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Big Data Management and Systems Design
Dr. Weikuan Yu
Associate Professor
Department of Computer Science
Florida State University
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Oct 15, 2015 – CS5935 - S2
Life as a Graduate Student
· Course work– Degrees vs. skill sets
· Research– Goals vs. means
· Career development– Presentation and teaching skills– Communication and social skills– Internships– Networking
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Oct 15, 2015 – CS5935 - S3
Big Data Challenge
Sources: 1: http://www.infobarrel.com/Evolution 2:http://visual.ly/big-data-explosion?utm_source=visually_embed
Where is Data Coming From ?
3.0 BillionInternet Users
1.35 BillionFacebook Users
550 MillionTweets Per Day
72 Hrs VideoPer Minutes
4.7 Billion Google Search Per Day
And so many others
The dawn of civilization
Year 2003
5 EXABYTES of DATA 5 EXABYTES of DATAEvery 2 Days
Year 2015
7910EXABYTES
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Oct 15, 2015 – CS5935 - S4
Big Data Ecosystem
HDFS Hbase
Storage
DisksNetworks
Mahout, Giraph, Pregel, R
MR/Hadoop
Hive, Pig, Shark, Flume
Storm RamCloudSpark
Infrastructure
Applications
Run-Time
Hardware
MemCached
VM, Containers, Public and Private CloudsOS
ComplexityData
Insight
Processors(Accelerators)
Services
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Oct 15, 2015 – CS5935 - S5
Research Strategies?· Descriptive research
– Examine and collect facts to document patterns
· Discovery oriented research– Inductive reasoning from patterns to general discoveries
· Engineering-based research– Use existing techniques and theories to create a
technology or tool.
· Hypothesis-driven research– Make a hypothesis and then test the hypothesis using
deductive reasoning
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Oct 15, 2015 – CS5935 - S6
Overview of Hadoop MapReduce
Split MapTask
…. ….
Split MapTask
Split MapTask
MOF
MOF
MOF
MOF ReduceTask
MEM
map
ReduceTask
ReduceTask
ReduceTask
shuffle/merge reduce
DFSDFS
JobTrackerAssign MapTasks Assign ReduceTasks
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Oct 15, 2015 – CS5935 - S7
1 2 3 4 5 6 70
10
20
30
40
50
60
0
500
1000
1500
2000
2500
3000
Standalone Execution Time (sec)
Sta
nd
alo
ne E
xecu
tion
Tim
e (s
ec)
Groups
Normalized Execution Time (slowdown)
No
rma
lize
d E
xecu
tion
Tim
e
1.5×
Small Job Starvation within Hadoop
52×
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Oct 15, 2015 – CS5935 - S8
Hadoop Fair Scheduler
· The mostly widely used Hadoop scheduler
· It is designed to provide fairness among concurrently running jobs.
· Tasks occupy slots until completion or failure
Job Arrival
J-1
J-1
J-1
J-2
J-2
J-2
J-2
J-2
J-3
J-3 J-3
J-3
J-3
J-2
J-2
Time
shuffle
reduce
Slot-R1
Slot-R2
Slot-R3
Slot-M1
Slot-M2
Slot-M3
Slot-M4
Slot-M5
5 Map Slots
3 Reduce Slots
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Oct 15, 2015 – CS5935 - S9
How to achieve both Efficiency and Fairness?
· How to correct the non-preemptive nature of reduce tasks for flexible and dynamic allocation of reduce slots?
– Existing schedulers are not aware of such behavior. Once a reduce task is
launched, it stays with the reduce slot till the end.
· How to better schedule two different types of tasks?
– Hadoop schedules both map and reduce tasks with a similar max-min fair
sharing policy without paying attention to their relationship in a job.
– Map and reduce slots need to be dynamically, and proportionally shared.
Objective
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Oct 15, 2015 – CS5935 - S10
Preemptive ReduceTasks
· Different from Linux command “Kill –STOP $PID”.
· Lightweight work-conserving preemption mechanism.
– Provides any-time preemption with negligible performance impact.
– Allows a reduce task to resume from where it was preempted.
– Preserves previous computation and I/O.
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Oct 15, 2015 – CS5935 - S11
TaskTracker
Heap
Seg
men
t
Merge
seg seg seg
Retrieve
R1: Before Preempt
Heapseg
R1: After Resume
Index
Seg
men
t
Preemption During Shuffle Phase
· Only merge the in-memory segs, while maintaining on-disk segs untouched
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Oct 15, 2015 – CS5935 - S12
R1: Before Preempt
MPQ
R1: After Resume
MPQ
DFS
Index
Flush
Ret
rieve
offset
Preemption During Reduce Phase
· Preemption is occurred at the boundary of intermediate <key, val> pairs.
· Recording the current offset of each segment and minimum priority queue
TaskTracker
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Oct 15, 2015 – CS5935 - S13
Evaluation of Preemptive ReduceTask
10% 30% 50% 70% 90%3000
4000
5000
6000
7000Work-Conserving PreemptionKilling PreemptionNo Preemption (Baseline)
Completion Ratio of ReduceTask
Jo
b E
xe
cu
tio
n T
ime
(s
e)
Negligible overhead
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Oct 15, 2015 – CS5935 - S14
Fast Completion Scheduler
· Strategy: – Find a reduce task for Preemption and select another for Launching,
and balance the utilization of reduce slots
– Decisions to make
· Which reduce task to preempt and for how many times?
· Which one to launch and on which slot/node?
· How to avoid starvation and achieve locality?
· New progress metrics of a job: – Remaining shuffle time
– Remaining data (<k,v> pairs) to be reduced
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Oct 15, 2015 – CS5935 - S15
FCS Algorithms
· Preemption algorithm– Select a reduce task from a job with longer remaining time + more
remaining data
– Task slackness: the number of times a task has been preempted
– Avoid starvation: do not preempt a reduce task that has a big slackness.
· Launching algorithm– Select another reduce task from a job with the least remaining time /
remaining data
– Delay a reduce task to maximize data locality.
– Avoid aggressive delay: set a threshold based on the cluster size.
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Oct 15, 2015 – CS5935 - S16
1 2 3 4 5 6 7 8 9 1010
100
1000
10000
FCS HFS
· FCS reduces average execution time by 31% (171 jobs).
· Significantly speeds up small jobs at a small cost of big jobs.
Ave
rage
Exe
cutio
n T
ime
(sec
)
10 Groups of Jobs
1.9 2.4 1.9 2.3
1.6
1.9
1.12.2 0.79
Results for Map-heavy Workload
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Oct 15, 2015 – CS5935 - S17
1 2 3 4 5 6 7 8 9 101
10
100
1000
10000FCS HFS
Av
era
ge
Re
du
ce
Ta
sk
Wa
it
Tim
e (
se
c)
· Small jobs are benefited from significantly shortened reduce wait time.
· Waiting time are reduced by 22× for the jobs in the first 6 groups.
Average ReduceTask Wait Time
19.512.4
2221 32.2
1.224.5
0.5
0.8
10 Groups of Jobs
27.2
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Oct 15, 2015 – CS5935 - S18
1 2 3 4 5 6 7 8 9 1002468
101214161820
Fair Comple-tion
Max
imum
Slo
wdo
wn
· Nearly uniform maximum slowdown for all groups of jobs.
· FCS improves the fairness by 66.7% on average.
10 groups of jobs
Fairness Evaluation: Maximum Slowdown
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Oct 15, 2015 – CS5935 - S19
Summary for Coordinated Scheduling
· Identified the fairness and efficiency issues because of the lack of scheduling coordination.
· Introduced Preemptive ReduceTasks for efficient preemption of reduce tasks from long-running jobs
· Designed and Implemented Fast Completion Scheduler for fast execution of small jobs and better job fairness.
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Oct 15, 2015 – CS5935 - S20
Broad Research Interests
Big Data· System Design and
Management– Fast Data Movement– Efficient job management– Multi-purpose framework
· High Performance Computing– Parallel computing models– Scalable I/O & communication – Computation & I/O optimization
· Data Analytics– K-mer indexing for sequence
fingerprinting and alignment– Scalable Image Processing– Fast community detection
· Security and Reliability– Analytics logging and Recovery– Cloud security– Storage security
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Oct 15, 2015 – CS5935 - S21
Resource and Capabilities· Software: Unstructured Data
Accelerator (UDA)– Accelerator for Big Data Analytics– Transferred to Mellanox
· In-house big data platform– 22 nodes; InfiniBand and 10GigE– SSD and GPGPU (Phi and Kepler)– Donations from Mellanox, Solarflare,
and NVIDIA
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Oct 15, 2015 – CS5935 - S22
Sponsors, Contractors, and Collaborators· Current Sponsors and Contractors:
– NSF: Two active grants on big data analytics, storage and network systems, – LLNL: burst buffer based storage systems
· Past Sponsors and Contractors– NASA: one grant for for I/O Optimization of climate applications– DOE Labs: many contracts for high-performance computing.– Industry: contracts from Intel, Mellanox, NVIDIA and Scitor; – Alabama: Innovation Award; Auburn IGP for TigerCloud.
· Collaborators:– IBM, Intel, Mellanox, Scitor, Solarflare, AMD– LBNL, ORNL, LLNL, SNL, GSFC, LPS– Illinois Tech, Clemson, College of William Mary, NJIT, Georgia Tech, Auburn
(OIT, Physics, Biology, SFWS)
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Oct 15, 2015 – CS5935 - S23
Research Directions
· Main Thrusts
· Big Data Analytics and systems design
· Network and data privacy and security
· Interdisciplinary data-driven computational research
· Key: solve challenging problems with novel strategies…
· Collaborations
· Students (systems/network oriented, interdisciplinary)
· Faculty (on and off campus)
· National laboratories
· Industry: IBM, Intel, and many more
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Oct 15, 2015 – CS5935 - S24
Student Cultivation and Team Building Numerous Internships
ORNL, Sandia, LANL, LLNL, IBM. Awards and honors
First Prize of ACM Grand Finals SC11 Fellowship ($5000). Outstanding students: 2011-2014.
Alumni (Ph.D. listed) Yuan Tian – ORNL Xinyu Que – IBM T.J. Watson Yandong Wang – IBM Watson Zhuo Liu – Yahoo! Cong Xu – Intel Bin Wang – Arista Networks
Current Team 4 Ph.D. students