1. Cloud Computing and MapReduce
Used slides from RAD Lab at UC Berkeley about the cloud ( and slides from Jimmy Lin’s slides ( (licensed under Creation Commons Attribution 3.0 License)

2. Cloud computing What is the “cloud”?
Many answers. Easier to explain with examples:
Gmail is in the cloud
Amazon (AWS) EC2 and S3 are the cloud
Google AppEngine is the cloud
Windows Azure is the cloud
SimpleDB is in the cloud
The “network” (cloud) is the computer

3. Cloud Computing What about Wikipedia?
“Cloud computing is the delivery of computing as a service rather than a product, whereby shared resources, software, and information are provided to computers and other devices as a utility (like the electricity grid) over a network (typically the Internet). “

4. Cloud Computing Computing as a “service” rather than a “product”
Everything happens in the “cloud”: both storage and computing
Personal devices (laptops/tablets) simply interact with the cloud
Advantages
Device agonstic – can seamlessly move from one device to other
Efficiency/scalability: programming frameworks allow easy scalability (relatively speaking)
Increasing need to handle “Big Data”
Reliability
Multi-tenancy (better on the cloud provider)
Cost: “pay as you go” allows renting computing resources as needed – much cheaper than building your own systems

5. more
Scalability means that you (can) have infinite resources, can handle unlimited number of users
Multi-tenancy enables sharing of resources and costs across a large pool of users. Lower cost, higher utilization… but other issues: e.g. security.
Elasticity: you can add or remove computer nodes and the end user will not be affected/see the improvement quickly.
Utility computing (similar to electrical grid)

6. X-as-a-Service

7. X-as-a-Service

8. Cloud Types

9. Data Centers The key infrastructure piece that enables CC
Everyone is building them
Huge amount of work on deciding how to build/design them

10. Data Centers

11. Data Centers

12. Data Centers Amazon data centers: Some old data
8 MW data center can include about 46,000 servers
Costs about $88 million to build (just the facility)
Power a pretty large portion, but server costs still dominate
source: James Hamilton Presentation

13. Data Centers Power distribution
Slides from 4-5 years ago
Data Centers
Power distribution
Almost 11% lost in distribution – starts mattering when the total power consumption is in millions
Modular and pre-fab designs
Fast and economic deployments, built in a factory
source: James Hamilton Presentation

14. Data Centers Networking equipment Cooling/temperature/energy issues
Very very expensive: server/storage prices dropping fast
Networking frozen in time: vertically integrated ecosystem
Bottleneck – forces workload placement restrictions
Cooling/temperature/energy issues
Appropriate placement of vents, inlets etc. a key issue
Thermal hotspots often appear and need to worked around
Overall cost of cooling is quite high
So is the cost of running the computing equipment
Both have led to issues in energy-efficient computing
Hard to optimize PUE (Power Usage Effectiveness) in small data centers
 may lead to very large data centers in near future
Ideally PUE should be 1, currently numbers are around
1.07 is a Facebook data center that does not have A/C
source: James Hamilton Presentation

15. MGHPCC Massachusetts Green High Performance Computing Center
MGHPCC in Holyoke, MA
Cost: $95M
8.6 acres, 10,000 high-end computers with hundreds of thousands of processor cores
10 MW power + 5MW for cooling/lighting
Close to electricity sources (hydroelectric plant)+ solar
More:

16. Amazon Web Services
11 AWS regions worldwide

17. Virtualization
Virtual machines (e.g., running Windows inside a Mac) etc. has been around for a long time
Used to be very slow…
Only recently became efficient enough to make it a key for CC
Basic idea: run virtual machines on your servers and sell time on them
That’s how Amazon EC2 runs
Many advantages:
“Security”: virtual machines serves as “almost” impenetrable boundary
Multi-tenancy: can have multiple VMs on the same server
Efficiency: replace many underpowered machines with a few high-power machines

18. Virtualization
Consumer VM products include VMWare, Parallels (for Mac), VirtualBox etc…
Some tricky things to keep in mind:
Harder to reason about performance (if you care)
Identical VMs may deliver somewhat different performance
Much continuing work on the virtualization technology itself

19. Docker Hottest thing right now…
Avoid the overheads of virtualization altogether

20. CLOUD COMPUTING ECONOMICS AND ELASTICITY20

21. Cloud Application Demand
Many cloud applications have cyclical demand curves
Daily, weekly, monthly, …
Demand
Time
Resources 21

22. Economics of Cloud Users
How do you pick a capacity level?
Economics of Cloud Users
Pay by use instead of provisioning for peak
Recall: DC costs >$150M and takes 24+ months to design and build
Demand
Capacity
Time
Resources
Demand
Capacity
Time
Resources
Unused resources
Static data center
Data center in the cloud 22

23. Economics of Cloud Users
Risk of over-provisioning: underutilization
Huge sunk cost in infrastructure
Demand
Capacity
Time
Resources
Unused resources
Resources
Demand
Capacity
Time (days) 1 2 3
Static data center 23

24. Utility Computing Arrives
Amazon Elastic Compute Cloud (EC2)
“Compute unit” rental: $ /hour
1 CU ≈ GHz 2007 AMD Opteron/Intel Xeon core
No up-front cost, no contract, no minimum
Billing rounded to nearest hour (also regional,spot pricing)
Platform
Units
Memory
Disk
Small - $0.10 $.085/hour
32-bit 1
1.7GB
160GB
Large - $0.40 $0.35/hour
64-bit 4
7.5GB
850GB – 2 spindles
X Large - $0.80 $0.68/hour 8
15GB
1690GB – 4 spindles
High CPU Med - $0.20 $0.17 5
350GB
High CPU Large - $0.80 $0.68 20
7GB
1690GB
High Mem X Large - $0.50
6.5
17.1GB
High Mem XXL - $1.20 13
34.2GB
High Mem XXXL - $2.40 26
68.4GB 24

25. Utility Storage Arrives
Amazon S3 and Elastic Block Storage offer low-cost, contract-less storage 25

27. Programming Frameworks
Third key piece emerged from efforts to “scale out”
i.e., distribute work over large numbers of machines (1000’s of machines)
Parallelism has been around for a long time
Both in a single machine, and as a cluster of computers
But always been considered very hard to program, especially the distributed kind
Too many things to keep track of
How to parallelize, how to distribute the data, how to handle failures etc etc..
Google developed MapReduce and BigTable frameworks, and ushered in a new era

28. Programming Frameworks
Note the difference between “scale up” and “scale out”
scale up usually refers to using a larger machine – easier to do
scale out refers to distributing over a large number of machines
Even with VMs, I still need to know how to distribute work across multiple VMs
Amazon’s largest single instance may not be enough

29. Cloud Computing Infrastructure
Computation model: MapReduce*
Storage model: HDFS*
Other computation models: HPC/Grid Computing
Network structure
*Some material adapted from slides by Jimmy Lin, Christophe Bisciglia, Aaron Kimball, & Sierra Michels-Slettvet, Google Distributed Computing Seminar, 2007 (licensed under Creation Commons Attribution 3.0 License) 29

30. Cloud Computing Computation Models
Finding the right level of abstraction
von Neumann architecture vs cloud environment
Hide system-level details from the developers
No more race conditions, lock contention, etc.
Separating the what from how
Developer specifies the computation that needs to be performed
Execution framework (“runtime”) handles actual execution
Similar to SQL!! 30

31. Typical Large-Data Problem
Map
Iterate over a large number of records
Extract something of interest from each
Shuffle and sort intermediate results
Aggregate intermediate results
Generate final output
Reduce
Key idea: provide a functional abstraction for these two operations – MapReduce
(Dean and Ghemawat, OSDI 2004) 31

32. MapReduce Programmers specify two functions:
map (k, v) → <k’, v’>*
reduce (k’, v’) → <k’, v’’>*
All values with the same key are sent to the same reducer
The execution framework handles everything else… 33

33. Shuffle and Sort: aggregate values by keys
MapReduce k1 k2 k3 k4 k5 k6 v1 v2 v3 v4 v5 v6
map
map
map
map b a 1 2 c 3 6 a c 5 2 b c 7 8
Shuffle and Sort: aggregate values by keys a 1 5 b 2 7 c 2 3 6 8
reduce
reduce
reduce r1 s1 r2 s2 r3 s3 34

34. MapReduce What’s “everything else”? Programmers specify two functions:
map (k, v) → <k’, v’>*
reduce (k’, v’) → <k’, v’>*
All values with the same key are sent to the same reducer
The execution framework handles everything else…
What’s “everything else”? 35

35. Sounds simple, but many challenges!
MapReduce “Runtime”
Handles scheduling
Assigns workers to map and reduce tasks
Handles “data distribution”
Moves processes to data
Handles synchronization
Gathers, sorts, and shuffles intermediate data
Handles errors and faults
Detects worker failures and automatically restarts
Handles speculative execution
Detects “slow” workers and re-executes work
Everything happens on top of a distributed FS (later)
Sounds simple, but many challenges! 36

36. MapReduce Programmers specify two functions:
map (k, v) → <k’, v’>*
reduce (k’, v’) → <k’, v’>*
All values with the same key are reduced together
The execution framework handles everything else…
Not quite…usually, programmers also specify:
partition (k’, number of partitions) → partition for k’
Often a simple hash of the key, e.g., hash(k’) mod R
Divides up key space for parallel reduce operations
combine (k’, v’) → <k’, v’>*
Mini-reducers that run in memory after the map phase
Used as an optimization to reduce network traffic 37

37. Shuffle and Sort: aggregate values by keys
map
map
map
map b a 1 2 c 3 6 a c 5 2 b c 7 8
combine
combine
combine
combine b a 1 2 c 9 a c 5 2 b c 7 8
partition
partition
partition
partition
Shuffle and Sort: aggregate values by keys a 1 5 b 2 7 c 2 3 6 8 c 2 9 8
reduce
reduce
reduce r1 s1 r2 s2 r3 s3 38

38. Two more details… Barrier between map and reduce phases
But we can begin copying intermediate data earlier
Keys arrive at each reducer in sorted order
No enforced ordering across reducers 39

39. MapReduce Overall Architecture
User Program
(1) submit
Master
(2) schedule map
(2) schedule reduce
worker
split 0
(5) remote read
(6) write
output
file 0
split 1
worker
(3) read
split 2
(4) local write
worker
split 3
output
file 1
split 4
worker
worker
Input
files
Map
phase
Intermediate files
(on local disk)
Reduce
phase
Output
files
Adapted from (Dean and Ghemawat, OSDI 2004) 40

40. “Hello World” Example: Word Count
Map(String docid, String text):
for each word w in text:
Emit(w, 1);
Reduce(String term, Iterator<Int> values):
int sum = 0;
for each v in values:
sum += v;
Emit(term, value); 41

41. MapReduce can refer to…
The programming model
The execution framework (aka “runtime”)
The specific implementation
Usage is usually clear from context! 42

42. MapReduce Implementations
Google has a proprietary implementation in C++
Bindings in Java, Python
Hadoop is an open-source implementation in Java
Development led by Yahoo, used in production
Now an Apache project
Rapidly expanding software ecosystem, but still lots of room for improvement
Lots of custom research implementations
For GPUs, cell processors, etc. 43

43. Cloud Computing Storage, or how do we get data to the workers?
NAS
SAN
Compute Nodes
What’s the problem here? 44

44. Distributed File System
Don’t move data to workers… move workers to the data!
Store data on the local disks of nodes in the cluster
Start up the workers on the node that has the data local
Why?
Network bisection bandwidth is limited
Not enough RAM to hold all the data in memory
Disk access is slow, but disk throughput is reasonable
A distributed file system is the answer
GFS (Google File System) for Google’s MapReduce
HDFS (Hadoop Distributed File System) for Hadoop 45

45. GFS: Assumptions Choose commodity hardware over “exotic” hardware
Scale “out”, not “up”
High component failure rates
Inexpensive commodity components fail all the time
“Modest” number of huge files
Multi-gigabyte files are common, if not encouraged
Files are write-once, mostly appended to
Perhaps concurrently
Large streaming reads over random access
High sustained throughput over low latency
GFS slides adapted from material by (Ghemawat et al., SOSP 2003) 46

46. GFS: Design Decisions Files stored as chunks
Fixed size (64MB)
Reliability through replication
Each chunk replicated across 3+ chunkservers
Single master to coordinate access, keep metadata
Simple centralized management
No data caching
Little benefit due to large datasets, streaming reads
Simplify the API
Push some of the issues onto the client (e.g., data layout)
HDFS = GFS clone (same basic ideas implemented in Java) 47

47. From GFS to HDFS Terminology differences: Functional differences:
GFS master = Hadoop namenode
GFS chunkservers = Hadoop datanodes
Functional differences:
No file appends in HDFS (was planned)
HDFS performance is (likely) slower 48

48. HDFS Architecture … … HDFS namenode /foo/bar (file name, block id)
Application
/foo/bar
(file name, block id)
File namespace
HDFS Client
block 3df2
(block id, block location)
HDFS datanode
Linux file system …
instructions to datanode
HDFS datanode
Linux file system …
datanode state
(block id, byte range)
block data
Adapted from (Ghemawat et al., SOSP 2003) 49

49. Namenode Responsibilities
Managing the file system namespace:
Holds file/directory structure, metadata, file-to-block mapping, access permissions, etc.
Coordinating file operations:
Directs clients to datanodes for reads and writes
No data is moved through the namenode
Maintaining overall health:
Periodic communication with the datanodes
Block re-replication and rebalancing
Garbage collection 50

50. Putting everything together…
namenode
job submission node
namenode daemon
jobtracker
tasktracker
tasktracker
tasktracker
datanode daemon
datanode daemon
datanode daemon
Linux file system
Linux file system
Linux file system … … …
slave node
slave node
slave node 51

51. MapReduce/GFS Summary
Simple, but powerful programming model
Scales to handle petabyte+ workloads
Google: six hours and two minutes to sort 1PB (10 trillion 100-byte records) on 4,000 computers
Yahoo!: hours to sort 1PB on 3,800 computers
Incremental performance improvement with more nodes
Seamlessly handles failures, but possibly with performance penalties 52