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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?

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cloud computing systems

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?


Program Execution on Web-Scale Data

The MapReduce Approach

  • 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


  • 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

Old ideas can be fabulous, too!

( = Lisp “Lost In Silly Parentheses”) ?



  • Map is a higher-order function: apply an op to all elements in a list
    • Result is a new list
  • Parallelizable

(map (lambda (x) (* x x)) \'(1 2 3 4 5))  \'(1 4 9 16 25)







Program Execution on Web-Scale Data

The MapReduce Approach

  • 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

(fold + 0 \'(1 2 3 4 5))  15

(fold * 1 \'(1 2 3 4 5))  120






Initial value

final result


Program Execution on Web-Scale Data

The MapReduce Approach

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>

Reduce for key w:

count = 0

for each input item

count = count + 1

output <w, count>

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

Design Context

Designed years ago, still working today, growing adoptions


Single Master node

Worker threads

Worker threads


Single master, numerous worker threads

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.


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.


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.


How to write a MapReduce programto

Generate inverted indices?


How to express more sophisticated logic?

What if some workers (slaves) or the master fails?




Master informed ofresult locations

Initial data split

into 64MB blocks

R reducers retrieve

Data from mappers

Computed, resultslocally stored

Final output written

Where is the communication-intensive part?


Data Storage – Key-Value Store

  • 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
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()

Data Storage – Key-Value Store


Data Storage – Network Block Device

  • 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 – Traditional File Systems

  • 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, …

Boot sector


FAT 2 (dup)

ROOT dir

Normal directories and files


Data Storage – Network File System

  • 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

Client Server

NFS organization


Data Storage – Google File System (GFS)

  • 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

A 42342 E

A 42342 E

B 42521 W

B 42521 W

B 42521 W

C 66354 W

D 12352 E

E 75656 C

F 15677 E

A 42342 E

C 66354 W

C 66354 W

D 12352 E

D 12352 E

E 75656 C

E 75656 C

F 15677 E

F 15677 E

Data Storage – Database

PNUTS – a relational database service

Indexes and views



StockNumber INT,




Parallel database

Structured schema

Designed and used by Yahoo!

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-sourcevariant of MapReduce named Hadoop
  • After 2008, MapReduce/Hadoop become a key technology component in cloud computing
  • In 2010, the U.S. conferred the MapReducepatent to Google



… Hadoop or variants …



  • 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: 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

Re-thinking MapReduce

  • 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

MRlite: Lightweight Parallel Processing

  • 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 towards lightweight scalable and general parallel processing
MRlite:Towards Lightweight, Scalable, and General Parallel Processing
  • The MRlite master accepts jobs from clients and schedules them to execute on slaves


  • Distributed nodes accept tasks from master and execute them



MRlite master


MRlite client


  • 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

Data flow

Command flow


Computing Capability

Z. Ma and L. Gu. The Limitation of MapReduce: a Probing Case and a Lightweight Solution. CLOUD COMPUTING 2010

Using MRlite, the parallel compilation jobs, mrcc, is 10 times faster than that running on Hadoop!


Inside MapReduce-Style Computation

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


Master informed ofresult locations

Initial data split

into 64MB blocks

R reducers retrieve

Data from mappers

Computed, resultslocally stored

Final output written

Where is the communication-intensive part?


Inside MapReduce

  • 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 MapReduce

Packet reception on the master server


Inside MapReduce

Packet transmission on the master server

major components of a datacenter

Datacenter Networking

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
growth trends in datacenters
Growth Trends in Datacenters

Datacenter Networking

  • 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

Datacenter Networking

  • 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
Networking components (examples)

Datacenter Networking

64 10-GE port Upstream

768 1-GE port Downstream

  • 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
common data center topology


Layer-3 router



Layer-2/3 switch


Layer-2 switch


Datacenter Networking

Common data center topology

Data Center

data center network design goals

Datacenter Networking

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


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
dcell ideas


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
one approach switched network with a hypercube interconnect

Data Center Networking

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


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


Core switch: 10Gbps port x 10

How many servers can be connected in this system?

81920 servers with 1Gbps bandwidth

Leaf switch: 1Gbps port x 40 + 10Gbps port x 2.

shipping container as data center module

Data Center Network

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
unit of data center growth

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
multiple site redundancy and enhanced performance using load balancing
Multiple-Site Redundancy and Enhanced Performance using load balancing

Data Center Network

Global Data Center Deployment Problems


  • 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




  • 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

High-performance, reliable, cost-effective computing infrastructure

Cooling, air cleaning, and energy efficiency

Challenges and Research Problems

[Andersen] FAWN

[Barraso] Clusters

[Reghavendra] Power

[Fan] Power

System software

Operating systems



Execution engines and containers

Challenges and Research Problems

DeCandia: Dynamo

Cooper: PNUTS

Burrows: Chubby

Isard: Quincy

Yu: DryadLINQ

Ghemawat: GFS

Chang: Bigtable

Brantner: DB on S3

Dean: MapReduce


Interconnect and global network structuring

Traffic engineering

Challenges and Research Problems

Guo 2009: BCube

Guo 2008: DCell

Al-Fares: Commodity DC

Data and programming

Data consistency mechanisms (e.g., replications)

Fault tolerance

Interfaces and semantics

Software engineering

User interface

Application architecture

Challenges and Research Problems

Olston: Pig Latin

Pike: Sawzall

Buyya: IT services

[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.

[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.


[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.

[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.


[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.

[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.


[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.

[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.


[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.

[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.

[Yu] Y. Yu, M. Isard, D. Fetterly, M. Budiu, Ú. 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.



Thank you!