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CS525
Advanced Distributed Systems
Spring 2010
Indranil Gupta (Indy)
Lecture 1
January 19, 2010
1
All Slides © IG
What is a Distributed System?
(examples)
The Internet
A Sensor Network
Gnutella peer to peer system
Food Web of
Little Rock Lake, WI
2
Can you name some examples of
Operating Systems?
3
Can you name some examples of
Operating Systems?
…
Linux WinXP Vista Unix FreeBSD Mac OSX
2K Aegis Scout Hydra Mach SPIN
OS/2 Express Flux Hope Spring
AntaresOS EOS LOS SQOS LittleOS TINOS
PalmOS WinCE TinyOS
…
4
What is an Operating System?
5
What is an Operating System?
•
•
•
•
•
User interface to hardware (device driver)
Provides abstractions (processes, file system)
Resource manager (scheduler)
Means of communication (networking)
…
6
FOLDOC definition
•
•
•
•
The low-level software which handles the interface to peripheral hardware,
schedules tasks, allocates storage, and presents a default interface to the user
when no application program is running.
The OS may be split into a kernel which is always present and various system
programs which use facilities provided by the kernel to perform higher-level
house-keeping tasks, often acting as servers in a client-server relationship.
Some would include a graphical user interface and window system as part of
the OS, others would not. The operating system loader, BIOS, or other
firmware required at boot time or when installing the operating system would
generally not be considered part of the operating system, though this
distinction is unclear in the case of a roamable operating system such as RISC
OS.
The facilities an operating system provides and its general design philosophy
exert an extremely strong influence on programming style and on the technical
cultures that grow up around the machines on which it runs.
7
Can you name some examples of
Distributed Systems?
8
Can you name some examples of
Distributed Systems?
•
•
•
•
•
•
•
•
Client-server (e.g., NFS)
The Internet
The Web
An ad-hoc network
A sensor network
DNS
BitTorrent (peer to peer overlays)
Datacenters
9
What is a Distributed System?
10
FOLDOC definition
A collection of (probably heterogeneous) automata whose distribution
is transparent to the user so that the system appears as one local
machine. This is in contrast to a network, where the user is aware that
there are several machines, and their location, storage replication, load
balancing and functionality is not transparent. Distributed systems
usually use some kind of client-server organization.
11
Textbook definitions
• A distributed system is a collection of independent
computers that appear to the users of the system as
a single computer
[Andrew Tanenbaum]
• A distributed system is several computers doing
something together. Thus, a distributed system has
three primary characteristics: multiple computers,
interconnections, and shared state
[Michael Schroeder]
12
Unsatisfactory
• Why are these definitions short?
• Why do these definitions look inadequate to us?
• Because we are interested in the insides of a
distributed system
–
–
–
–
algorithmics
design and implementation
maintenance
study
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I shall not today attempt further to define the kinds of
material I understand to be embraced within that shorthand
description; and perhaps I could never succeed in
intelligibly doing so. But I know it when I see it…
[Potter Stewart, Associate Justice, US Supreme Court
(talking about his interpretation of a technical term laid
down in the law, case Jacobellis versus Ohio 1964) ]
14
A working definition for us
A distributed system is a collection of entities, each
of which is autonomous, programmable,
asynchronous and failure-prone, and which
communicate through an unreliable communication
medium.
• Our interest in distributed systems involves
– algorithmics, design and implementation, maintenance,
study
• Entity=a process on a device (PC, PDA, mote)
• Communication Medium=Wired or wireless network
15
A range of interesting problems
for Distributed System designers
•
•
•
•
•
•
•
•
•
•
Routing and Multicast [IP multicast, SRM, RMTP]
Post and retrieve [Usenet]
Search [BitTorrent, Google]
Programming [MapReduce, Pig, Dryad]
Storage [Databases, HDFS]
Coordination and Scheduling [EC2, SETI@Home]
Infrastructures [EC2, S3, AppEngine, CCT, OpenCirrus]
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A range of challenges
•
• Failures: no longer the exception, but rather
a norm
• Scalability: 1000s of machines, Terabytes
of data
• Asynchrony: clock skew and clock drift
• Security: of data, users, computations, etc.
•
17
Multicast
18
Multicast
Node with a piece of information
to be communicated to everyone
Distributed
Group of
“Nodes”=
Processes
at Internetbased hosts
19
Fault-tolerance and Scalability
Multicast sender
X
X
Multicast Protocol



Nodes may crash
Packets may
be dropped
1000’s of nodes
20
Centralized


Simplest
implementation
Problems?
UDP/TCP packets
21
Tree-Based


UDP/TCP packets

e.g., IPmulticast, SRM
RMTP, TRAM,TMTP
Tree setup
and maintenance
Problems?
22
A Third Approach
Multicast sender
23
Periodically, transmit to
b random targets
Gossip messages (UDP)
24
Other nodes do same
after receiving multicast
Gossip messages (UDP)
25
26
“Epidemic” Multicast (or “Gossip”)
Infected
Protocol rounds (local clock)
b random targets per round
Gossip Message (UDP)
Uninfected
27
Properties
Claim that this simple protocol
• Is lightweight in large groups
• Spreads a multicast quickly
• Is highly fault-tolerant
28
Analysis
From old mathematical branch of Epidemiology
[Bailey 75]
• Population of (n+1) individuals mixing
homogeneously
• Contact rate between any individual pair is 
• At any time, each individual is either uninfected
(numbering x) or infected (numbering y)
• Then, x0  n, y0  1
and at all times x  y  n  1
• Infected–uninfected contact turns latter infected,
and it stays infected
29
Analysis (contd.)
• Continuous time process
• Then
dx
   xy
dt
(why?)
with solution
n(n  1)
(n  1)
x
,y
 ( n 1) t
ne
1  ne   ( n 1)t
(correct? can you derive it?)
30
Epidemic Multicast
Infected
Protocol rounds (local clock)
b random targets per round
Gossip Message (UDP)
Uninfected
31
Epidemic Multicast Analysis
b

n
(why?)
Substituting, at time t=clog(n), num. infected is
y  (n  1) 
1
n
cb  2
(correct? can you derive it?)
32
Analysis (contd.)
• Set c,b to be small numbers independent of n
• Within clog(n) rounds, [low latency]
1
– all but
n
cb  2
of nodes receive the multicast
[reliability]
– each node has transmitted no more than cblog(n)
gossip messages [lightweight]
33
Fault-tolerance
• Packet loss
– 50% packet loss: analyze with b replaced with
b/2
– To achieve same reliability as 0% packet loss,
takes twice as many rounds
• Node failure
– 50% of nodes fail: analyze with n replaced with
n/2 and b replaced with b/2
– Same as above
34
Fault-tolerance
• With failures, is it possible that the epidemic might
die out quickly?
• Possible, but improbable:
– Once a few nodes are infected, with high probability, the
epidemic will not die out
– So the analysis we saw in the previous slides is actually
behavior with high probability
[Galey and Dani 98]
• Think: why do rumors spread so fast? why do
infectious diseases cascade quickly into epidemics?
why does a worm like Code Red spread rapidly?
35
So,…
• Is this all theory and a bunch of equations?
• Or are there implementations yet?
36
Some implementations
• Clearinghouse and Bayou projects: email
and database transactions [PODC ‘87]
• refDBMS system [Usenix ‘94]
• Bimodal Multicast [ACM TOCS ‘99]
• Sensor networks [Li Li et al, Infocom ’02,
and PBBF, ICDCS ‘05]
• Usenet NNTP (Network News Transport
Protocol) ! [‘79]
• AWS EC2 and S3 Cloud (rumored). [’00s] 37
NNTP Inter-server Protocol
1. Each client uploads and downloads news posts
from a news server
2.
Server retains news posts for a while,
transmits them lazily, deletes them after a while
38
We’ll cover some of these other
implementations during the course
• But let’s dwell on the big picture of the
course
39
Angles of Distributed Systems
Infrastructured D.S.’s
e.g., Internet-based
Distributed System (D.S.) Theory
Non-infrastructured D.S.’s
e.g., ad-hoc network based
40
CS 525 and Distributed Systems
Peer to peer systems
Cloud Computing
D.S. Theory
Sensor Networks
41
CS 525 and Distributed Systems
…DHTs, overlays,
Causality, snapshots, consensus,…
multicast, design
methodologies, …
…MapReduce,
EC2, …
…Smart Dust, TinyOS,
Aggregation,
42
In-network processing…
Interesting: Area Overlaps
Epidemics
NNTP
Gossip-based ad-hoc routing
43
Interesting: Area Overlaps
Do projects and write papers in these overlap areas!
The Internet
A Sensor Network
Gnutella peer to peer system
Clouds
44
Let’s Look at the Course
Information Sheet…
• No exams
• Paper Reading
– Presentations (groups of 2)
– Reviews
– See instructions on website for presentations and reviews
• Project
– Conference-quality paper (groups of 2)
– Novel idea solving useful problem, backed up with good
evaluation
– CS525 only class to give students access to multiple testbeds:
PlanetLab, Emulab, and (tentatively) Amazon Web Services Cloud
• Class Participation a must (and fun!)
• TA: Brian Cho
• My office hours: right after lecture/class (3112 SC)
45
Things for you to do today
• Look at the course website
• Follow “Schedule / Papers and Presentations link”
and read instructions
– http://www.cs.uiuc.edu/class/sp10/cs525/
– Need to sign up for a presentation slot by Jan 31
• Take a look at conference papers arising out of
previous versions of this course (CS598IG/CS525)
– Fall 03: 9/12 project papers in conferences and journals
– Fall 04, Spring 06, Spring 07, Spring 08, Spring 09:
Many under review in conferences and workshops,
similar success rates expected
46
Next Lecture
• Cloud Computing
– Take a look at all papers on website for that
session
– Read at least one of those papers completely
– Try to read all of them completely
– (no reviews required yet)
47
Backup Slides
48
Epidemic Multicast Analysis
b

n
(why?)
Substituting, at time t=clog(n)
n 1
y
1  ne
b
 ( n 1) c log(n )
n
n 1

1
1  cb 1
n
1
 (n  1)(1  cb 1 )
n
1
 ( n  1)  cb  2
n
49