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Introduction. Dr. Ying Lu [email protected] Schorr Center 104. CSCE990 Advanced Distributed Systems Seminar. Some lecture notes are based on slides created by Dr. Zahorjan at Univ. of Washington, Dr. Konev at Univ. of Liverpool, Steve Crouch at Software Sustainability Institute,

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Dr. Ying Lu

[email protected]

Schorr Center 104

CSCE990 Advanced Distributed Systems Seminar

Some lecture notes are based on slides created by

Dr. Zahorjan at Univ. of Washington,

Dr. Konev at Univ. of Liverpool,

Steve Crouch at Software Sustainability Institute,

Petru Eles at Linköpings University, and

Dr. Majd F. Sakr, Mohammad Hammoud, Vinay Kolar at CMU

I have modified them and added new slides

Giving credit where credit is due:

Cloud grid utility computing
Cloud’ & ‘Grid’ – Utility Computing?

The Grid…

The Cloud…

j.o.h.n walker


Is it really like the grid?

Is it more like a fog?

But… they’re both about providing access to compute and data resources

The problem
The Problem

Basically, want to run compute/data intensive task

Don’t have enough resources to run job locally

At least, to return results within sensible timeframe

Would like to use another, more capable resource

Distributed computing in olden times
Distributed Computing in Olden Times

Small number of ‘fast’ computers

Very expensive


Used nearly all the time

Time allocations for users

Not updated often

Michael L. Umbricht and Carl R. Friend

  • Punched cards

    • Wait time huge

    • MailNet, SneakerNet, etc…

  • Mainframes

  • Cray-1 1976 - $8.8 million, 160 megaflops, 8MB memory

Univac 1710


Cray X-MP(Cray -1 successor)

It s about scaling up
It’s About Scaling Up…

Compute and data – you need more, you go somewhere else to get it

  • Then… the march towards localization of computation, the Personal Computer

  • Computational Science develops in laboratories

  • Is this changing again?

Images: nasaimages, Extra Ketchup, Google Maps, Dave Page

What is cloud computing
What is Cloud Computing?

Many ways to define it i.e. one for every supplier of ‘cloud’

Key characteristics:

On demand, dynamic allocation of resources – ‘elasticity’



Billed for what you use e.g. in terms of CPU, storage

Standardized interfaces e.g. OCCI

… it’s more like an electricity grid than the Grid

How does it deliver
How Does it Deliver?

Cloud computing can deliver at any of these levels

These levels are often blurred and routinely disputed!

Resources provided on demand


End user/Customer

Developer/ Service Provider

Iaas infrastructure as a service
IaaS – Infrastructure as a Service

You get access to (usually) virtualised hardware

Servers, storage, networking

Operating system

Responsible for managing OS, middleware, runtime, data, application (development)

e.g. Amazon EC2

Amazon ec2 the idea
Amazon EC2 – The Idea

‘Elastic Computing’

Sign up

Select & configure virtualized resources

Emulated OS: RHEL, OEL, Windows Server, OpenSolaris, Fedora, Ubuntu, Debian, SUSE, Gentoo, Amazon Linux AMI


Data: IBM DB2, IBM Informix, Microsoft SQL, MySQL, Oracle

Web Hosting: Apache HTTP, IIS/Asp.NET, IBM WebSphere

Batch Processing: Hadoop, Condor, Open MPI

Newer addition - development environments:

IBM sMash, Ruby on Rails, Jboss Enterprise Application Platform

Moving towards PaaS! (Already there?)

Additional web services

S3: Simple Storage Solution – transfer data in/out, 1 byte to 5 TB

SQS: Simple Queue Service

Amazon ec2 pricing
Amazon EC2: Pricing

Free (at the start!):

Run single Amazon Micro Instance for a year

750 hours of EC2, 750 hours of Elastic Load Balancing plus 15 GB data processing

15 GB bandwidth in/out across all services

On demand instances:

Pay per hour, no long-term commitment

From $0.025/hour -> $0.76/hour

Reserved instances:

Upfront payment, with discount per hour

From $227/year + $0.01/hour -> $1820/year + $0.32/hour

Spot instances:

Bid for unused EC2 capacity:

Spot Price fluctuates with supply/demand, if bid over Spot Price, you get it

From $0.007/hour -> $0.68/hour

Ec2 application example
EC2 Application Example

  • Peter Harkins, a Senior Engineer at The Washington Post, used 200 EC2 instances (1,407 server hours) to convert 17,481 pages of Hillary Clinton’s travel documents into a form more friendly to use on the WWW within nine hours after they were released*


Paas platform as a service
PaaS – Platform as a Service

You get integrated development environment

e.g. application design, testing, deployment, hosting, frameworks for database integration, storage, app versioning, etc.

Develop applications on top

Responsible for managing data, application (development)

e.g. Google App Engine

Google app engine the idea
Google App Engine: The Idea

Sign up via Google Accounts

Develop App Engine web applications locally using SDK – emulates all services

Includes tool to upload application code, static files and config files

Can ‘version’ your web application instances

Apps run in a Java/Python ‘sandbox’

Automatic scaling and load balancing – abstract across underlying resources

Google app engine pricing
Google App Engine: Pricing

Free within a quota:

500MB storage, 5 million page views a month (~6.5 CPU hours, 1GB)

10 applications/developer

Billed model:

Each app $8/user (max $1000) a month

For each app:

Saas software as a service
SaaS – Software as a Service

Top layer consumed directly by end user – the ‘business’ functionality

Application software provided, you configure it (more or less)

Various levels of maturity:

Level 1: each customer has own customised version of application in own instance

Level 2: all instances use same application code, but configured individually

Level 3: single instance of application across all customers

Level 4: multiple customers served on load-balanced ‘farm’ of identical instances

Levels 3 & 4: separate customer data!

e.g. Gmail, Google Sites, Google Docs, Facebook

Summary of provision
Summary of Provision

Application Migration – adopt the level you want

Cloud open standards
Cloud Open Standards

Implementations typically have proprietary standards and interfaces

Vendors like this – often locked in to one implementation

Community ‘push’ towards open cloud standards:

Open Grid Forum (OGF) – Open Cloud Computing Interface (OCCI)

Distributed Management Task Force (DMTF) – Open Virtualisation Format (OVF)

Definition of a distributed system
Definition of a Distributed System

A distributed system is:

A collection of independent computers that appear to its users as a single coherent system (Tanenbaum book)

One in which components located at networked computers communicate and coordinate their actions only by passing messages (Coulouris book)

Why distributed systems
Why Distributed Systems?




Diversity in Application Domains



Why distributed systems1
Why Distributed Systems?

Big data continues to grow:

In mid-2010, the information universe carried 1.2 zettabytes and 2020 predictions expect nearly 44 times more at 35 zettabytes coming our way.

Applications are becoming data-intensive.

Why distributed systems2
Why Distributed Systems?

Individual computers have limited resources compared to scale of current day problems & application domains:

Caches and Memory:

16KB- 64KB, 2-4 cycles

512KB- 8MB, 6-15 cycles

4MB- 32MB, 30-50 cycles

1GB- 4GB, 300+ cycles

Why distributed systems3
Why Distributed Systems?

Hard Disk Drive:

Limited capacity

Limited number of channels

Limited bandwidth

Why distributed systems4
Why Distributed Systems?

  • Processor:

  • The number of transistors that can be integrated on a single die has continued to grow at Moore’s pace.

  • Chip Multiprocessors (CMPs) are now available













L2 Cache

A single Processor Chip


Why distributed systems5
Why Distributed Systems?

  • Processor (cont’d):

  • Up until a few years ago, CPU speed grew at the rate of 55% annually, while the memory speed grew at the rate of only 7% [H & P].













L2 Cache




Processor-Memory speed gap


Why distributed systems6
Why Distributed Systems?

  • Even if 100s or 1000s of cores are placed on a CMP, it is a challenge to deliver input data to these cores fast enough for processing.

10000 seconds (or 3 hours) to load data










L2 Cache

A Data Set

of 4 TBs


4 100MB/S IO Channels

Why distributed systems7
Why Distributed Systems?

Only 3 minutes to load data












A Data Set (data)

of 4 TBs


  • But this requires:

    • A way to express the problem as parallel processes and execute them on different machines (Programming Models and Concurrency).

    • A way for processes on different machines to exchange information (Communication).

    • A way for processes to cooperate, synchronize with one another and agree on shared values (Synchronization).

    • A way to enhance reliability and improve performance (Consistency and Replication).


  • But this requires (Cont.):

    • A way to recover from partial failures (Fault Tolerance).

    • A way to secure communication and ensure that a process gets only those access rights it is entitled to (Security).

    • A way to extend interfaces so as to mimic the behavior of another system, reduce diversity of platforms, and provide a high degree of portability and flexibility (Virtualization)

Course objective
Course Objective

  • This is a course on advanced distributed systems, where we will understand the state of the art in distributed systems, in particular, data-intensive distributed computing systems, and how and why we got there and how to engage in systems research.