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CS 525 Advanced Distributed Systems Spring 2010. Yeah! That’s what I’d like to know. Indranil Gupta (Indy) Lecture 2 What(’s in) the Cloud? January 21, 2010. 1. All Slides © IG. Clouds are Water Vapor. Oracle has a Cloud Computing Center. And yet…

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cs 525 advanced distributed systems spring 2010

CS 525 Advanced Distributed SystemsSpring 2010

Yeah! That’s what I’d like to know.

Indranil Gupta (Indy)

Lecture 2

What(’s in) the Cloud?

January 21, 2010

1

All Slides © IG

clouds are water vapor
Clouds are Water Vapor

Oracle has a Cloud Computing Center.

And yet…

Larry Ellison’s Rant on Cloud Computing

2

the hype
The Hype!

Gartner - Cloud computing revenue will soar faster than expected and will exceed $150 billion within five years.

Forrester - Cloud-Based Email Is Often Cheaper Than On-Premise Email

Vivek Kundra, CTO of Obama Government: “Growing adoption of cloud computing could improve data sharing and promote collaboration among federal, state and local governments.” E.g: fedbizopps.gov

Merrill Lynch: “By 2011 the volume of cloud computing market opportunity would amount to $160bn, including $95bn in business and productivity apps (email, office, CRM, etc.) and $65bn in online advertising.”

IDC: “Spending on IT cloud services will triple in the next 5 years, reaching $42 billion and capturing 25% of IT spending growth in 2012.”

3

Sources: http://www.infosysblogs.com/cloudcomputing/2009/08/the_cloud_computing_quotes.htm and http://www.mytestbox.com

slide5
Ingo Elfering, Vice President of Information Technology Strategy, GlaxoSmithKline:“With Online Services, we are able to reduce our IT operational costs by roughly 30% of what we’re spending now and introduce a variable cost subscription model for these technologies that allows us to more rapidly scale or divest our investment as necessary as we undergo a transformational change in the pharmaceutical industry”

Jim Swartz, CIO, Sybase: “At Sybase, a private cloud of virtual servers inside its data centre has saved nearly $US2 million annually since 2006, Swartz says, because the company can share computing power and storage resources across servers.”

Dave Power, Associate Information Consultant at Eli Lilly and Company: “With AWS, Powers said, a new server can be up and running in three minutes (it used to take Eli Lilly seven and a half weeks to deploy a server internally) and a 64-node Linux cluster can be online in five minutes (compared with three months internally). The deployment time is really what impressed us. It's just shy of instantaneous."

$$$

Sources: http://www.infosysblogs.com/cloudcomputing/2009/08/the_cloud_computing_quotes.htm and http://www.mytestbox.com

what is a cloud
What is a Cloud?
  • It’s a cluster! It’s a supercomputer! It’s a datastore!
  • It’s superman!
  • None of the above
  • All of the above
  • Cloud = Lots of storage + compute cycles nearby
what is a cloud8
What is a Cloud?
  • A single-site cloud (aka “Datacenter”) consists of
    • Compute nodes (split into racks)
    • Switches, connecting the racks
    • A network topology, e.g., hierarchical
    • Storage (backend) nodes connected to the network
    • Front-end for submitting jobs
    • Services: physical resource set, software services
  • A geographically distributed cloud consists of
    • Multiple such sites
    • Each site perhaps with a different structure and services
a sample cloud topology
A Sample Cloud Topology

Core Switch

Top of the Rack Switch

Rack

Servers

scale of industry datacenters
Scale of Industry Datacenters
  • Microsoft [NYTimes, 2008]
    • 150,000 machines
    • Growth rate of 10,000 per month
    • Largest datacenter: 48,000 machines
    • 80,000 total running Bing
  • Yahoo! [Hadoop Summit, 2009]
    • 25,000 machines
    • Split into clusters of 4000
  • AWS EC2 (Oct 2009)
    • 40,000 machines
    • 8 cores/machine
  • Google
    • (Rumored) several hundreds of thousands of machines
slide11

OK, they are massive. But it is still called a “cluster”! And that’s not a new concept!

slide12

“A Cloudy History of Time”© IG 2010

The first datacenters!

1940

1950

Timesharing Companies & Data Processing Industry

1960

Clusters

1970

Grids

1980

1990

PCs

(not distributed!)

2000

Peer to peer

systems

2010

Clouds and datacenters

slide13

“A Cloudy History of Time”© IG 2010

First large datacenters: ENIAC, ORDVAC, ILLIAC

Many used vacuum tubes and mechanical relays

Berkeley NOW Project

Supercomputers

Server Farms (e.g., Oceano)

  • P2P Systems (90s-00s)
  • Many Millions of users
  • Many GB per day

Data Processing Industry

- 1968: $70 M. 1978: $3.15 Billion.

  • Timesharing Industry (1975):
  • Market Share: Honeywell 34%, IBM 15%,
  • Xerox 10%, CDC 10%, DEC 10%, UNIVAC 10%
  • Honeywell 6000 & 635, IBM 370/168,
  • Xerox 940 & Sigma 9, DEC PDP-10, UNIVAC 1108

Clouds

  • Grids (1980s-2000s):
  • GriPhyN (1970s-80s)
  • Open Science Grid and Lambda Rail (2000s)
  • Globus & other standards (1990s-2000s)
trends technology
Trends: Technology
  • Doubling Periods – storage: 12 mos, bandwidth: 9 mos, and (what law is this?) cpu speed: 18 mos
  • Then and Now

Bandwidth

    • 1985: mostly 56Kbps links nationwide
    • 2004: 155 Mbps links widespread

Disk capacity

    • Today’s PCs have 100GBs, same as a 1990 supercomputer
trends users
Trends: Users
  • Then and Now

Biologists:

    • 1990: were running small single-molecule simulations
    • 2004: want to calculate structures of complex macromolecules, want to screen thousands of drug candidates, sequence very complex genomes

Physicists

    • 2008 onwards: CERN’s Large Hadron Collider will produce 700 MB/s or 15 PB/year
  • Trends in Technology and User Requirements: Independent or Symbiotic?
prophecies
Prophecies

In 1965, MIT's Fernando Corbató and the other designers of the Multics operating system envisioned a computer facility operating “like a power company or water company”.

Plug your thin client into the computing Utility

and Play your favorite Intensive Compute &

Communicate Application

  • [Have today’s clouds brought us closer to this reality?]
slide18

So, clouds have been around for decades! But aside from massive scale what’s new about today’s cloud computing?!

what s new in today s clouds
What(’s new) in Today’s Clouds?

Three major features:

  • On-demand access: Pay-as-you-go, no upfront commitment.
    • Anyone can access it (e.g., Washington Post – Hillary Clinton example)
  • Data-intensive Nature: What was MBs has now become TBs.
    • Daily logs, forensics, Web data, etc.
    • Do you know the size of Wikipedia dump?
  • New Cloud Programming Paradigms: MapReduce/Hadoop, Pig Latin, DryadLinq, Swift, and many others.
    • High in accessibility and ease of programmability

Combination of one or more of these gives rise to novel and unsolved distributed computing problems in cloud computing.

i on demand access aas classification
I. On-demand access: *aaS Classification

On-demand: renting a cab vs (previously) renting a car, or buying one. E.g.:

    • AWS Elastic Compute Cloud (EC2): $0.086-$1.16 per CPU hour
    • AWS Simple Storage Service (S3): $0.055-$0.15 per GB-month
  • HaaS: Hardware as a Service
    • You get access to barebones hardware machines, do whatever you want with them
    • Ex: Your own cluster, Emulab
  • IaaS: Infrastructure as a Service
    • You get access to flexible computing and storage infrastructure. Virtualization is one way of achieving this. Often said to subsume HaaS.
    • Ex: Amazon Web Services (AWS: EC2 and S3), Eucalyptus, Rightscale.
  • PaaS: Platform as a Service
    • You get access to flexible computing and storage infrastructure, coupled with a software platform (often tightly)
    • Ex: Google’s AppEngine
  • SaaS: Software as a Service
    • You get access to software services, when you need them. Often said to subsume SOA (Service Oriented Architectures).
    • Ex: Microsoft’s LiveMesh, MS Office on demand
ii data intensive computing
II. Data-intensive Computing
  • Computation-Intensive Computing
    • Example areas: MPI-based, High-performance computing, Grids
    • Typically run on supercomputers (e.g., NCSA Blue Waters)
  • Data-Intensive
    • Typically store data at datacenters
    • Use compute nodes nearby
    • Compute nodes run computation services
  • In data-intensive computing, the focus shifts from computation to the data: CPU utilization no longer the most important resource metric
  • Problem areas include
    • Distributed systems
    • Middleware
    • OS
    • Storage
    • Networking
    • Security
    • Others
iii new cloud programming paradigms
III. New Cloud Programming Paradigms

Dataflow programming frameworks

  • Google: MapReduce and Sawzall
  • Yahoo: Hadoop and Pig Latin
  • Microsoft: DryadLINQ
  • Facebook: Hive
  • Amazon: Elastic MapReduce service (pay-as-you-go)
  • Google (MapReduce)
    • Indexing: a chain of 24 MapReduce jobs
    • ~200K jobs processing 50PB/month (in 2006)
  • Yahoo! (Hadoop + Pig)
    • WebMap: a chain of 100 MapReduce jobs
    • 280 TB of data, 2500 nodes, 73 hours
  • Facebook (Hadoop + Hive)
    • ~300TB total, adding 2TB/day (in 2008)
    • 3K jobs processing 55TB/day
  • Similar numbers from other companies, e.g., Yieldex, eharmony.com, etc.
two categories of clouds
Two Categories of Clouds
  • Industrial Clouds
    • Can be either a (i) public cloud, or (ii) private cloud
    • Private clouds are accessible only to company employees
    • Public clouds provide service to any paying customer:
      • Amazon S3 (Simple Storage Service): store arbitrary datasets ,pay per GB-onth stored
      • Amazon EC2 (Elastic Compute Cloud): upload and run arbitrary images, pay per CPU hour used
      • Google AppEngine: develop applications within their appengine framework, upload data that will be imported into their format, and run
  • Academic Clouds
    • Allow researchers to innovate, deploy, and experiment
    • Google-IBM Cloud (U. Washington): run apps programmed atop Hadoop
    • Cloud Computing Testbed (CCT @ UIUC): first cloud testbed to support systems research. Runs: (i) apps programmed atop Hadoop and Pig, (ii) systems-level research on this first generation of cloud computing models (~HaaS), and (iii) Eucalyptus services (~AWS EC2). http://cloud.cs.illinois.edu
    • OpenCirrus: first federated cloud testbed. http://opencirrus.org
academic clouds
Academic Clouds
  • CCT = Cloud Computing Testbed
    • NSF infrastructure
    • Used by 10+ NSF projects, including several non-UIUC projects
    • Housed within Siebel Center (4th floor!)
    • Accessible to students of CS525!
      • Almost half of SP09 course used CCT for their projects
  • OpenCirrus = Federated Cloud Testbed
    • Contains CCT and other sites
  • If you need a CCT account for your CS525 experiment, let me know asap! There are a limited number of these available for CS525
cct hardware in more detail
CCT Hardware in more Detail
  • 128 compute nodes = 64+64
  • 500 TB & 1000+ shared cores
slide28

Goal of CCT: Support both Systems Research and Applications Researchin Data-intensive Distributed Computing

cct software services
Accessing and Using CCT:

Systems Partition (64-8 nodes):

CentOS machines

Dedicated access to a subset of machines (~ Emulab), with sudo access

User accounts

User requests # machines (<= 64) + storage quota (<= 30 TB)

Machine allocation survives for 4 weeks, storage survives for 6 months (both extendible)

Hadoop/Pig Partition and Service (64 nodes)

Eucalyptus Partition (8 nodes)

CCT Software Services
slide30
Accessing and Using CCT:

Systems Partition (64-8 nodes)

Hadoop/Pig Partition and Service (64 nodes):

Looks like a regular shared Hadoop cluster service

Users share 64 nodes. Individual nodes not directly reachable.

4 slots per machine

Several users report stable operation at 256 instances

During Spring 09, 10+ projects running simultaneously

User accounts

User requests account + storage quota (<= 30 TB)

Storage survives for 6 months (extendible)

Eucalyptus Partition (8 nodes)

CCT Software Services

slide31
Accessing and Using CCT:

Systems Partition (64-8 nodes)

Hadoop/Pig Partition and Service (64 nodes):

Eucalyptus Partition (8 nodes):

Based on open-source version of Eucalyptus from UCSB (Rich Wolski)

Exports same interface as AWS EC2 and S3.

CCT Software Services

slide32
Some Services running inside CCT

ZFS: backend file system.

Zenoss: Systems Monitoring. Shared with department’s other computing clusters

Hadoop + HDFS

Ability to make datasets publicly available

How do users request an account: two-stage process (go to http://cloud.cs.illinois.edu )

User account request – require background check

Allocation request

CCT Software Services

slide33

Open Cirrus Federation

Founding 6 sites

slide34

Open Cirrus Federation

First open federated cloud testbed

Shared: research, applications, infrastructure (9*1,000 cores), data sets

Global services: sign on, monitoring, store, etc., Federated clouds, meaning each is different

RAS

KIT (de)

Intel

HP

ETRI

Yahoo

UIUC

CMU

IDA (sg)

MIMOS

34

10 March 2014

Grown to 9 sites, with more to come

slide35

OK, so that’s what a cloud looks like today. Now, suppose I want to start my own company, Devils Inc. Should I buy a cloud and own it, or should I outsource to a public cloud?

next week
Next Week
  • We will continue discussion of cloud computing
    • How MapReduce works
    • What is PlanetLab and Emulab
    • What is Grid computing
  • Then we will start to discuss Basics of P2P systems
  • Please read at least one paper from each session
administrative announcements
Administrative Announcements

Student-led paper presentations (see instructions on website)

Start from February 11th

Groups of up to 2 students present each class, responsible for a set of 3 “Main Papers” on a topic

45 minute presentations (total) followed by discussion

Set up appointment with me to show slides by 5 pm day prior to presentation

Select your topic by Jan 31st

List of papers is up on the website

Each of the other students (non-presenters) expected to read the papers before class and turn in a one to two page review of the any two of the main set of papers (summary, comments, criticisms and possible future directions)

Email review and bring in hardcopy before class

37

announcements contd
Announcements (contd.)

Projects

Groups of 2 (need not be same as presentation groups)

We’ll start detailed discussions “soon” (a few classes into the student-led presentations)

38