Cloud Computing Systems MapReduce Limitations

Cloud Computing Systems
Lin Gu
Hong Kong University of Science and Technology
Sept. 14, 2011

How to effectively compute in a datacenter?
Is MapReduce the best answer to computation in the cloud?
What is the limitation of MapReduce?
How to provide general-purpose parallel processing in
DCs?

• MapReduce—parallel computing for Web-scale
data processing
• Fundamental component in Google’s
technological architecture
– Why didn’t Google use parallel Fortran, MPI, …?
• Followed by many technology firms
The MapReduce Approach
Program Execution on Web-Scale DataProgram Execution on Web-Scale Data

MapReduce
Old ideas can be fabulous, too!
( = Lisp “Lost In Silly Parentheses”) ?
• Map and Fold
– Map: do something to all elements in a list
– Fold: aggregate elements of a list
• Used in functional programming languages
such as Lisp

• Map is a higher-order function: apply an op to all
elements in a list
– Result is a new list
• Parallelizable
f f f f f
MapReduce
(map (lambda (x) (* x x))
'(1 2 3 4 5))
→ '(1 4 9 16 25)

• Reduce is also a higher-order function
• Like “fold”: aggregate elements of a list
– Accumulator set to initial value
– Function applied to list element and the accumulator
– Result stored in the accumulator
– Repeated for every item in the list
– Result is the final value in the accumulator
f f f f f
final result
Initial value
(fold + 0 '(1 2 3 4 5))
→ 15
(fold * 1 '(1 2 3 4 5))
→ 120

Massive parallel processing made simple
• Example: word count
• Map: parse a document and generate <word, 1> pairs
• Reduce: receive all pairs for a specific word, and count
(sum)
// D is a document
for each word w in D
output <w, 1>
Map Reduce
Reduce for key w:
count = 0
for each input item
count = count + 1
output <w, count>

Design Context
• Big data, but simple dependence
– Relatively easy to partition data
• Supported by a distributed system
– Distributed OS services across thousands of
commodity PCs (e.g., GFS)
• First users are search oriented
– Crawl, index, search
Designed years ago, still working today, growing adoptions

Single Master node
Worker threads
Worker threads
Workflow
Single master, numerous worker threads

Workflow
• 1. The MapReduce library in the user program first
splits the input files into M pieces of typically 16
megabytes to 64 megabytes (MB) per piece. It then
starts up many copies of the program on a cluster of
machines.
• 2. One of the copies of the program is the master. The
rest are workers that are assigned work by the master.
There are M map tasks and R reduce tasks to assign.
The master picks idle workers and assigns each one a
map task or a reduce task.

Workflow
• 3. A worker who is assigned a map task reads the
contents of the corresponding input split. It parses
key/value pairs out of the input data and passes each
pair to the user-defined Map function. The
intermediate key/value pairs produced by the Map
function are buffered in memory.
• 4. Periodically, the buffered pairs are written to local
disk, partitioned into R regions by the partitioning
function. The locations of these buffered pairs on the
local disk are passed back to the master, who is
responsible for forwarding these locations to the
reduce workers.

Workflow
• 5. When a reduce worker is notified by the master about
these locations, it uses RPCs to read the buffered data
from the local disks of the map workers. When a reduce
worker has read all intermediate data, it sorts it by the
intermediate keys so that all occurrences of the same key
are grouped together.
• 6. The reduce worker iterates over the sorted
intermediate data and for each unique intermediate key
encountered, it passes the key and the corresponding set
of intermediate values to the Reduce function. The output
of the Reduce function is appended to a final output file
for this reduce partition.
• 7. When all map tasks and reduce tasks have been
completed, the MapReduce returns back to the user code.

Programming
• How to write a MapReduce programto
–Generate inverted indices?
–Sort?
• How to express more sophisticated
logic?
• What if some workers (slaves) or the
master fails?

Workflow
Where is the communication-intensive part?
Initial data split
into 64MB blocks
Computed, results
locally stored
Master informed of
result locations
R reducers retrieve
Data from mappers
Final output written

• Distributed, scalable storage for key-value pairs
• Example: Dynamo (Amazon)
• Another example may be P2P storage (e.g., Chord)
• Key-value store can be a general foundation for more
complex data structures
• But performance may suffer
Data Storage – Key-Value Store

Data Storage – Key-Value Store
Dynamo: a decentralized, scalable key-value
store
– Used in Amazon
– Use consistent hashing to distributed data
among nodes
– Replicated, versioning, load balanced
– Easy-to-use interface: put()/get()

• Networked block storage
– ND by SUN Microsystems
• Remote block storage over Internet
– Use S3 as a block device [Brantner]
• Block-level remote storage may become slow in
networks with long latencies
Data Storage – Network Block Device

• PC file systems
• Link together all clusters of a file
– Directory entry: filename, attributes, date/time,
starting cluster, file size
• Boot sector (superblock) : file system wide
information
• File allocation table, root directory, …
Data Storage – Traditional File Systems
Boot
sector
FAT 1 FAT 2
(dup)
ROOT dir Normal directories and files

• NFS—Network File System [Sandberg]
– Designed by SUN Microsystems in the 1980’s
• Transparent remote access to files stored
remotely
– XDR, RPC, VNode, VFS
– Mountable file system, synchronous behavior
• Stateless server
Data Storage – Network File System

NFS organization
Client Server
Data Storage – Network File System

• A distributed file system at work (GFS)
• Single master and numerous slaves communicate with each other
• File data unit, “chunk”, is up to 64MB. Chunks are replicated.
• “master” is a single point of failure and bottleneck of scalability,
the consistency model is difficult to use
Data Storage – Google File System (GFS)

22
E 75656 C
A 42342 E
B 42521 W
C 66354 W
D 12352 E
F 15677 E
E 75656 C
A 42342 E
B 42521 W
C 66354 W
D 12352 E
F 15677 E
CREATE TABLE Parts (
ID VARCHAR,
StockNumber INT,
Status VARCHAR
…
)
CREATE TABLE Parts (
ID VARCHAR,
StockNumber INT,
Status VARCHAR
…
)
Parallel databaseParallel database ReplicationReplication
Indexes and viewsIndexes and views
Structured schemaStructured schema
A 42342 E
B 42521 W
C 66354 W
D 12352 E
E 75656 C
F 15677 E
Data Storage – Database
Designed and used by
Yahoo!
PNUTS – a relational database service

MapReduce/Hadoop
• Around 2004, Google invented MapReduce to
parallelize computation of large data sets. It’s been a
key component in Google’s technology foundation
• Around 2008, Yahoo! developed the open-source
variant of MapReduce named Hadoop
• After 2008, MapReduce/Hadoop become a key
technology component in cloud computing
• In 2010, the U.S. conferred the MapReduce patent to
Google
MapReduce … Hadoop or variants …Hadoop

• MapReduce provides an easy-to-use framework for parallel
programming, but is it the most efficient and best solution to
program execution in datacenters?
• MapReduce has its discontents
– DeWitt and Stonebraker: “MapReduce: A major step backwards” –
MapReduce is far less sophisticated and efficient than parallel query
processing
• MapReduce is a parallel processing framework, not a database
system, nor a query language
– It is possible to use MapReduce to implement some of the parallel query
processing functions
– What are the real limitations?
• Inefficient for general programming (and not designed for that)
– Hard to handle data with complex dependence, frequent updates, etc.
– High overhead, bursty I/O, difficult to handle long streaming data
– Limited opportunity for optimization
MapReduce—LimitationsMapReduce—Limitations

Critiques
MapReduce: A major step backwards
-- David J. DeWitt and Michael Stonebraker
(MapReduce) is
– A giant step backward in the programming paradigm for large-
scale data intensive applications
– A sub-optimal implementation, in that it uses brute force
instead of indexing
– Not novel at all
– Missing features
– Incompatible with all of the tools DBMS users have come to
depend on

• Inefficient for general programming (and not designed
for that)
– Hard to handle data with complex dependence, frequent
updates, etc.
– High overhead, bursty I/O
• Experience with developing a Hadoop-based distributed
compiler
– Workload: compile Linux kernel
– 4 machines available to Hadoop for parallel compiling
– Observation: parallel compiling on 4 nodes with Hadoop can
be even slower than sequential compiling on one node
MapReduce—LimitationsMapReduce—Limitations

• Proprietary solution developed in an environment with
one prevailing application (web search)
– The assumptions introduce several important constraints in
data and logic
– Not a general-purpose parallel execution technology
• Design choices in MapReduce
– Optimizes for throughput rather than latency
– Optimizes for large data set rather than small data structures
– Optimizes for coarse-grained parallelism rather than fine-
grained
Re-thinking MapReduceRe-thinking MapReduce

• A lightweight parallelization framework following the
MapReduce paradigm
– Implemented in C++
– More than just an efficient implementation of MapReduce
– Goal: a lightweight “parallelization” service that programs
can invoke during execution
• MRlite follows several principles
– Memory is media—avoid touching hard drives
– Static facility for dynamic utility—use and reuse threads
for map tasks
MRlite: Lightweight Parallel ProcessingMRlite: Lightweight Parallel Processing

MRlite ： Towards Lightweight, Scalable, and
General Parallel Processing
MRlite clientMRlite client
MRlite master
scheduler
MRlite master
scheduler
slaveslave
slaveslave
slaveslave
slaveslave
applicationapplication
Data flow
Command flow
Linked together with the
app, the MRlite client
library accepts calls from
app and submits jobs to
the master
Linked together with the
app, the MRlite client
library accepts calls from
app and submits jobs to
the master High speed distributed
storage, stores
intermediate files
High speed distributed
storage, stores
intermediate files
The MRlite master accepts jobs
from clients and schedules them
to execute on slaves
The MRlite master accepts jobs
from clients and schedules them
to execute on slaves
Distributed nodes
accept tasks from
master and execute
them
Distributed nodes
accept tasks from
master and execute
them

30
Computing Capability
Using MRlite, the parallel compilation jobs, mrcc, is 10Using MRlite, the parallel compilation jobs, mrcc, is 10
times faster than that running on Hadoop!times faster than that running on Hadoop!
Z. Ma and L. Gu. The Limitation of MapReduce: a
Probing Case and a Lightweight Solution. CLOUD
COMPUTING 2010

Network activities under MapReduce/Hadoop workload
• Hadoop: open-source implementation of MapReduce
• Processing data with 3 servers (20 cores)
– 116.8GB input data
• Network activities captured with Xen virtual
machines
Inside MapReduce-Style ComputationInside MapReduce-Style Computation

• Packet reception under MapReduce/Hadoop workload
– Large data volume
– Bursty network traffic
• Genrality—widely observed in MapReduce workloads
Packet reception
on a slave server
Inside MapReduceInside MapReduce

Packet reception on the master server

Packet transmission on the master server

Major Components of a Datacenter
• Computing hardware (equipment racks)
• Power supply and distribution hardware
• Cooling hardware and cooling fluid
distribution hardware
• Network infrastructure
• IT Personnel and office equipment
Datacenter Networking

Growth Trends in Datacenters
• Load on network & servers continues to rapidly grow
– Rapid growth: a rough estimate of annual growth rate:
enterprise data centers: ~35%, Internet data centers: 50% -
100%
– Information access anywhere, anytime, from many devices
• Desktops, laptops, PDAs & smart phones, sensor
networks, proliferation of broadband
• Mainstream servers moving towards higher speed links
– 1-GbE to10-GbE in 2008-2009
– 10-GbE to 40-GbE in 2010-2012
• High-speed datacenter-MAN/WAN connectivity
– High-speed datacenter syncing for disaster recovery

• A large part of the total cost of the DC hardware
– Large routers and high-bandwidth switches are very
expensive
• Relatively unreliable – many components may fail.
• Many major operators and companies design their
own datacenter networking to save money and
improve reliability/scalability/performance.
– The topology is often known
– The number of nodes is limited
– The protocols used in the DC are known
• Security is simpler inside the data center, but
challenging at the border
• We can distribute applications to servers to distribute
load and minimize hot spots

Networking components (examples)
• High Performance & High
Density Switches & Routers
– Scaling to 512 10GbE ports per
chassis
– No need for proprietary
protocols to scale
• Highly scalable DC
Border Routers
– 3.2 Tbps capacity in a single
chassis
– 10 Million routes, 1 Million in
hardware
– 2,000 BGP peers
– 2K L3 VPNs, 16K L2 VPNs
– High port density for GE and
10GE application connectivity
– Security
768 1-GE port Downstream
64 10-GE port Upstream

Common data center topology
Internet
Servers
Layer-2 switchAccess
Data Center
Layer-2/3 switchAggregation
Layer-3 routerCore

Data center network design goals
• High network bandwidth, low latency
• Reduce the need for large switches in the core
• Simplify the software, push complexity to the
edge of the network
• Improve reliability
• Reduce capital and operating cost

Avoid this…
Data Center Networking
and simplify this…and simplify this…

??
Can we avoid using high-end switches?
• Expensive high-end switches to
scale up
• Single point of failure and
bandwidth bottleneck
– Experiences from real systems
• One answer: DCell
43
Interconnect

DCell Ideas
• #1: Use mini-switches to scale out
• #2: Leverage servers to be part of the routing
infrastructure
– Servers have multiple ports and need to forward
packets
• #3: Use recursion to scale and build complete
graph to increase capacity
Interconnect

One approach: switched network with
a hypercube interconnect
• Leaf switch: 40 1Gbps ports+2 10 Gbps ports.
– One switch per rack.
– Not replicated (if a switch fails, lose one rack of
capacity)
• Core switch: 10 10Gbps ports
– Form a hypercube
• Hypercube – high-dimensional rectangle

Hypercube properties
• Minimum hop count
• Even load distribution for all-all communication.
• Can route around switch/link failures.
• Simple routing:
– Outport = f(Dest xor NodeNum)
– No routing tables
Interconnect

A 16-node (dimension 4) hypercube
0
32
10
0
12
3
3
1
30
0
2
1
5
47
3
2
10
118
1
1
1
1
1
2
2
22
2
0
0
0
0
3
3
3
3
Interconnect

Interconnect
How many servers can
be connected in this
system?
81920 servers with
1Gbps bandwidth
Core switch:
10Gbps port x 10
Leaf switch: 1Gbps port x
40 + 10Gbps port x 2.

The Black Box

Shipping Container as Data Center Module
• Data Center Module
– Contains network gear, compute, storage, &
cooling
– Just plug in power, network, & chilled water
• Increased cooling efficiency
– Water & air flow
– Better air flow management
• Meet seasonal load requirements
Data Center Network

Unit of Data Center Growth
• One at a time:
– 1 system
– Racking & networking: 14 hrs ($1,330)
• Rack at a time:
– ~40 systems
– Install & networking: .75 hrs ($60)
• Container at a time:
– ~1,000 systems
– No packaging to remove
– No floor space required
– Power, network, & cooling only
– Weatherproof & easy to transport
• Data center construction takes 24+
months
Data Center Network

Multiple-Site Redundancy and Enhanced
Performance using load balancing
• Handling site failures
transparently
• Providing best site
selection per user
• Leveraging both DNS and
non-DNS methods for
multi-site redundancy
• Providing disaster
recovery and non-stop
operation
LB system
DNS
Datacenter
Datacenter
Datacenter
LB (load balancing) System
• The load balancing systems regulate global data center traffic
• Incorporates site health, load, user proximity, and service response for user
site selection
• Provides transparent site failover in case of disaster or service outage
Global Data Center
Deployment Problems
Data Center Network

Challenges and Research Problems
Hardware
– High-performance, reliable, cost-effective
computing infrastructure
– Cooling, air cleaning, and energy efficiency
[Barraso]
Clusters
[Fan] Power
[Andersen]
FAWN
[Reghavendra]
Power

System software
– Operating systems
– Compilers
– Database
– Execution engines and containers
Ghemawat: GFS
Chang: Bigtable
DeCandia:
Dynamo
Brantner: DB on
S3
Cooper: PNUTS
Yu: DryadLINQ
Dean:
MapReduce
Burrows:
Chubby Isard: Quincy

Networking
– Interconnect and global network structuring
– Traffic engineering
Al-Fares:
Commodity DC
Guo 2008: DCell
Guo 2009: BCube

• Data and programming
– Data consistency mechanisms (e.g., replications)
– Fault tolerance
– Interfaces and semantics
• Software engineering
• User interface
• Application architecture
Pike: Sawzall
Olston: Pig
Latin
Buyya: IT
services

Resources
• [Al-Fares] Al-Fares, M., Loukissas, A., and Vahdat, A. A scalable, commodity data center
network architecture. In Proceedings of the ACM SIGCOMM 2008 Conference on Data
Communication (Seattle, WA, USA, August 17 - 22, 2008). SIGCOMM '08. 63-74.
http://baijia.info/showthread.php?tid=139
• [Andersen] David G. Andersen, Jason Franklin, Michael Kaminsky, Amar Phanishayee,
Lawrence Tan, Vijay Vasudevan. FAWN: A Fast Array of Wimpy Nodes. SOSP'09.
• [Barraso] Luiz Barroso, Jeffrey Dean, Urs Hoelzle, "Web Search for a Planet: The Google
Cluster Architecture," IEEE Micro, vol. 23, no. 2, pp. 22-28, Mar./Apr. 2003
• [Brantner] Brantner, M., Florescu, D., Graf, D., Kossmann, D., and Kraska, T. Building a
database on S3. In Proceedings of the 2008 ACM SIGMOD international Conference on
Management of Data (Vancouver, Canada, June 09 - 12, 2008). SIGMOD '08. 251-264.

Resources
• [Burrows] Burrows, M. The Chubby lock service for loosely-coupled distributed systems.
In Proceedings of the 7th Symposium on Operating Systems Design and Implementation
(Seattle, Washington, November 06 - 08, 2006). 335-350. .
• [Buyya] Buyya, R. Chee Shin Yeo Venugopal, S. Market-Oriented Cloud Computing. The
10th IEEE International Conference on High Performance Computing and
Communications, 2008. HPCC '08. http://baijia.info/showthread.php?tid=248
• [Chang] Chang, F., Dean, J., Ghemawat, S., Hsieh, W. C., Wallach, D. A., Burrows, M.,
Chandra, T., Fikes, A., and Gruber, R. E. Bigtable: a distributed storage system for
structured data. In Proceedings of the 7th Symposium on Operating Systems Design and
Implementation (Seattle, Washington, November 06 - 08, 2006). 205-218.
• [Cooper] Cooper, B. F., Ramakrishnan, R., Srivastava, U., Silberstein, A., Bohannon, P.,
Jacobsen, H., Puz, N., Weaver, D., and Yerneni, R. PNUTS: Yahoo!'s hosted data serving
platform. Proc. VLDB Endow. 1, 2 (Aug. 2008), 1277-1288.

Resources
• [Dean] Dean, J. and Ghemawat, S. 2004. MapReduce: simplified data processing on large
clusters. In Proceedings of the 6th Conference on Symposium on Opearting Systems
Design & Implementation - Volume 6 (San Francisco, CA, December 06 - 08, 2004).
• [DeCandia] DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A.,
Pilchin, A., Sivasubramanian, S., Vosshall, P., and Vogels, W. 2007. Dynamo: amazon's
highly available key-value store. In Proceedings of Twenty-First ACM SIGOPS Symposium
on Operating Systems Principles (Stevenson, Washington, USA, October 14 - 17, 2007).
SOSP '07. ACM, New York, NY, 205-220. http://baijia.info/showthread.php?tid=120
• [Fan] Fan, X., Weber, W., and Barroso, L. A. Power provisioning for a warehouse-sized
computer. In Proceedings of the 34th Annual international Symposium on Computer
Architecture (San Diego, California, USA, June 09 - 13, 2007). ISCA '07. 13-23.

Resources
• [Ghemawat] Ghemawat, S., Gobioff, H., and Leung, S. 2003. The Google file system. In
Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles
(Bolton Landing, NY, USA, October 19 - 22, 2003). SOSP '03. ACM, New York, NY, 29-43.
• [Guo 2008] Chuanxiong Guo, Haitao Wu, Kun Tan, Lei Shi, Yongguang Zhang, and
Songwu Lu, DCell: A Scalable and Fault-Tolerant Network Structure for Data Centers, in
ACM SIGCOMM 08. http://baijia.info/showthread.php?tid=142
• [Guo 2009] Chuanxiong Guo, Guohan Lu, Dan Li, Xuan Zhang, Haitao Wu, Yunfeng Shi,
Chen Tian, Yongguang Zhang, and Songwu Lu, BCube: A High Performance, Server-
centric Network Architecture for Modular Data Centers, in ACM SIGCOMM 09.
• [Isard] Michael Isard, Vijayan Prabhakaran, Jon Currey, Udi Wieder, Kunal Talwar and
Andrew Goldberg. Quincy: Fair Scheduling for Distributed Computing Clusters. SOSP'09.

Resources
• [Olston] Olston, C., Reed, B., Srivastava, U., Kumar, R., and Tomkins, A. 2008. Pig Latin: a
not-so-foreign language for data processing. In Proceedings of the 2008 ACM SIGMOD
international Conference on Management of Data (Vancouver, Canada, June 09 - 12,
2008). SIGMOD '08. 1099-1110. http://baijia.info/showthread.php?tid=124
• [Pike] Pike, R., Dorward, S., Griesemer, R., and Quinlan, S. 2005. Interpreting the data:
Parallel analysis with Sawzall. Sci. Program. 13, 4 (Oct. 2005), 277-298.
• [Reghavendra] Ramya Raghavendra, Parthasarathy Ranganathan, Vanish Talwar, Zhikui
Wang, Xiaoyun Zhu. No "Power" Struggles: Coordinated Multi-level Power Management
for the Data Center. In Proceedings of the International Conference on Architectural
Support for Programming Languages and Operating Systems (ASPLOS), Seattle, WA,
March 2008. http://baijia.info/showthread.php?tid=183
• [Yu] Y. Yu, M. Isard, D. Fetterly, M. Budiu, Ãš. Erlingsson, P. K. Gunda, and J. Currey.
DryadLINQ: A system for general-purpose distributed data-parallel computing using a
high-level language. In Proceedings of the 8th Symposium on Operating Systems Design
and Implementation (OSDI), December 8-10 2008. http://baijia.info/showthread.php?
tid=5

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