU
CPU CPU CPU
… Storage
Mirror
• multicore is the key
• multicore !=traditional parallel processing03
• on-demand capture, DPI,heterogeneous tasks 04
03 myself+0 "...Multicore Capture in Data Center Forensics" ACM AISACCS-SFCS (June 2014)
04 myself+0 "A Lock-Free Shared Memory Design for ... Multicore Packet Traffic Capture" IJNM (in print)
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S1: Multicore Hates Memory Locks• lockfree design 04 : no messages, no memory locks
PF_RING
PF_RING
TimeManager
Shared Memory
Capture
Capture
…
Core 1
Core 2
Core 3
….
Core X Manager
PF_ RING
Shared memory
One thread
Create
Fork
Lifespan Stale check
Process/wrap Wrap wait
Doub
le-L
inke
d Li
st (D
LL)
Assign
04 myself+0 "A Lock-Free Shared Memory Design for ... Multicore Packet Traffic Capture" IJNM (in print)
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Solution (S) 2: DC Performance APIs
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S2: E2E QoS, M2M Patterns
Meter Merger
Per flow statistics
Analyzer
History,state
Profiler
UDP
Users
Clients
Probe Analysis machine
Web application
• clean slate: captureQoS context 05
• visualize usercommunities
• export via APIs to usersand/or service providers
05 myself+0 "A holistic community-based architecture for measuring E2E QoS at data centres" IJCSE (in print)
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S2: Has to be a Clean Slate
ProbeRouter
Data center infrastructure
source IP
timestamp Key Key DLL0 #01 #02 #03 1 #042 #05 #06….….2^24 #07
source portdest IP
dest port
protocolpacket size
CRC24
Packet Hash table
#01
DLL#05#04
#02#03#07
Exportover UDP
Byte048
12162024…
0 (bits) 32
Source port Dest port
Source IPDestination IP
* psize pspace
Start time (s)Start time (us)
* psize pspace1 11
Data unit
psize:Packet size
pspace:Packet space
(us)
#06
Exportvia
a file
UDP RX
Buffer (5s)
Byte048
12162024…
0 (bits) 32
Source port Dest port
Source IPDestination IP
D psize pspace
Start time (s)Start time (us)
D psize pspace1 11
Data unit
D:Direction(0 or 1)
Merger
Find flow fromopposite direction
Analyzer
History
State
Read and update
Ring buffer of data units per IP on internal networks
Statistic MeaningMinOWD Global minimum OWDMaxBatch Max byte count of a
packet burstBulks Throughputs in flows
Per source-dest pair
• has to be a clean slate!• cisco, ntop, sflow are notfeasible
• QoS context is somethingnew
• (figure is vector, so, zoom in!)
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S2: But Payoff is Great!
0 6400 12800 19200Batch size (bytes)
0
800
1600
2400
3200
4000O
WD
(ms)
+ T
X tim
e (x
0.1m
s)
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Solution (S) 3: Cloud Weather System(work in progress)
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S3: Cloud Weather System
(high/low)Pressure
front
Typhoon
Drought
Goodweather
Badweather
• continents: user, services 07
• water: network• weather, clouds, etc.: changes inperformance
• droughts: insufficiency ofinfrastructure, users do not get enoughcapacity
• typhoons: basically, Flash Crowds inservices, going viral, ...
• forecasting: possible with enoughperformance monitoring, similar to stockmarket
07 myself+0 "Cloud Weather System as a Futuristic Performance Model" IEICE総合大会 (March 2013)
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Solution (S) 4: Mobile ThroughputBoosters
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S4: Mobile Throughput Booster
• so far, only possible in wireless -- WiFi Direct
Single Connection Multipath
Singular Connectivity
Traditional Applications
Traditional Multipath
Multiple Connectivity
No known cases (wasted potential)
Group Communication 3G/LTE/* + WiFi Direct THIS PROPOSAL
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S4: Group Resource Pooling
Remote connectivity Local Connectivity
Content Provider
Main Client
Delegated Client
Delegated Client
3G/LTE/* Access 3G/LTE/*
Access
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S4: Converged Wireless Campus
Student
Develop, make secure
APP + CODE
Campus
Another Student
APP + CODE
APP + CODE
1
2 2 Distribute
3 Meet and delegate
API Tokens
API Tokens
Distribute
Pass at delegation
University 4
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Solution (S) 5: Over-the-NetworkIndexing
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S5: Indexing in Clouds
Data
Indexer
Index
Network
Traditional Client
Data
Indexer
Index Read, Write
Stringex Client
The
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S5: Over-the-Network Optimization• in short: throughput-centric network storage optimization 08
Stringex
Index
Stringex Client
The
Sync Engine
Optimization
Local Cache
Check 1 2
Use
08 myself+0 "A New Practical Design for Browsable Over-the-Network Indexing" ISEEE (April 2014)
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S5: Performance
3.15 3.85 4.55 5.25 5.95 6.65Index Size (log)
2.55
2.65
2.75
2.85
2.95
3.05
3.15
3.25
Thro
ughp
ut (l
og o
f byt
es/d
oc)
Lucene
Stringex
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That’s all, thank you ...
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[01] myself+0 (April 2014)High Availability Cloud Storage ... Social Graph ... Smart DistributionNS研
[02] myself+0 (2014)Multi-Source Stream Aggregation in the CloudWiley Book on ACDN, Chapter 10
[03] myself+0 (June 2014)...Multicore Capture in Data Center ForensicsACM AISACCS-SFCS
[04] myself+0 (in print)A Lock-Free Shared Memory Design for ... Multicore Packet Traffic CaptureIJNM
[05] myself+0 (in print)A holistic community-based architecture for measuring E2E QoS at data centresIJCSE
[06] myself+0 (May 2014)Marat Zhanikeev -- [email protected] Is It Time to Go Global with Cloud Performance Management? -- http://tinyurl.com/marat140328 30/30
...
30/30
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Towards a Practical Method for Interactive Traffic Visualizations in Data CentersSC研
[07] myself+0 (March 2013)Cloud Weather System as a Futuristic Performance ModelIEICE総合大会
[08] myself+0 (April 2014)A New Practical Design for Browsable Over-the-Network IndexingISEEE
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