SEASR eScience 2008

SEASR:

Meandre: !
Semantic-Driven Data-Intensive !
Flows in the Clouds
Xavier Llora, Bernie Acs, Loretta Auvil, Boris Capitanu, Michael Welge, David Goldberg

National Center for Supercomputing Applications!
University of Illinois at Urbana-Champaign

{xllora, acs1, lauvil, capitanu, mwelge, deg}@illinois.edu
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation

SEASR: The Project

SEASR: Software Environment for the!
Advancement of Scholarly Research
•  Funded by the Andrew W. Mellon Foundation to answer the humanities community’s call for a
research and development environment capable of powering leading edge digital humanities
initiatives.

•  Fosters collaboration through empowering scholars to share data and research processes with
an infrastructure and framework designed to support reusable, repeatable, and scalable services
and processes.

•  Designed to enable developers to rapidly design, build, and share software applications that
support research and collaboration using modular components that can be assembled to create
reusable data-ﬂows.

•  Project web site: http://seasr.org


SEASR: The High-Altitude Picture


SAESR: @ Work – Evolution Highway

SEASR: A Quick Overview
•  Addresses:
–  Challenges of transforming information into knowledge

–  Constructs software bridges to migrate unstructured and semi-
structured data into structured data and/or metadata to enable
analysis and accessibility.

•  Aims:
–  Make digital collections more useful and ﬂexible

–  Provide access to analytic processes and visualizations

–  Enable easy mash-up with other web-based services (SOA)


SEASR: Knowledge Discovery…
Predictable process

The Process
•  Selection
•  Preparation
•  Transform
•  Processing
•  Interpret


Predictable process across domains.

Domains
•  Literature
•  History
•  Music
•  Art
• Science


Predictable process across domains and digital collections.

Collection Types
• Text
•  Multimedia
•  Data


SEASR: Design Goals
•  Transparency
–  From a single laptop to a HPC cluster

–  Not bound to a particular computation fabric

–  Allow heterogeneous development

•  Intuitive programming paradigm
–  Modular Components, Flows, and Reusable

–  Foster Collaboration and Sharing

•  Open Source
•  Service Orientated Architecture (SOA)

Meandre: Infrastructure
•  SEASR/Meandre Infrastructure:
–  Dataﬂow execution paradigm
–  Semantic-web driven
–  Web Oriented
–  Supports publishing services
–  Modular components
–  Encapsulation and execution mechanism
–  Promotes reuse, sharing, and collaboration


Meandre: Data Driven Execution
•  Execution Paradigms
–  Conventional programs perform computational tasks by
executing a sequence of instructions.
–  Data driven execution revolves around the idea of
applying transformation operations to a ﬂow or stream
of data when it is available.

•  Dataﬂow Approach
–  May have zero to many inputs
–  May have zero to many outputs
–  Performs a logical operation when data is available

Meandre: Dataﬂow Example
•  Dataﬂow Addition Example
–  Logical Operation ‘+’
Value1
–  Requires two inputs
Sum
–  Produces one output
Value2

•  When two inputs are available
–  Logical operation can be preformed

–  Sum is output

•  When output is produced
–  Reset internal values

–  Wait for two new input values to become available

Meandre: The Dataﬂow Component
•  Data dictates component execution semantics

Inputs Outputs

Component

P

Descriptor in RDF! The component !
of its behavior
implementation

Meandre: Component Metadata
•  Describes a component
•  Separates:
–  Components semantics (black box)
–  Components implementation

•  Provides a uniﬁed framework:
–  Basic building blocks or units (components)
–  Complex tasks (ﬂows)
–  Standardized metadata


Meandre: Semantic Web Concepts
•  Relies on the usage of the resource description framework
(RDF) which uses simple notation to express graph relations
written usually as XML to provide a set of conventions and
common means to exchange information
•  Provides a common framework to share and reuse data
across application, enterprise, and community boundaries
•  Focuses on common formats for integration and combination
of data drawn from diverse sources
•  Pays special attention to the language used for recording how
the data relates to real world objects
•  Allows navigation to sets of data resources that are
semantically connected.

Meandre: Metadata Ontologies
•  Meandre's metadata relies on three ontologies:
–  The RDF ontology serves as a base for deﬁning
Meandre descriptors
–  The Dublin Core Elements ontology provides basic
publishing and descriptive capabilities in the description
of Meandre descriptors
–  The Meandre ontology describes a set of relationships
that model valid components, as understood by the
Meandre execution engine architecture


Meandre: Components in RDF
@prefix meandre: <http://www.meandre.org/ontology/> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
Existing!
@prefix dc: <http://purl.org/dc/elements/1.1/> .
Standards
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix : <#> .

<http://dita.ncsa.uiuc.edu/meandre/e2k/components/limited-iterations>
meandre:name Limited iterations^^xsd:string ;
rdf:type meandre:executable_component ;
dc:creator Xavier Llora^^xsd:string ;
dc:date 2007-11-17T00:32:35^^xsd:date ;
dc:description Allows only a limited number of
iterations^^xsd:string ;
dc:format java/class^^xsd:string ;
dc:rights University of Illinois/NCSA Open Source
License^^xsd:string ;
meandre:execution_context

<http://norma.ncsa.uiuc.edu/public-dav/Meandre/demos/E2K/V1/resources/
colt.jar> ,

<http://norma.ncsa.uiuc.edu/public-dav/Meandre/demos/E2K/V1/resources/
gacore.jar> ,

<http://dita.ncsa.uiuc.edu/meandre/e2k/components/limited-
iterations/implementation/> ,

<http://norma.ncsa.uiuc.edu/public-dav/Meandre/demos/E2K/V1/
resources/gacore-meandre.jar> ,

Meandre: Components Types
•  Components are the basic building block of any
computational task.

•  There are two kinds of Meandre components:
–  Executable components

•  Perform computational tasks that require no human
interactions during runtime

•  Processes are initialized during flow startup and are fired when
in accordance to the policies defined for it.

–  Control components

•  Used to pause dataflow during user interaction cycles

•  WebUI may be a HTML Form, Applet, or Other user interface

Meandre: Component Assemblies
•  Deﬁned by connecting outputs from one component to the
inputs of another.
–  Cyclical connections are supported

–  Components may have
•  Zero to many inputs

•  Zero to many output

•  Properties that control runtime behavior

•  Described using RDF
–  Enables storage, reuse, and sharing like components

–  Allows discovery and dynamic execution


Meandre: Flow (Complex Tasks)
•  A ﬂow is a collection of connected components

Read
Merge
P

P

Show
Get
P
P

Do
P

Dataflow execution

Meandre: Create, Publish, & Share
•  “Components” and “Flows” have RDF descriptors
–  Easily shared, fosters sharing, & reuse

–  Allow machines to read and interpret
–  Independent of the implementations

–  Combine different implementation & platforms

–  Components: Java, Python, Lisp, Web Services

–  Execution: On a Laptop or a High Performance Cluster

•  A “Location” is RDF descriptor of one to many
components, one to many ﬂows, and their
implementations


Meandre: Repository & Locations
•  Each location represents a set components/flows
•  Users can
–  Combine different locations together

–  Create components

–  Assemble flows

–  Share components and flows

•  Repositories Help
–  Administrate complex environments

–  Organize components and flows


Meandre: Metadata Properties
•  Components and Flows share properties such as
component name, creator, creation date, description, tags,
and rights.
•  Components specific metadata to describe the
components' behavior, it’s location, type of
implementation, firing policy, runnable, format, resource
location, and execution context
•  Flow specific metadata describes the directed graph of
components, components instances, connectors,
connector instance data port source, connector, instance
data port target, connector instance source, connector
instance target, instance name


Meandre: Programming Paradigm

•  The programming paradigm creates complex
tasks by linking together a bunch of specialized
components. Meandre's publishing mechanism
allows components develop by third parties to be
assembled in a new ﬂow.
•  There are two ways to develop ﬂows :
–  Meandre’s Workbench visual programming tool
–  Meandre’s ZigZag scripting language


Meandre: Workbench Existing Flow

Components

Flows

Locations


Meandre: Workbench Create Flow

Components

Flows
Locations


Drag & Drop Selected
Component into
workspace

Components

Flows
Locations


Properties for Selected
Component Exposed

Components

Flows
Locations



Description for Selected
Component Exposed

Components

Flows
Locations


Drag & Drop Another
Component into
workspace

Components

Flows
Locations


Connect Output of First
Component to Input of
Second

Click First Port to
connect will highlight
Components
with color change (Red)

Flows
Locations


Connect Output of First
Component to Input of
Second

Click Port to Connect
will cause a line to be
Components
displayed as visual
indicator

Flows
Locations


Repeat Drag & Drop to
Complete the Assembly

Components

Flows
Locations


Meandre: ZigZag Script Language
•  ZigZag is a simple language for describing data-
intensive flows
–  Modeled on Python for simplicity.
–  ZigZag is declarative language for expressing the
directed graphs that describe flows.

•  Command-line tools allow ZigZag files to compile
and execute.
–  A compiler is provided to transform a ZigZag program
(.zz) into Meandre archive unit (.mau).
–  Mau(s) can then be executed by a Meandre engine.

•  As an example the Flow Diagram
–  The ﬂow below pushes two strings that get concatenated and
printed to the console

– 


•  ZigZag code that represents example ﬂow:
#
# Imports the three required components and creates the component aliases
#
Repository import <http://localhost:1714/public/services/demo_repository.rdf>
Location alias <http://test.org/component/push_string> as PUSH
alias <http://test.org/component/concatenate-strings> as CONCAT
alias <http://test.org/component/print-object> as PRINT
#
Defines the logical # Creates four instances for the flow
repository location #
push_hello, push_world, concat, print = PUSH(), PUSH(), CONCAT(), PRINT()
where components in #
this flow can be found # Sets up the properties of the instances
#
similar to defining a push_hello.message, push_world.message = Hello , world!
location for workbench #
# Describes the data-intensive flow
which would then #
display available @phres, @pwres = push_hello(), push_world()
@cres = concat( string_one: phres.string; string_two: pwres.string )
components located print( object: cres.concatenated_string )
there #


#
#
import <http://localhost:1714/public/services/demo_repository.rdf>
alias <http://test.org/component/push_string> as PUSH
Alias alias <http://test.org/component/concatenate-strings> as CONCAT
#
# Creates four instances for the flow
Assigns a logical #
name reference for push_hello, push_world, concat, print = PUSH(), PUSH(), CONCAT(), PRINT()
#
each component # Sets up the properties of the instances
making subsequent #
push_hello.message, push_world.message = Hello , world!
program calls easier to #
read and write. # Describes the data-intensive flow
#
@phres, @pwres = push_hello(), push_world()
print( object: cres.concatenated_string )
#


#
#
alias <http://test.org/component/concatenate-strings> as CONCAT
#
# Creates four instances for the flow
#
Implementation push_hello, push_world, concat, print = PUSH(), PUSH(), CONCAT(), PRINT()
Instances #
# Sets up the properties of the instances
#
Create instances of push_hello.message, push_world.message = Hello , world!
the components using #
the “Alias” references #
similar to dragging @phres, @pwres = push_hello(), push_world()
components on to print( object: cres.concatenated_string )
workbench canvas #


#
#
Define the property import <http://localhost:1714/public/services/demo_repository.rdf>
values for components alias <http://test.org/component/concatenate-strings> as CONCAT
which is similar to filing alias <http://test.org/component/print-object> as PRINT
#
in values in the # Creates four instances for the flow
workbench’s properties #
push_hello, push_world, concat, print = PUSH(), PUSH(), CONCAT(), PRINT()
panel. #
#
Set the Property push_hello.message, push_world.message = Hello , world!
Values #
#
print( object: cres.concatenated_string )
#


#
#
Define the connections alias <http://test.org/component/concatenate-strings> as CONCAT
or relationships between #
the components in this # Creates four instances for the flow
#
flow which is similar to push_hello, push_world, concat, print = PUSH(), PUSH(), CONCAT(), PRINT()
drawing connection #
lines on the workbench #
canvas push_hello.message, push_world.message = Hello , world!
#
#
Describe @cres = concat( string_one: phres.string; string_two: pwres.string )
Connections print( object: cres.concatenated_string )
#


•  Automatic Parallelization
–  Multiple instances of a component could be run in parallel to boost
throughput.

–  Specialized operator available in ZigZag Scripting to cause multiple
instances of a given component to used
•  Consider a simple ﬂow example show in the diagram

•  The dataﬂow declaration would look like
#
#
@pu = push()
@pt = pass( string:pu.string )
print( object:pt.string )

–  Adding the operator [+AUTO] to middle component
#
@pu = push()
@pt = pass( string:pu.string ) [+AUTO]

–  [+AUTO] tells the ZigZag compiler to parallelize the “pass
component instance” by the number of cores available on
system.
–  [+AUTO] may also be written [+N] where N is an numeric
value to use for example [+10].


–  Adding the operator [+4] would result in a directed graph

#
@pu = push()
@pt = pass( string:pu.string ) [+4]


–  ZigZag has created 4 parallel instances of the component.
•  It has also introduced a mapper instance that is in charge of
distributing the incoming data to each of the parallel instance.

•  This is called unordered parallelization, since data may be
arriving to the print ﬂow out of the original order in which they
were generated by the push component instance.


–  The operator [+AUTO] can be told to maintain data order with
“!”

#
@pu = push()
@pt = pass( string:pu.string ) [+AUTO!]

–  The [+AUTO!] tells the ZigZag compiler to parallelize the “pass
component instance” by the number of cores available on
system and to maintain order of data throughput.


–  ZigZag has created 4 parallel instances of the component.
•  It has also introduced a mapper instance that is in charge of
distributing the incoming data to each of the parallel instance.

•  It has also introduced a reducer instance that is in charge of
distributing the incoming data to each of the parallel instance


Meandre: Flows to MAU
•  Flows can be executed using their RDF
descriptors
•  Flows can be compiled into MAU
•  MAU is:
–  Self-contained representation
–  Ready for execution
–  Portable
–  The base of ﬂow execution in grid environments


Meandre: The Architecture
•  The design of the Meandre architecture follows
three directives:
–  provide a robust and transparent scalable solution from
a laptop to large-scale clusters
–  create an uniﬁed solution for batch and interactive tasks
–  encourage reusing and sharing components

•  To ensure such goals, the designed architecture
relies on four stacked layers and builds on top of
service-oriented architectures (SOA)


Meandre: Basic Single Server


Meandre MDX: Cloud Computing
•  Servers can be
–  instantiated on demand
–  disposed when done or on demand

•  A cluster is formed by at least one server
•  The Meandre Distributed Exchange (MDX)
–  Orchestrates operational integrity by managing cluster
conﬁguration and membership using a shared database
resource.


Meandre MDX: The Picture
MDX Backbone 


Meandre MDX: The Architecture
•  Virtualization infrastructure
–  Provide a uniform access to the underlying execution environment. It relies on
virtualization of machines and the usage of Java for hardware abstraction.

•  IO standardization
–  A unified layer provides access to shared data stores, distributed file-system,
specialized metadata stores, and access to other service-oriented architecture
gateways.

•  Data-intensive flow infrastructure
–  Provide the basic Meandre execution engine for data-intensive flows, component
repositories and discovery mechanisms, extensible plugins and web user interfaces
(webUIs).

•  Interaction layer
–  Can provide self-contained applications via webUIs, create plugins for third-party
services, interact with the embedding application that relies on the Meandre engine,
or provide services to the cloud.

Meandre MDX: The Experiment
•  Experimental Prototype
–  Designed and built to validate viability of MDX cluster

–  Using VMWare Server 2.0 on three identical hosts with
•  Windows Server 2003

•  Equipped with two quad-core 2.8GHz Xeon processors

•  1600MHz front side bus

•  32Gb of RAM

•  4Tb of RAID 5 disk


•  Experimental Prototype
–  8 virtual Machine instances were created on each host with
•  32-bit Ubuntu 8.04 Linux

•  3 Gb RAM dedicated to each instance

•  1 Physical processor core assigned to each VM

•  VM instances were equipped to run a Meandre MDX server using Sun's Java
1.5 JVM

–  A Third Physical hosts support 2 virtual machine instances with
•  32-bit Ubuntu 8.04 Linux

•  3 Gb RAM dedicated to each instance

•  1 Physical processor core assigned to each VM

•  Highly available MySQL database and HTTP load-balancing facility

•  We conducted three different experiments
–  All three were based on the same flow shown earlier in the ZigZag
example with a single change to make the single line of text into
250,000 lines of text for each iteration of the flow.

–  The first test was designed to test the scalability of a single
Meandre server.

–  Concurrent flows !
running on a standalone!
engine on a log/log scale, !
each iteration of the flow !
pushed 250,000 lines of text



–  The second experiment were run against a virtual Meandre cluster
consisting of 16 Meandre servers.

pushed 1 lines of text



–  The third experiment were run against a virtual Meandre cluster
consisting of 16 Meandre servers.

pushed 250,000 lines of text


–  The first test clearly shows
•  The average time per flow increased linearly with the
number of concurrent flows

–  The next experiments clearly shows
•  Cluster throughput grows linearly with the number of
Meandre servers available


Upcoming Events
•  SEASR 2009 workshop
–  The workshop is organized to provide expanded
opportunities for learning, knowledge sharing, and
support and is intended to provide sufﬁcient
introduction and support so that teams can implement
a study using SEASR.
–  The workshop is intended for institutional teams of
scholars from the Humanities.
–  The workshop will include communication and work
from a team’s home campus as well as face-to-face
meeting on the University of Illinois campus.

SEASR eScience 2008

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