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THE NATURE OF DATACENTER:
MEASUREMENTS & ANALYSIS
Srikanth Kandula, Sudipta Sengupta, Albert Greenberg, Parveen Patel,
Ronnie Chaiken
Microsoft Research
IMC November, 2009
Abhishek Ray
[email protected]
Outline
 Introduction
 Data & Methodology
 Application
 Traffic Characteristics
 Tomography
 Conclusion
Introduction
 Analysis and mining of data sets
 Processing around some petabytes of data
 This paper has tried to describe characteristics of
traffic
 Detailed view of traffic
 Congestion conditions and patterns
Contribution
 Measurement Instrumentation
 Measures traffic at data centers rather than switches
 Traffic characteristics
 Flow, congestion and rate of change of traffic mix.
 Tomography Inference Accuracy
 Performs
 Clusters =1500 servers
 Rack = 20
2 months
Data & Methodology
 ISPs
 SNMP Counters
 Sampled Flow
 Deep packet Inspection
 Data Center
 Measurements at Server
 Servers, Storage and network
 Linkage of network traffic with application level logs
 Socket level events at each servers
 ETW – Event Tracing for Windows
 One per application read or write
Aggregates over several packets
http://msdn.microsoft.com/en-us/magazine/cc163437.aspx#S1
ETW – Event tracing for Windows
Application Workload
 SQL Programming language like Scope
 3 phases of different types
 Extract
 Partition
 Aggregate
 Combine
 Short interactive programs to long running
programs
Traffic Characteristics
Patterns
Work-Seeks-BW and Scatter-Gather patterns in
datacenter traffic exchanged b/w server pairs
 Work-seeks-bandwidth
 Within same servers
 Within servers in same rack
 Within servers in same VLAN
 Scatter-gather-patterns
 Data is divided into small parts and each servers
works on particular part
 Aggregated
How much traffic is exchanged between server
pairs?
 Server pair with same rack are more likely to
exchange more bytes
 Probability of exchanging no traffic
 89% - servers within same rack
 99.5% - servers in different rack
How many other servers does a server correspond
with?
 Sever either talks to all other servers with the same
rack
 Servers doesn’t talk to servers outside the rack or
talks 1-10% outside servers.
Congestion within the
Datacenter
 N/W at as high an utilization as possible without
adversely affecting throughput
 Low network utilization indicate
 Application by nature demands more of other
resources such as CPU and disk than the network
 Applications can be re-written to make better use
of available network bandwidth
Where and when the congestion happens in
data center
Congestion Rate
 86% - 10 seconds
 15% - 100 seconds
 Short congestion periods are highly correlated
across many tens of links and are due to brief spurts
of high demand from the application
 Long lasting congestion periods tend to be
more localized to a small set of links
Length of Congestion Events
Compares the rates of flows that overlap high
utilization periods with the rates of all flows
Impact of high utilization
 Read failure - Job is killed
 Congestion
 To attribute network traffic to the applications that
generate it, they merge the network event logs with
logs at the application-level that describe which job
and phase were active at that time
 Reduce phase - Data in each partition that is present
at multiple servers in the cluster has to be pulled to
the server that handles the reduce for the partition
 e.g. count the number of records that begin with ‘A’
 Extract phase – Extracting the data
 Largest amount of data
 Evaluation phase – Problem
 Conclusion – High utilization epochs are caused by
application demand and have a moderate negative
impact to job performance
Flow Characteristics
Traffic mix changes frequently
How traffic changes over time within the data
center
 Change in traffic
 10th and 90th percentiles are 37% and 149%
 the median change in traffic is roughly 82%
 even when the total traffic in the matrix remains the
same, the server pairs that are involved in these
traffic exchanges change appreciably
 Short bursts cause spikes at the shorter time-scale
(in dashed line) that smooth out at the longer time
scale (in solid line) whereas gradual changes appear
conversely, smoothed out at shorter time-scales yet
pronounced on the longer time-scale
 Variability - key aspect for data center
Inter-arrival times in the entire cluster, at
Top-of-Rack switches and at servers
 Inter-arrivals at both servers and top-of-rack switches
have spaced apart by roughly 15ms
 This is likely due to the stop-and-go behavior of the
application that rate-limits the creation of new flows
 Median arrival rate of all flows in the cluster is 105
flows per second or 100 flows in every millisecond
Tomography
 N/W tomography methods to infer traffic matrices
 If the methods used in ISP n/w is applicable to
datacenters, it would help to unravel the nature of traffic
 Why?
 Data flow volume is quadratic n(n - 1) – no. of links
measurements are fewer
 Assumptions - Gravity model - Amount of traffic a node
(origin) would send to another node (destination) is
proportional to the traffic volume received by the
destination
 Scalability
Methodology
 Computes ground truth TM and measure how well
the TM estimated by tomography from these link
counts approximates the true TM
Tomogravity and Spare Maximization
 Tomogravity - Communication likely to be B/W
nodes with same job rather than all nodes, whereas
gravity model, not being aware of these job-clusters,
introduces traffic across clusters, resulting in many
non-zero TM entries
 Spare maximization – Error rate starts from several
hundreds
Comparison the TMs by various
tomography methods with the ground truth
 Ground TMs are sparser than tomogravity estimated
TMs, and denser than sparsity maximized estimated
TMs
Conclusion
 Capture both
 Macroscopic patterns – which servers talk to which
others, when and for what reasons
 Microscopic characteristics – flow durations, inter-arrival
times
 Tighter coupling between network, computing, and
storage in datacenter applications
 Congestion and negative application impact do occur,
demanding improvement - better understanding of
traffic and mechanisms that steer demand
My Take
 More data should be examined over a period of 1 year
instead of 2 months
 I would certainly like to see some mining of data and
application running at datacenters of companies like
Google, Yahoo etc
Related Work
 T. Benson, A. Anand, A. Akella, andM. Zhang:
Understanding Datacenter Traffic Characteristics,
In SIGCOMMWREN workshop, 2009.
 A. Greenberg, N. Jain, S. Kandula, C. Kim, P. Lahiri,
D. Maltz, P. Patel, and S. Sengupta:
VL2: A Scalable and Flexible Data Center Network,
In ACM SIGCOMM, 2009.
Thank You