2010.04.30 summary of cloud futures 2010 marco parenzan pov

Cloud Futures 2010
http://research.microsoft.com/en-
us/events/cloudfutures2010/

Marco Parenzan
MOSE

MOSE – University of Trieste 30 April, 2010 - slide 1

Marco Parenzan
36Più di 30 anni; sposato e due figli; abito a Fiume Veneto
(PN)
Un ―prodotto‖ di questa Università
Passato di sviluppo software in aziende...
 ...di software (visione esterna)...
 ...di produzione (visione interna)....
 ―Insourcing― (il contrario dell‘Outsourcing)
Sono un libero professionista
 Sviluppo applicazioni su commessa
 Consulenza
Collaboro con il Laboratorio MOSE dell‘Università degli Studi
di Trieste
 Mi occupo di metodologie e strumenti di sviluppo
 Mi occupo di progetti software 2


Attività di formazione
9 anni come Docente a Contratto in questa Università
 4 alla Triennale di Ing.Inf (Programmazione dei Calcolatori – ex. Prof.
Cesari)
 5 alla Magistrale di Ing.Inf (Programmazione dei Web Services)
Formazione in azienda
Formazione in enti regionali
 IAL/Centro Formazione Pordenone (Villaggio del Fanciullo)
 2 IFTS (Istruzione e formazione tecnica superiore, 1200 ore – Tecnico
Software): intero curriculum Microsoft
Speaker per user groups
 xe.net (http://www.xe.net/)
 UGI.ALT.net (http://www.ugialt.net/)
 1nn0va (http://www.1nn0va.net/)
 Eventi presso il Consorzio Universitario di Pordenone
3


1nn0va
L'associazione non ha scopo di lucro, è apartitica, apolitica e
ha finalità esclusivamente scientifica.
Si prefigge la diffusione delle tecnologie emergenti e attuali
attraverso l'organizzazione di conferenze, la redazione a la
diffusione di pubblicazioni, l'applicazione pratica in progetti
non a fini di lucro di innovative tecniche e metodologie di
sviluppo informatico, il coordinamento con altre Associazioni,
gruppi o Enti.
Divulgazione sul territorio (pordenonese)


Primo 1nn0valab: 28 maggio 2010
ECCEZIONALE EVENTO WPF DI 1NN0VA: NASCE 1nn0vaLAB.
Niente slide o lunghi monologhi, potrete ''toccare con mano'', digitare
direttamente il codice mostrato da Marco su un PC messo
eccezzionalmente a vostra completa disposizione per questo evento.

Dalla versione 3.0 del framework .NET, rilasciata nel novembre del 2006, abbiamo a disposizione una nuova
libreria per sviluppare applicazioni desktop: Windows Presentation Foundation. A quattro anni dal rilascio, e in
concomitanza del rilascio del framework .NET 4.0, è ora di fare il salto. Capiremo:
· quali sono i nuovi presupposti e quindi le differenze dal vecchio modello GDI delle Windows Forms
· il nuovo sistema di layout
· la nuova relazione tra designer e developer con l‘approccio dichiarativo e il linguaggio XAML
· l‘uso di pattern architetturali nello sviluppo di applicazioni desktop con il Model-View-View Model (M-V-
VM)
· perchè è ora di passare a WPF, visto che abbiamo anche Silverlight per lo sviluppo di Rich Internet
Applications (ora alla versione 4) e (supernovità) lo sviluppo di applicazioni per il prossimo Windows Phone 7

L'evento si svolgerà al Consorzio Universitario di Pordenone sito in via
Prasecco 3a, sala L2 (piano interrato), edificio B


Call for abstract


Call for Abstracts
“Advancing Research with Cloud Computing”

Cloud computing is fast becoming the most important platform for
research. Researchers today need vast computing resources to collect, share,
manipulate, and explore massive data sets as well as to build and deploy new
services for research. Cloud computing has the potential to advance research
discoveries by making data and computing resources readily available
at unprecedented economy of scale and nearly infinite scalability. To realize the full
promise of cloud computing for research, however, one must think about the cloud
as a holistic platform for creating new services, new experiences, and new methods
to pursue research, teaching and scholarly communication. This goal presents a
broad range of interesting questions.
We invited extended abstracts that illustrate the role of cloud computing across a
variety of research and curriculum development areas—including computer science,
earth sciences, healthcare, humanities, life sciences, and social sciences—that
highlight how new techniques and methods of research in the cloud may solve
distinct challenges arising in those diverse areas.
Source: http://research.microsoft.com/en-us/events/cloudfutures2010/


Microsoft Research


The University of Washington
eScience Institute
Ed Lazowska
Bill & Melinda Gates Chair in
Computer Science & Engineering
University of Washington

Director
University of Washington
eScience Institute

http://lazowska.cs.washington.edu/cloud2010.pdf


Dan Reed


Massive volumes of data from
sensors and networks of sensors


SDSS
Apache Point telescope,
80TB of raw image data
(80,000,000,000,000 bytes)
over a 7 year period


Large Hadron Collider
700MB of data
per second,
60TB/day, 20PB/year


Illumina Major labs
HiSeq 2000 have 25-100
Sequencer of these
machines
~1TB/day

Regional Scale
Nodes of the NSF
Ocean Observatories
Initiative

1000 km of fiber
optic cable on the
seafloor,thousands of
connecting
chemical, physical,
and biological
sensors


The Web

20+ billion web pages
x 20KB = 400+TB

One computer can
read 30-35 MB/sec
just to read the web
from disk => 4 months


eScience: Sensor-driven (data-driven)
science and engineering

Jim Gray

Transforming science (again!)


http://research.microsoft.com/en-us/collaboration/fourthparadigm/


eScience is about the analysis of data

The automated or semi-automated extraction of
knowledge from massive volumes of data

It’s not just a matter of volume


Large Synoptic Survey
Telescope (LSST)
40TB/day
(an SDSS every two days),
100+PB in its 10-year
lifetime

400mbps sustained data
rate between
Chile and NCSA


LSST Data Management System is widely distributed

Headquarters Site Archive Center

Systems Operations Archive Site
Co-located
Center (SOC) Data Access Center (DAC)

Education and Public • Site
Outreach Center (EPOC) • A physical
location/space
that hosts DM
centers
Connected via
•
Base Site
dedicated,
Base Center • Center
protected fiber
• optic circuits
A DM functional
Co-located capability hosted
Data Access Center (DAC) at a Site

NSF Review
December 15-17, 2009 NSF Review
Tucson, AZ
[Andy Connolly, University
December 15-17, 2009 of Washington, and LSST]
Tucson, AZ


But astronomy is substantially ahead of
most other fields
Data management in computational astrophysics
fopen()

fread()
fwrite()

fclose()

scp – Jeff Gardner, UW eScience Institute

Each simulation generates a sequence of snapshots;
each snapshot is a single flat file; analysis is via C or
Fortran programs


Data management in biology

90% of all business data is maintained in spreadsheets
– Enrique Godreau, Voyager Capital


Top faculty across all disciplines understand
and fear the coming data tsunami
Survey of 125 top
investigators
“Data, data, data”
Flat files and Excel are
the most common data
management tools …
Great for Microsoft
lousy for science!
Typical science workflow:
2 years ago: 1/2 day/week
Now: 1 FTE
In 2 years: 10 FTE
Need tools, tools, tools!


eScience is married to the Cloud: Scalable
computing and storage for everyone


Economics of Cloud Users
• Pay by use instead of provisioning for peak

Capacity
Resources

Resources
Demand Capacity

Demand
Time Time

Static data center Data center in the cloud

Unused resources
26


Cloud Computing: Confusion

The interesting thing about cloud computing is that we’ve redefined
Cloud Computing to include everything that we already do… I don’t understand
what we would do differently in the light of Cloud Computing than change
some of the words in our ads.

Larry Ellison (Oracle CEO) , quoted in the Wall Street Journal, Sept 26, 2008



A lot of people are jumping on the [cloud] bandwagon, but I have not heard
two people say the same thing about it. There are multiple definitions
out there of “the cloud”

Andy Isherwood (HP VP of European Software Sales), in ZDNews, Dec 11, 2008



It’s stupidity. It’s worse than stupidity: it’s a marketing hype campaign.
Somebody is saying this is inevitable – and whenever you hear somebody saying
that, it’s very likely to be a set of businesses campaigning to make it true.

Richard Stallman (“free software” advocate), in The Guardian, Sept 29, 2008


Dilbert on Cloud Computing

Novembre 18th, 2009
http://www.dilbert.com/strips/comic/2009-11-18/

Novembre 19th, 2009
http://www.dilbert.com/strips/comic/2009-11-19/


Il Cloud...questo sconosciuto?


http://www.youtube.com/watch?v=PPnoKb9fTkA&feature=player_embedded


Cloud Computing
A large-scale distributed computing paradigm that is driven
by economies of scale, in which a pool of abstracted,
virtualized, dynamically-scalable, managed computing power,
storage, platforms, and services are delivered on demand to
external customers over the Internet.
[I. Foster, Y. Zhao, I. Raicu, S. Lu,‖Cloud Computing and Grid
Computing 360-Degree Compared‖, in Proc. IEEE Grid
Computing Environments Workshop, Austin (Tx), Nov. 2008,
pp. 1-10.]


What Is Cloud Computing?
Three New Aspects to Cloud Computing
The Illusion of Infinite Computing Resources Available on Demand

The Elimination of an Upfront Commitment by Cloud Users

The Ability to Pay for Use of Computing Resources
on a Short-Term Basis as Needed

Above the Clouds: a Berkeley View of Cloud Computing
http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-28.pdfCloud Computing
Book Report” on the UC Berkeley Paper “Above the Clouds: a Berkeley View
of Cloud Computing”
http://blogs.msdn.com/pathelland/archive/2009/04/10/book-report-on-the-uc-berkeley-paper-
above-the-clouds-a-berkeley-view-of-cloud-computing.aspx
http://cid-84f3c5ef51d06e8b.skydrive.live.com/self.aspx/.Public/2009/Above-the-Clouds-
090401k.pptx

Evolution to Cloud Computing
(from another presentation)

Application runs Application runs at a Application runs using
on-premises hoster cloud platform

• Bring my own • Rent machines,
connectivity, • Shared,
machines, Pay someone for a pool of
connectivity, software
Pay someone to host my
multi-tenant
computing resources that can
software, etc. • application using hardware
Less control, but environment set of
be applied to a
that I specify applications
Buy my own hardware, and
•manage my owncontrol
Complete data center fewer • Offers pool of
and responsibility responsibilities computing
• Lower capital costs, resources,
• Upfront capital abstracted from
costs for the but pay for fixed
capacity, even if idle infrastructure
infrastructure
• Pay as you go

New Application Opportunities
Some Interesting New Types of Applications Enable By the Cloud:

Mobile Interactive Apps: Applications that respond in real time but work with lots of data. Cloud computing offers highly-available
large datasets.

Parallel Batch Processing: “Cost Associativity” – Many systems for a short time. Washington Post used 200EC2 instances to process
17,481 pages of Hillary Clinton’s travel documents within 9 hours of their release.

Rise of Analytics: Again, “Cost Associativity” – Many systems for a short time.
Compute intensive data analysis which may be parallelized.

Compute Intensive Desktop Apps: For example, symbolic mathematics requires lots of computing per unit of data. Cost efficient to
push the data to the cloud for computation


Conclusions and Questions
about the Cloud of Tomorrow
Utility Computing: It‘s Happening!
 Grow and Shrink on Demand
 Pay-As-You-Go
Cloud Provider‘s View
 Huge Datacenters Opened Economies and Possibilities
Cloud User‘s View
 Startups Don‘t Need Datacenters
 Established Organizations Leverage Elasticity
 UC Berkeley Has Extensively Leveraged Elasticity to Meet Deadlines
Cloud Computing: High-Margin or Low-Margin Business?
 Potential Cost Factor of 5-7X
 Today‘s Cloud Providers Had Big Datacenter Infrastructure Anyway
Implications of Cloud:
 Application Software: Scale-Up and Down Rapidly; Client and Cloud
 Infrastructure Software: Runs on VMs; Has Built-in Billing
 Hardware Systems: Huge Scale; Container-Based; Energy Proportional


Top 10 Obstacles and Opportunities
Obstacle Opportunity
1 Availability of Service Use Multiple Cloud Providers;
Use Elasticity to Prevent DDOS
2 Data Lock-In Standardized APIs; Compatible Software to
Enable Surge Computing
3 Data Confidentiality and Auditability Deploy Encryption, VLANs, Firewalls;
Geographical Data Storage
4 Data Transfer Bottlenecks FedExing Disks; Data Backup/Archival;
Higher Bandwidth Switches
5 Performance Unpredictability Improved VM Support; Flash Memory; Gang
Scheduling VMs
6 Scalable Storage Invent Scalable Store
7 Bugs in Large Distributed Systems Invent Debugger that Relies on Dist VMs
8 Scaling quickly Auto-Scaler; Snaphots for Conservation
9 Reputation Fate Sharing Reputation Guarding Services
10 Software Licensing Pay-for-Use Licenses; Bulk Use Sales


#3 Obstacle: Data Confidentiality and Auditability
“My sensitive corporate data will never be in the cloud!”
Current Clouds Are Essentially Public Auditability Is Required
Networks
Sarbanes-Oxley

They Are Exposed to More Attacks HIPAA

Berkeley Believes There Are No Fundamental Obstacles
to Making Cloud Computing as Secure as Most In-House IT
Encrypted Storage Virtual LANs Network Middleboxes (Firewalls, Packet Filters)

Encrypted Data in the Cloud Is Likely More Secure than Unencrypted Data on Premises

Maybe: Cloud Concerns over
More Focus on
Provided Auditability Virtual National Boundaries USA PATRIOT Act Gives
Capabilities… Some Europeans
Auditing Below VMs Foreign Subpoenas Worries over SaaS in the
USA
Maybe More Tamper Resistant Blind Subpoenas


#4 Obstacle: Data Transfer Bottlenecks
Problem: At $100 to $150 per Terabyte Transferred,
Data Placement and Movement Is an Issue
Opportunity-1: Sneaker-Net Opportunity-2: Keep Data
 Jim Gray Found Cheapest Transfer in Cloud
Was FedEx-ing Disks  If the Data Is in the Cloud,
 1 Data Failure in 400 Attempts Transfer Doesn‘t Cost
Example: Ship 10TB from  Amazon Hosting Large Data
UC Berkeley to Amazon
 E.g. US Census
-- WAN: S3 < 20Mbits/sec:
10TB  4Mil Seconds  > 45 Days
 Free on S3; Free on EC2
$1000 in AMZN Net Fees  Entice EC2 Business
-- FedEx: Ten 1TB Disks via Overnight Shipping
< 1 Day to Write 10TB to Disks Locally
Opportunity-3: Cheaper WAN
Cost ≈ $400  High-End Routers Are a Big
Effective BW of 1500Mbits/Sec Part of the Cost of Data
“NetFlix for Cloud Computing” Transfer
 Research into Routing using
Cheap Commodity Computers

To better understand, read the originals...
Above the Clouds: a Berkeley View of Cloud Computing
 http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-28.pdfCloud Computing
Book Report‖ on the UC Berkeley Paper ―Above the Clouds: a Berkeley
View of Cloud Computing‖
 http://blogs.msdn.com/pathelland/archive/2009/04/10/book-report-on-the-uc-berkeley-
paper-above-the-clouds-a-berkeley-view-of-cloud-computing.aspx
 http://cid-84f3c5ef51d06e8b.skydrive.live.com/self.aspx/.Public/2009/Above-the-Clouds-
090401k.pptx
Demystifying the Cloud (Simon Guest)
 http://simonguest.com/blogs/smguest/archive/2009/05/14/Slides-from-TechEd-2009.aspx
An introduction to Cloud Computing
 http://s3.amazonaws.com/ppt-download/ima-cloud-computing-mar2010-v8-100320181538-
phpapp02.pdf?Signature=GhK3ogCr2Z%2FzhWFa%2F%2BJUr1cT1eg%3D&Expires=12699
58049&AWSAccessKeyId=AKIAJLJT267DEGKZDHEQ
…and many others


A Spectrum of Application
Models
Less Constrained Constraints in the App Model More Constrained

Google App Engine

Amazon AWS Microsoft Azure Traditional Web Apps
.NET CLR/Windows Only Auto Scaling/Provisioning
VMs Look Like Hardware
Choice of Language
No Limit on App Model
Some Auto Failover/ Scale (but Force.Com
User Must Implement Scalability and
needs declarative application
Failover SalesForce Biz Apps
properties)
Auto Scaling/Provisioning

Less Automation Automated Management Services More Automation


Spectrum of Clouds
Instruction Set VM (Amazon EC2, 3Tera)
Bytecode VM (Microsoft Azure)
Framework VM
 Google AppEngine, Force.com

Lower-level, Higher-level,
Less management More management

EC2 Azure AppEngine Force.com
49


A Spectrum of Application Models

Which Model Will Dominate?? High-Level Languages and
Frameworks Can Be Built on Lower-
Analogy: Programming Languages and Frameworks Level
• Low-Level Languages (C/C++) Allow Fine-Grained Control
• Building a Web App in C++ Is a Lot of Cumbersome Work
• Ruby-on-Rails Hides the Mechanics but Only If You Follow More-Constrained Clouds May Be
Request/Response and Ruby’s Abstractions Built on Less-Constrained Ones


Deploying A Service Manually
Resource allocation
 Machines must be chosen to host roles of the service
 Fault domains, update domains, resource utilization, hosting environment, etc.
 Procure additional hardware if necessary
 IP addresses must be acquired
Provisioning
 Machines must be setup
 Virtual machines created
 Applications configured
 DNS setup
 Load balancers must be programmed
Upgrades
 Locate appropriate machines
 Update the software/settings as necessary
 Only bring down a subset of the service at a time
Maintaining service health
 Software faults must be handled
 Hardware failures will occur
 Logging infrastructure is provided to diagnose issues
This is ongoing work…you‘re never done

Windows Azure Service Lifecycle
Goal is to automate life cycle as much as possible

Coding & Provisioning Deployment Maintain goal
Modeling state

•New services •Desired •Mapping and •Monitor
and updates configuration deploying to •React to
actual events
hardware
•Network
configuration

Developer Developer/ Automated Automated
Deployer


ARCHITECTURE OF A CLOUD

ENVIRONMENT
users

INTERNET

Application
Application
Application Data Application
s
developers
COMPUTE STORAGE
Platform
FABRIC

Infrastructure


Domain Specific Cloud
Components for General
Availability in the Research
Marco Parenzan
•Tenure - Web Service Programming
Computer Engineering – University of Trieste
•Researcher
Methodologies and Tools
MOSE Laboratory – University of Trieste

Maurizio Fermeglia
•Full Professor Chemical Engineering
MOSE Laboratory – University of Trieste


MOSE: Molecular Simulation Engineering
Vision
 Multi – Scale Molecular Modeling will revolution the
world of research and industrial production in the next
years by strongly accelerating the development of new
products.
Mission
 Material Sciences: thermo physical properties for materials, polymer
technology and nanoscience/nanotechnology
 Life Sciences: drug-receptor interactions, drug-design, QSAR, drug-
S02 S08
delivery…
Process simulation: process synthesis, design, modeling for chemical,
S01 COL1
MAKUPB
COL2

S05 H3
T1

H1 P1
S13Z MAKUPA


S12
F1 Q1
S07
S10
M1

S06

H2

biochemical, energy production
H4

S14 S13


Multiscale Molecular Modeling
Characteristic Time

years Engineering
Engineering
design
design
hours
Simulazione
Process
minutes Simulation
di processo
FEM
seconds
Mesoscale
modeling FEM
(segments)
microseconds
Meccanica
Molecular
nanoseconds Mechanics
molecolare
(atoms)
(atomi)
picoseconds Quantum
Meccanica
Mechanics
Quantistica
femtoseconds (electrons)
(elettroni)

1Å 1nm 1μm 1mm 1m

Characteristic Length


Message Passing Multiscale Molecular Modeling
Engineering
design

Process
Simulation
FEM

Mesoscale
modeling
(segments)

Molecular
Mechanics
(atoms)

Quantum
Meccanica
Mechanics
Quantistica
(electrons)
(elettroni)


Cloud-based Message Passing for
Multiscale Molecular Modeling

Engineering
Engineering
design
Quantum design
Mechanics Process
(electrons) Simulation
FEM
Simulazione
di processo

FEM

Mesoscale Meccanica
modeling molecolare
(segments) (atomi)


Abstract
This paper deals with availability of cloud computing to computational research labs. We will focus to the
concept of availability. This concept may have two different interpretations, namely:
 ―Available‖ as an accessible resource, always, from everywhere
 ―Available‖ as the ability to consume a service (as a client or as the publisher)

AvailableAccessible
This paper will focus on the second interpretation: a cloud service is ―available‖ if it is easy for anyone in the
academic community (and not) to consume the cloud. Indeed, cloud allows sharing ―knowledge‖ in form of
components or data to be ―executed‖ in the cloud. The challenge here is to make possible for
researchers, not necessarily expert in programming and computer science, to make
available her/his knowledge in form of components and data tables.
The solution we propose is based on Domain Specific Languages (DSL), by which a researcher will express the
components in her/his specific language, that will be user-friendly since it is directly related to the particular
research field. In this framework, cloud components will be expressed in terms of a generic mathematical model
rather than a software component. This vision is quite common in computing thanks to the availability of many
tools that simplify the development of DSL such as dynamic languages like IronRuby or revolutionary data
access with SQL Server Modeling.
The objective of this work is to present a model of a general ―Domain Specific Cloud Component‖ (DSCC) that
can be expressed, published and consumed by the research community using tools that allow an easy and direct
implementation for the mathematical algorithms developed by the scientists. The general concept will be applied
to specific examples by developing frameworks customized to share a specific ―DSCC‖.
Examples will be taken in the area of multiscale molecular modeling for the design of nanostructured polymer
systems (nanotechnology) and the estimation of the environmental impact of a production process
(sustainability).


MOSE in the cloud…
…no. Why?
 Because it heavily depends on software (molecular simulation, process
simulation) that are not on the Cloud
Can MOSE access ―alone‖ the Cloud? No, at the moment
 The actors:
 Chemists, Chemical Engineers, Materials Engineers, Biologists, Medical
Doctors …
 Just ―Computer Science‖ classes in the first two years of Engineering
Curriculum (some C/C++, no VB(A) or .NET, some Matlab)
 But they need programs to solve their problems…
 …and sometimes they try to write them!


Objectives of this Research
Move MOSE to the Cloud!
Cannot wait software companies
Computer engineers can ―simplify‖ write these codes
 But she needs speaking with (non-computer) engineers about the
details (Analysis, Specifications, ―DOMAIN‖)
Why don‘t we enable (non-computer) scientists writing their
own code?
Simplifying (programming) tools to consume the Cloud
Allow DOMAIN Engineers participating actively building the
platform


Simplification development path
We are ‗still @ C++‘ (some apps need C++ plug in/custom code)
We already stepped into CLR world
 Example our development in CAPE-OPEN (http://co-lan.org)
The next step are
 Dynamic Languages such as Python or Ruby
 DSLs world for data (custom data texts)
 Again another example in CAPE-OPEN

Native VM Dynamic DSL

Internal
C/C++ C#/Java Python/Ruby
External


Traditional App Architecture: n-Tier apps

Presentation

Infrastructure
Business Logic (BLL)

Data Access (DAL)


Onion Architecture
Domain Driver Design User Interface

Application Services

Domain Services Database

Domain
M
Model Services

G
File
system

etc


Domain Driven
Design

Objects ---> Vocabulary
Grammar ---> Language

DSL

VISUAL UNIT
PROGRAMMING WITH
CAPEOPENSTUDIO.NET
Marco Parenzan, Maurizio Fermeglia
MOSE Lab – University of Trieste


CAPE-OPEN Toolkit Wizard


Exploring the model


Language: our tool
Il linguaggio di programmazione è il nostro strumento (―la
nostra cassetta degli attrezzi‖)
 Ma la metafora (meccanica) si ferma qui
 Una chiave inglese è immutabile
 I nostri linguaggi no
 Non siamo più nel XX secolo in cui VB6, C, C++, Pascal, Perl, Java,
Javascript erano a lungo stabili…
Stiamo vivendo un momento particolarmente fertile…


A Language Renaissance
Diversi paradigmi di programmazione
 Imperativo
 Object Oriented
 Funzionale
 Dichiarativo
Visual Studio 2010 esce con un nuovo linguaggio ufficiale
(F#)
Tipizzazione
 Statica (compile type)
 Dinamica (runtime)


Why should we care then?
 More languages, more
options
 DLR gives apps instant C# 4.0
scripting abilities Dynamic
 C# has moved in that C# 3.0
Programming

direction too! Language Integrated
Query
 LINQ C# 2.0
 Lambda expressions Generics
 Parallel extensions (C# C# 1.0
4.0) Managed Code
 ‗dynamic‘ (C# 4.0) and
‗var‘ keywords


Using internal DSL
(aka Fluent Interface)


Good
Easy to do... no parsers, etc.

Full IDE support

Bad
Limited by host language

Using external DSL


Good
Unlimited expressiveness
You choose execution
environment

Bad
Requires “more” work
No IDE support

Don Box


Don Box Career
DevelopMentor years (‗90ties)
 Worldwide COM expert (Essential COM)
Millenium Work (‗90ties thru 21° century)
 SOAP Specifications
 XML musings (Essential XML)
 XML Schema
 XML Infoset
.NET Expert (Essential .NET)
TechEd 2001, Barcelona
 Musings having a bath in a tube on stage
Microsoft years (since 2002)
 Indigo Architect (Windows Communication Foundation – 2° generation
Web Services - .NET 3.0)
 Oslo (now SQL Server Modeling)


Il linguaggio “M”

DSL Point.m DSLX DomainX.m DSLY DomainY.m
Domain Model Domain Model Domain Model
GPSLanguage.mg DomainX.mg DomainY.mg
Domain Grammar Domain Grammar Domain Grammar

"M"
Domain-specific data models type Point {
X : Integer where X < 100;
Y : Integer?;
MSchema }

Domain-specific grammars language GPSLanguage {
syntax Main = h:Integer ("," v:Integer)?
=> Point { X { h }, Y { v }};
}
MGrammar
Abstract data model Point { X { 100 }, Y { 200 } }
MGraph

86


Da M al Repository
Da “Oslo” a “SQL Server Modeling”

ModelA.m M.exe MX.exe
Domain Model Domain Model
ModelB.m Compiler Loader
ModelC.m ModelABC.mx

SQL
M M
Framework Framework Server


IntelliPad
Chiamato inizialmente EMACS.NET
Editor testuale, non ha funzioni visuali
Buffer interni interagiscono con runtime
 Parsing in tempo reale
 Generazione risultati in finestre side-by-side


MGrammar in Intellipad

Input Grammar Output

Text MGraph
Transfor
m

Errors


Douglas Purdy


ANTLR Overview
ANother Tool for Language Recognition
written by Terence Parr in Java

Easier to use than most/all similar tools
graphical grammar editor and debugger I’m a
Supported byBovet using Swing
written by Jean
ANTLRWorks professor at the
University of San

Used to implement Francisco.

domain-speciﬁc languages (DSLs)
“real” programming languages Ter
http://www.antlr.org I worked with
Ter as a masters
download ANTLR and ANTLRWorks here student there.

both are free and open source
docs, articles, wiki, mailing list, examples
Jean
ANTLR 3
3


ANTLR Overview ...
Uses EBNF grammars
can directly express optional and repeated elements BNF grammars require more
Extended Backus-Naur Form verbose syntax to express these.

supports subrules (parenthesized groups of elements)

Supports many target languages
for generated code
Java, Ruby, Python, Objective-C, C, C++ and C#

Provides inﬁnite lookahead
most parser generators don’t
used to choose between rule alternatives

Plug-ins available for
4 ANTLR 3
IDEA and Eclipse

ANTLR Overview ...
Supports LL(*)
LL(k) parsers are top-down parsers that
construct a Leftmost derivation of the input
parse from Left to right Wikipedia has
look ahead k tokens
good descriptions
of LL and LR.
LR(k) parsers are bottom-up parsers that
parse from Left to right

construct a Rightmost derivation of the input

look ahead k tokens

LL parsers can’t handle left-recursive rules
most people ﬁnd LL grammars easier to understand than LR

Supports predicates
aid in resolving ambiguities (non-syntactic rules)
5 ANTLR 3


ANTLRWorks
A graphical grammar editor and debugger
Features
highlights grammar syntax errors
checks for grammar errors beyond the syntax variety
such as conﬂicting rule alternatives

displays a syntax diagram for the selected rule
debugger can step through creation of parse trees and ASTs

38 ANTLR 3


ANTLRWorks ...
parser rule
syntax diagram

39 ANTLR 3
lexer rule
MOSE – University of Trieste syntax diagram 30 April, 2010 - slide 95

ANTLRWorks ...

grammar check
result

40 ANTLR 3


ActiProSoftware
Free add-ons are
included that
integrate domain-
specific language
(DSL) parsers created
using Microsoft Oslo's
MGrammar and
ANTLR with
SyntaxEditor.


Le peculiarità di un Dynamic Language
Semplice e stringato
 È una scelta tipica di chi ha sviluppato questi linguaggi
 Sintassi leggera
 Molte funzioni lasciate in API in linguaggi statici sono implementate nel
linguaggio
Interpretato
 È la diretta conseguenza della semplicità, evitando la ―complicazione‖
di un processo di compilazione
Implicitamente tipizzato
 Il tipo è associato ai valori, non alle variabili
 Non permettono di verificare e notificare gli errori di tipo se non
quando vanno in esecuzione

98

Cos’è Python?
Un linguaggio di programmazione general purpose
Sviluppato da Guido van Rossum nel 1991
Un linguaggio dinamico usato spesso come linguaggio di
scripting
Supporta diversi paradigmi di programmazione:
 Object Oriented
 Imperative
 Functional
È stato creato con questi obiettivi in mente:
 Leggibilità del codice
 Sintassi minimalista
 Un esteso set di librerie
 Duck Typing
99

Cos’è IronPython?
È una implementazione del linguaggio Python su piattaforma .NET
 IronPython è scritto interamente in C#
Creato da Jim Hugunin
 sviluppatore anche di Jython (Python su JVM)
Voleva scrivere un paper dal titolo ―Why .NET is a Terrible Platform for
Dynamic Languages‖
 ―Itwas a little less than a year ago that I first started
investigating the Common Language Runtime (CLR). My
plan was to do a little work and then write a short pithy
article called, "Why .NET is a terrible platform for dynamic
languages‖" http://www.ironpython.com/old.html
Nel settembre del 2004 iniziava a lavorare in Microsoft
 ―My plans changed when I found the CLR to be an
excellent target for the highly dynamic Python language.
Since then I've spent much of my spare time working on
the development of IronPython‖ http://www.ironpython.com/old.html
 http://www.python.org/community/pycon/dc2004/papers/9/
 http://conferences.oreillynet.com/presentations/os2004/hugunin_jim_up.ppt
100

Armando Fox (U.C. Berkley)
Ha parlato di uso di
Python in ambienti
Cloud
Parla di PLL
(Production Level
Languages)...
Vs. BLL


Dynamic Languages on .NET
IronPython IronRuby C# VB.NET Others…

Dynamic Language Runtime

Expression Trees Dynamic Dispatch Call Site Caching

Object JavaScript Python Ruby COM
binder binder binder binder binder


Storage in Windows Azure
GOAL:
SCALABLE, DURABLE
STORAGE Tables: simply Queues: serially
structured data,
Blobs: large, accessed messages
accessed using
unstructured or requests, allowing
Windows Azure storage is an data (audio,
ADO.NET Data web-roles and worker-
application managed by the Services
video, etc) roles to interact
Fabric Controller

Windows Azure applications can use
native storage or SQL Azure

Application state is kept in storage
services, so worker roles can
replicate as needed


Simplification steps
1. Write apps running on cloud
1. Windows Azure
2. (ASP.NET MVC2) Web Role for the front-end
3. Worker Role for background processing
4. Table, Blob and Queue for ―unstructured‖, but easy, storage
2. Use Dynamic Languages to do the processing
1. Simplified deployment
2. Simplified ―code‖ model
3. Simplified type management (dynamic typing, no variable declaration)
4. Now fully integrated in .NET with DLR and IronPython and IronRuby
3. Input and Output as structured text
1. ―M‖ (in ―Oslo‖, now SQL Server Modeling) gives us a generic schema
language (more general that XSD) and more ―readable‖ than xml
2. This gives structure and metadata to the Azure Storage data (as
requested by Ed Lazowska in his yesterday wonderful keynote)

Domain Specific Cloud
Components for General
Availability in the Research

Demo


The matrix was too simple?

This is a two-dimensional matrix of three dimensional vectors
Size of cube is: 100 nano meters

The results
1. Windows Azure
3. Web Role for background processing
3. Simplified type management (dynamic typing, no
variable declaration)
4. Now fully integrated in .NET with DLR and
IronPython and IronRuby
1. Oslo (now SQL Server Modeling) gives us a generic
schema language (more general that Xsd) and
more ―readable‖ than xml
2. Structured text as data sources


Conclusions
Why MOSE needs the cloud?
 To build a platform to orchestrate the message passing in Multiscale Molecular Modeling
activity
 To empower our research team with a flexible scientific platform that drives efficiency,
collaboration and innovation
In the demo we have seen
 The ―creation‖ and the execution (invocation) of the single step of the process
 The input and the output are the ―messages‖ that walk through the scale
The code:
 Definition of a library of a generic cloud component
 Usage of Dynamic Languages (IronPython)
 A new opportunity in .NET development
 More productive (PLLs, as told by Armando Fox yesterday)
 More simpler for non programmers
 Application of DSLs (Oslo) for the definition of simple input/output messages
 More confident with scientific people
 More simple than a graphical UI to implement
 It gaves metadata/schema to flat files (as requested by Ed Lazowska in his yesterday wonderful
keynote)

What‘s next?

What is next?
Continue with the project
The definition of a process (an orchestration)
 Did you saw the session from Paul Watson yesterday? (―Cloud
Computing from chemical Property Prediction‖)
The users in the process
 Collaboration in the process
 Again, as Paul said, we agree on a structure like a ―social science
community‖, a Web 2.0 application
 Security, Confidentiality
Verticalization on the domain
 Remove all the nitty-gritty details that lowers the experience
 Define custom component Languages


Simplification steps
1. Windows Azure
3. Worker Role for background processing
4. Table, Blob and Queue for ―unstructured‖, but easy, storage
3. Simplified type management (dynamic typing, no variable declaration)
4. Now fully integrated in .NET with DLR and IronPython and IronRuby
1. ―M‖ (in ―Oslo‖, now SQL Server Modeling) gives us a generic schema
language (more general that XSD) and more ―readable‖ than xml
2. This gives structure and metadata to the Azure Storage data
4. Write DSL

Writing a Custom DSL
(Supposed)Needs of the ―non-
cloud component
#naming part (entry point)
Name = "test 0004"

programmer‖ # declarative part

 Libraries # sections like cobol

 Integrated functionalities input
i(label = "Input Vector")
 No ―include‖ data
# static declaration
 Data Access as Libraries m(name = "matrix01" # this is the "query"
label = "multiplication matrix")
 Connect  Command  Execute LINQ output
o(label = "Output Vector")
 Define Datasource (Metadata), no SQL
schema # coding part
# dynamic like python (and vb)
 All-in-one # verbose like visual basic

 One Component, one ―file‖ (as much as code "this is the main"

possible)
# alterernative syntax of query from storage
# calculated

 Simplifing deployment m = lookup in Matrici
for NomeMatrice

Need of the programmer ### multiline
comment ###
 Not so (much) imperative, not so (much)
assign 0 to r
functional, not so (much) object oriented while r is less then m.rows do
assign 0 to c
 State is not so bad assign 0nm to a
while c is less then m.cols do
 Lambda are cool (no functions, all lambdas) #a = a + m(r,c) * i(c)
increment a by m(r,c) * i(c)

Escape to power (if DSL is ―poor‖) # python has no matrix, but jagged arrays
increment c by 1

 Backend of a full language, totally integrated end do
assign a to o

 DLR, (Iron)Python, (Iron)Ruby, (Iron)JS increment r by 1
end do
(Javascript) and so on


Home Automation


Cloud Futures 2010
Le sessioni


Le sessioni
Esperienze su Private Clouds
 Idee su Public/Hybrid Clouds
Esperienze ―sistemistiche‖
 meno...esperienze ―programmative‖


Cloud Computing for Chemical Property
Prediction
Paul Watson, Newcastle University


Bertrand Meyer
ETH Zurich


Cloud Futures 2010
Le sessioni degli Italiani


Danilo Montesi
Danilo Montesi (UniBO) ha
presentato il progetto ―Connected
City Campus‖, per connettere
diverse strutture (dall‘ospedale
all‘università alla biblioteca)
facilitando la comunicazione e i
servizi per i cittadini e sfruttando
dalla rete wireless già esistente.
Come dicevo, molte domande alla
fine sono arrivate sulle leggi
italiane in tema di
privacy/conservazione dei dati.


Fabio Panzieri
Fabio Panzieri -nella foto
con Judith Bishop, Direttore
Relazioni Esterne di Microsoft
Research- anch‘egli di UniBO,
ha presentato ―QoS-aware
Clouds‖ che propone la
creazione di un middleware
all‘interno della piattaforma di
Cloud per assegnare ―fette di
cloud‖ in modo dinamico e in
funzione del livello di servizio
acquistato dagli utenti.


Domenico Talia
Domenico Talia dell‘Università
della Calabria, probabilmente fra
noi più coinvolto nell‘argomento
Cloud trattandolo già nella sua
didattica in progetti nazionali ed
europei, ed insegnando proprio
Grid Computing. La sua
presentazione ha ricevuto molte
domande perché ha presentato
un problema reale in gran parte
già risolto tramite soluzioni cloud
open source, ovvero con la
definizione di un framework che
permette agli sviluppatori di
creare processi componendo
servizi disponibili su cloud o
intercloud.


Antonio Cisternino
La presentazione di Antonio
Cisternino dell‘Università di
Pisa (Informatica) -veterano di
questi eventi e introdotto nel
gruppo di Redmond da oltre
10 anni- ha esposto un
sistema di controllo dinamico
per virtual machine, con client
già disponibili anche per
dispositivi mobili in HTML5, ad
uso di farm di servizi cloud che
debbano garantire disponibilità
e risparmio energetico.


Marco Parenzan
Marco Parenzan, ricercatore
dell‘Università di Trieste e il
più giovane della nostra
delegazione, ha presentato un
progetto molto interessante
per rendere disponibile l‘uso
del cloud a ricercatori NON
esperti in computer science
(nel caso specifico, a chimici)
attraverso l‘uso di un
linguaggio (DSL) che loro di
esprimere e definire in
maniera semplice le richieste
elaborative e i dati su cui esse
operano.


Grazie! Q&A
blog: http://blog.codeisvalue.com/
email: marco.parenzan@libero.it
web: http://www.codeisvalue.com/
skype: marco.parenzan
messenger marco.parenzan@live.it
slides http://www.slideshare.com/marco.parenzan
twitter: marco_parenzan


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