Network Traffic Characteristics of Data Centers in the Wild

Report
Demystifying and Controlling
the Performance of Data
Center Networks
Why are Data Centers Important?
• Internal users
– Line-of-Business apps
– Production test beds
• External users
– Web portals
– Web services
– Multimedia applications
– Chat/IM
Why are Data Centers Important?
• Poor performance  loss of revenue
• Understanding traffic is crucial
• Traffic engineering is crucial
Road Map
• Understanding Data center traffic
• Improving network level performance
• Ongoing work
Canonical Data Center Architecture
Core (L3)
Aggregation (L2)
Edge (L2)
Top-of-Rack
Application
servers
Dataset: Data Centers Studied


DC Role
DC
Name
Location
Number
Devices
Universities
EDU1
US-Mid
22
EDU2
US-Mid
36
EDU3
US-Mid
11
Private
Enterprise
PRV1
US-Mid
97
PRV2
US-West
100
Commercial
Clouds
CLD1
US-West
562
CLD2
US-West
763
CLD3
US-East
612
External users
CLD4
S. America
427
 Clouds
 Large
 Globally diverse
CLD5
S. America
427
10 data centers
3 classes
 Universities
 Private enterprise
 Clouds

Internal users
 Univ/priv
 Small
 Local to campus

Dataset: Collection
• SNMP
–
–
–
–
DC
Name
SNMP
Poll SNMP MIBs
Bytes-in/bytes-out/discards EDU1 Yes
> 10 Days
EDU2 Yes
Averaged over 5 mins
• Packet Traces
– Cisco port span
– 12 hours
• Topology
– Cisco Discovery Protocol
Packet
Traces
Topology
Yes
Yes
Yes
Yes
EDU3
Yes
Yes
Yes
PRV1
Yes
Yes
Yes
PRV2
Yes
Yes
Yes
CLD1
Yes
No
No
CLD2
Yes
No
No
CLD3
Yes
No
No
CLD4
Yes
No
No
CLD5
Yes
No
No
Canonical Data Center Architecture
Core (L3)
Aggregation (L2)
Edge (L2)
Top-of-Rack
Application
servers
Packet
Sniffers
Analyzing Packet Traces
• Transmission patterns of the applications
• Properties of packet crucial for
– Understanding effectiveness of techniques
Routing must react quickly to overcome bursts
• ON-OFF traffic at edges
– Binned in 15 and 100 m. secs
– We observe that ON-OFF persists
9
Data-Center Traffic is Bursty
• Understanding arrival process
– Range of acceptable models
• What is the arrival process?
– Heavy-tail for the 3 distributions
Data
Center
Off Period
Dist
ON periods
Dist
Inter-arrival
Dist
Prv2_1
Lognormal
Lognormal
Lognormal
Prv2_2
Lognormal
Lognormal
Lognormal
Prv2_3
Lognormal
Lognormal
Lognormal
Need new models to generate traffic
• ON, OFF times, Inter-arrival,
– Lognormal across all data
centers
Prv2_4
Lognormal
Lognormal
Lognormal
EDU1
Lognormal
Weibull
Weibull
EDU2
Lognormal
Weibull
Weibull
EDU3
Lognormal
Weibull
Weibull
• Different from Pareto of WAN
– Need new models
10
Canonical Data Center Architecture
Core (L3)
Aggregation (L2)
Edge (L2)
Top-of-Rack
Application
servers
Intra-Rack Versus Extra-Rack
• Quantify amount of traffic using interconnect
– Perspective for interconnect analysis
Extra-Rack
Edge
Intra-Rack
Application
servers
Extra-Rack = Sum of Uplinks
Intra-Rack = Sum of Server Links – Extra-Rack
Intra-Rack Versus Extra-Rack
Results
100
90
80
70
60
50
40
30
20
10
0
Extra-Rack
Inter-Rack
EDU1 EDU2 EDU3 PRV1 PRV2 CLD1 CLD2 CLD3 CLD4 CLD5
• Clouds: most traffic stays within a rack (75%)
– Colocation of apps and dependent components
• Other DCs: > 50% leaves the rack
– Un-optimized placement
Extra-Rack Traffic on DC
Interconnect
• Utilization: core > agg > edge
– Aggregation of many unto few
• Tail of core utilization differs
– Hot-spots  links with > 70% util
– Prevalence of hot-spots differs across data centers
Persistence of Core Hot-Spots
• Low persistence: PRV2, EDU1, EDU2, EDU3, CLD1, CLD3
• High persistence/low prevalence: PRV1, CLD2
– 2-8% are hotspots > 50%
• High persistence/high prevalence: CLD4, CLD5
– 15% are hotspots > 50%
Prevalence of Core Hot-Spots
0.6%
0.0%
6.0%
Smart routing can better utilize core
and avoid hotspots
0.0%
24.0%
0.0%
0
10
20
30
Time (in Hours)
40
50
• Low persistence: very few concurrent hotspots
• High persistence: few concurrent hotspots
• High prevalence: < 25% are hotspots at any time
Insights Gained
• 75% of traffic stays within a rack (Clouds)
– Applications are not uniformly placed
• Traffic is bursty at the edge
• At most 25% of core links highly utilized
– Effective routing algorithm to reduce utilization
– Load balance across paths and migrate VMs
Road Map
• Understanding Data center traffic
• Improving network level performance
• Ongoing work
Options for TE in Data Centers?
• Current supported techniques
– Equal Cost MultiPath (ECMP)
– Spanning Tree Protocol (STP)
• Proposed
– Fat-Tree, VL2
• Other existing WAN techniques
– COPE,…, OSPF link tuning
How do we evaluate TE?
• Simulator
– Input: Traffic matrix, topology, traffic engineering
– Output: Link utilization
• Optimal TE
– Route traffic using knowledge of future TM
• Data center traces
– Cloud data center (CLD)
• Map-reduce app
• ~1500 servers
– University data center (UNV)
• 3-Tier Web apps
• ~500 servers
Draw Backs of Existing TE
• STP does not use multiple path
– 40% worst than optimal
• ECMP does not adapt to burstiness
– 15% worst than optimal
Design Goals for Ideal TE
Design Requirements for TE
• Calculate paths & reconfigure
network
– Use all network paths
– Use global view
• Avoid local optimals
– Must react quickly
• React to burstiness
• How predictable is traffic?
….
Is Data Center Traffic Predictable?
27%
99%
• YES! 27% or more of traffic matrix is predictable
• Manage predictable traffic more intelligently
How Long is Traffic Predictable?
1.5 – 5.0
1.6 - 2.5
• Different patterns of predictability
• 1 second of historical data able to predict future
MicroTE
MicroTE: Architecture
Monitoring Component
Routing Component
Network Controller
•
Global view:
– Created by network controller
•
React to predictable traffic:
– Routing component tracks demand history
•
All N/W paths:
– Routing component creates routes using all paths
Architectural Questions
• Efficiently gather network state?
• Determine predictable traffic?
• Generate and calculate new routes?
• Install network state?
Architectural Questions
• Efficiently gather network state?
• Determine predictable traffic?
• Generate and calculate new routes?
• Install network state?
Monitoring Component
• Efficiently gather TM
• Only one server per ToR monitors traffic
• Transfer changed portion of TM
• Compress data
• Tracking predictability
– Calculate EWMA over TM (every second)
• Empirically derived alpha of 0.2
• Use time-bins of 0.1 seconds
Routing Component
New Global
View
Determine predictable ToRs
Calculate network routes for
predictable traffic
Set ECMP for unpredictable
traffic
Install routes
Routing Predictable Traffic
• LP formulation
– Constraints
• Flow conservation
• Capacity constraint
• Use K-equal length paths
– Objective
• Minimize link utilization
• Bin-packing heuristic
– Sort flows in decreasing order
– Place on link with greatest capacity
Implementation
• Changes to data center
– Switch
• Install OpenFlow firmware
– End hosts
• Add kernel module
• New component
– Network controller
• C++ NOX modules
Evaluation
Evaluation: Motivating Questions
• How does MicroTE Compare to Optimal?
• How does MicroTE perform under varying
levels of predictability?
• How does MicroTE scale to large DCN?
• What overheard does MicroTE impose?
Evaluation: Motivating Questions
• How does MicroTE Compare to Optimal?
• How does MicroTE perform under varying
levels of predictability?
• How does MicroTE scale to large DCN?
• What overheard does MicroTE impose?
How do we evaluate TE?
• Simulator
– Input: Traffic matrix, topology, traffic engineering
– Output: Link utilization
• Optimal TE
– Route traffic using knowledge of future TM
• Data center traces
– Cloud data center (CLD)
• Map-reduce app
• ~1500 servers
– University data center (UNV)
• 3-Tier Web apps
• ~400 servers
Performing Under Realistic Traffic
• Significantly outperforms ECMP
• Slightly worse than optimal (1%-5%)
• Bin-packing and LP of comparable performance
Performance Versus Predictability
100
MLU
80
60
40
20
0
Time (in Secs)
ECMP
MicroTE
Optimal
• Low predictability  performance is similar to ECMP
Performance Versus Predictability
120
100
MLU
80
60
40
20
0
Time (in Secs)
ECMP
MicroTE
Optimal
• Low predictability  performance is similar to ECMP
• High predictability  performance is comparable to Optimal
• MicroTE adjusts according to predictability
Conclusion
• Study existing TE
– Found them lacking (15-40%)
• Study data center traffic
– Discovered traffic predictability (27% for 2 secs)
• Developed guidelines for ideal TE
• Designed and implemented MicroTE
– Brings state of the art within 1-5% of Ideal
– Efficiently scales to large DC (16K servers)
Road Map
• Understanding Data center traffic
• Improving network level performance
• Ongoing work
Looking forward
• Stop treating the network as a carrier of bits
• Bits in the network have a meaning
– Applications know this meaning.
• Can applications control networks?
– E.g Map-reduce
• Scheduler performs network aware task placement and
flow placement

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