learning distributed programming techniquessun/pdfd/cs550-lec1.pdf · distributed operating system,...

• Advanced Operating Systems (Distributed Operating Systems)

• Instructor: Xian-He Sun– Email: sun@iit.edu, Phone: (312) 567-5260

– Office hours: 1:30pm-2:30pm Tuesday, Thursday at SB229C, or by appointment

• TA: TBA

X.Sun (IIT) CS550: Advanced OS Lecture 1 Page 1

• TA: TBA– Email: xxx@iit.edu

– Office: xxx

– Office hours: TBA

• Blackboard:– http://blackboard.iit.edu

• Class Web site– http://www.cs.iit.edu/~sun/cs550.html

Outline

• Course information

• Key issues of distributed operating systems

• Hardware concepts– Multiprocessors

– Multicomputers

– Distributed systems

• Software concepts– Uniprocessor OS

– Distributed OS

– Network OS

– Middleware

What This Course is About

• Understanding the fundamental concepts of

distributed operating system, and distributed

systems in general

• Learning distributed programming techniques

– Multithreading, RPC, RMI, Sockets, MPI, etc.

• Understanding the general principles of

distributed paradigms

– MPI, JINI, NFS, Web Service, Grid, etc.

Prerequisite

• CS450 “Operating Systems”

• Familiar with

– Programming in C/C++ or Java

– UNIX tools and development environment

• Command

• Editors (vi, emacs), compilers (gcc), makefiles (GNU make)

– Networking programming

• Sockets

• Multithreaded

• RPC, Java RMI

– Basic concepts of computer architecture

Course Materials

• Required:– “Distributed Systems: Principles and Paradigms

(2nd edition)” by Tannenbaum and Van Steen, Pearson/Prentice Hall 2007

• Recommended:– “Distributed Systems: Concepts and Design (4th

– “Distributed Systems: Concepts and Design (4th edition)” by George Coulouris, Jean Dollimore, and Tim Kindberg, Addison-Wesley, 2005

• Supplemental readings– “Virtual Machines: Versatile Platforms for Systems

and Processes” by Jim Smith and Ravi Nair, Morgan Kaufmann, 2005

Misc. Course Details

• You are expected to attend all of the lectures and presentations

• Grading– Written and programming assignments (35%): individual

work– One exam (35%)– Final project (30%): individual or group with 2-3 students

– Final project (30%): individual or group with 2-3 students

• Use the course blackboard– Announcements– Lecture notes– Assignments– Discussion– …

Policies

• Collaboration

– Encouraged for high level concepts and

understanding the courses materials

– but …..

• Cheating

– Copying all or part of another student's homework

– Allowing another student to copy all or part of your

homework

– Copying all or part of code found in a book,

magazine, the Internet, or other resource

Any Questions?

Personal Introduction

• Research interests

– Middleware

– High End Computing

– Performance Analysis and Modeling

• Research group:

– Scalable Computing Software Laboratory (SCS)

– http://www.cs.iit.edu/~scs/

– Weekly Research seminar

Distributed Computing at SCSMany workstations are made available for graduate students

Scalable Computing Software (SCS) Lab.

NU-EUIC

NCSA/UIUC

Star TapIIT

I-WIRE

Distributed

Optical Testbed

(Grid)

Parallel Computers at SCS OMNII-WIRE

Pervasive Computing

Environments at SCS

• A computer system is called scalable if it can scale up to accommodate ever-increasing performance and functionality demand

• A software is called scalable if it can maintain

Scalable Systems

• A software is called scalable if it can maintain its functionality and efficiency while the underlying computer system and problem scale up

Evolution of ComputingBigger becomes even bigger

Smaller becomes ever smaller, & connected

Japan’s Earth Simulator

•640 processor nodes (PNs)

•Each PN is a system with 8

vector-type arithmetic

X.Sun (IIT) CS546 Lecture 1 Page 13.

processors (APs)

•Peak performance 40Tflops

1.4m x1m x 2m

approx; 50m x 65m x 17m

� The fastest

supercomputer in

November 2007

� Scalable ultra-

computer targeted

for 106,496

compute nodes

Scalable ComputingThe way to high performance

X.Sun (IIT) CS546 Lecture 1 Page 14

BlueGene / L

compute nodes

� BlueGene/L is now

running with

212992 processors

and 478.2 TFlops

on LINPACK

� Peak performance:

596 TFlops

Multicore Add Another Dimension

• Cell

– 1 PPE and 8 SPEs

– Shared L2 cache

– EIB

IBM Multicore

• Power6 – Dual core

– 5 GHz

Multi-Core• Motivation for Multi-Core

– Exploits improved feature-size and density– Increases functional units per chip (spatial

efficiency)– Limits energy consumption per operation– Constrains growth in processor complexity

• Challenges resulting from multi-core– Aggravates memory wall

• Memory bandwidth– Way to get data out of memory banks

– Way to get data into multi-core processor array

• Memory latency

• Fragments L3 cache

– Relies on effective exploitation of multiple-thread parallelism

• Need for parallel computing model and parallel programming model

– Pins become strangle point• Rate of pin growth projected to slow and flatten

• Rate of bandwidth per pin (pair) projected to grow slowly

– Requires mechanisms for efficient inter-processor coordination

• Synchronization

• Mutual exclusion

• Context switching

Distributed Computing: What is the new

� Supercomputers become

ever powerful

� Communities of “Virtual

organizations” are

formed

� No VO possesses all

required skills and

resources

� From “community

sharing” to “information

grid”

Higher Quality of Service

IncreasedEfficiency

Integrated VOs: the Grid

Mimic the electrical power grid

Reduced Complexity

& Cost

Increased Productivity

Improved Resiliency

The Challenge of Grid Computing

Discovery

Many sources

of data, services,

computationR

Registries organize

services of interestAccessRM

Security & policy

must underlie access

& management

decisions

Virtualization and Resource Management

services of interest

to a community

Data integration activities

may require access to, &

exploration/analysis of, data

at many locations

Exploration & analysis

may involve complex,

multi-step workflows

Resource management

is needed to ensure

progress & arbitrate

competing demandsSecurity

serviceSecurity

servicePolicy

service

Higher Quality of Service

IncreasedEfficiency

Cloud ComputingMimic the electrical power grid

Reduced Complexity

& Cost

Increased Productivity

Improved Resiliency

What is Cloud Computing?

• A computing paradigm in which tasks are assigned to a combination of connections, software and services accessed over a network

• The network of servers and connections is collectively known as the cloud

• Other terms

• Other terms– Mesh Computing

– Elastic Cloud Computing

– Network Computing

2009-1-21 21

What are the diff. between Cloud & Grid?

A commercial version of Grid computing

More likely under single management (VO)

More likely provide computing resources than resource

sharing

More like to serve lots of modest size jobs

billions of dollars being spent by the likes of Amazon, Google, and Microsoft to create real commercial grids

The prospect of needing only a credit card to get on-demand access to 100,000+ computers in tens of data centers distributed throughout the world

2009-1-21 22

Cloud: Integrated Resource

Provide virtual computing environments on demand

Virtualization and Virtual Machine

• Virtual service and virtual machine: the key for data

center and Cloud computing

• Virtual machine (in distributed environment): A

hosting platform where each user can create and

operate in a private machine(s), based on

Grid/distribute infrastructure, achieving:

– Virtualization

– Isolation and Protection

– Privacy

– Accountability and QoS

– On-demand creation and provisioning

Distributed (Dynamic) Virtual Machine

DVM’

Virtual service node

DVM Host

(physical)

Virtualization: Key Technique

• Two-level OS structure– Host OS– Guest OS

• Strong isolation– Administration isolation– Installation isolation– Fault / attack Isolation

DVMS1 DVMSn

– Fault / attack Isolation– Recovery, migration, and

reconfiguration

• Virtual service node– DVM Service (DVMS)– Guest OS – Internetworking enabled

One DVM host

Host OS

…Guest OS Guest OS

Embedded Systems: What is the new

• Devices become

smaller and more

powerful

• Devices are

coordinated via network

• From “autonomous

computing” to

coordinated “human-

center computing”

Pervasive Computing

MIT’s view of pervasive computing

Evolution of Computing

Remote Comm.

Coordination

High Availability

Security & Fault

Distributed

Computing

Federated Communities

Virtualization

Standardization

Uneven Conditions Grid

Computing

Pervasive

Global

Smart Space

(Cloud)

Security & Fault

Tolerance

Mobility

Mobile Network

Adaptive & Reflective

Energy Aware system

Computing

Mobile

Computing

Smart Space

Invisibility

Localized Scalability

Context Awareness

Pervasive

Computing

Service Oriented Computing

Convergence of Core Technology Standards allows

Have beenconverging WSRF

Started far apart in

applications &

technology

Compliant

Technology

• Internet computing: Web service

Computing as a service

Convergence of Core Technology Standards allows

Common base for Business and Technology ServicesWeb service

• Grid computing: Grid service and is merging with WS

• Pervasive computing: Human centered service

• Mobile computing: Phone service

Future Computing: Human-centered Service

Devices

They are connected to form `smart

space’

Grids link `smart spaces’ to

A new IT booming is coming

Devices

become smaller

and powerful

A device is an entry of the cyber

`smart spaces’ to support `global

smartness’

The Third Wave of Computing Revolutions

• Network, communication, and interconnectivity

• Begin in the late 90s until now

• Machine/machine, software/software,

people/people

• Anytime, anywhere, WWW

• The communications landscape is shifting

• Promising but a continued work

How do we get there?

– Many Challenges ahead!

Resource Management & Task Scheduling

• DVM provider selection:

– Among a set of DVM providers, which one should be

chosen to host an DVM?

• DVMS selection:

– Among a set of potential tenants (DVMSes), which

ones to host? (for QoS, resource utilization, security…)

• The Grid Harvest Service (GHS) System

– A long-term application-level performance

prediction and task scheduling system for non-

dedicated distributed (Grid) environments

– Reservation-based versus shared resources

– Good, but more issues, such as QoS, FT, need to solve

10,000

100,000

ance Uni-rocessor

Multi-core/many-core processor

Processor-memory performance gap

• Processor performance

increases rapidly

– Uni-processor: ~52% until 2004, ~25% since then

– New trend: multi-core/many-core architecture

• Intel TeraFlops chip, 2007

Source: Intel

1980 1985 1990 1995 2000 2005 2010

Memory

• Intel TeraFlops chip, 2007

– Aggregate processor performance much higher

• Memory: ~9% per year

• Processor-memory speed

gap keeps increasing

Source: OCZ

The Memory-wall problem requires a rethinking of the design of OS

Gordon

Moore’s Law

“the number of transistors

that can be fabricated on a

single integrated circuit at

a reasonable cost doubles

every year…”

• How?– Material techniques such as extreme ultraviolet lithography (<100 nm)

• Corollary– Processor speed doubles at same rate

• Problem– Moore’s law almost hits its limitation

– Then , what is the impact? who and which technology will lead?

Any Questions?

learning distributed programming techniquessun/pdfd/cs550-lec1.pdf · distributed operating system,...

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