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A quick overview of the Meandre infrastructure, programming models and tools.
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Meandre: !Semantic-Driven Data-Intensive !
Flows in the Clouds
Xavier Llorà!
National Center for Supercomputing Applications!University of Illinois at Urbana-Champaign!
[email protected] The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
Yes, It is not a Typo
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 assembled into Flows
– Foster Collaboration and Sharing
• Open Source
• Service Orientated Architecture (SOA)
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
Meandre: Infrastructure
• SEASR/Meandre Infrastructure:
– Dataflow execution paradigm
– Semantic-web driven
– Web oriented
– Supports publishing services
– Promotes reuse, sharing, and collaboration
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
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.
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
Meandre: Dataflow Example
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
Value1
Value2
Sum
Meandre: Dataflow Example
• Dataflow Addition Example
– Logical Operation ‘+’
– Requires two inputs
– Produces one output
• 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 The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
Value1
Value2
Sum
Meandre: The Dataflow Component
• Data dictates component execution semantics
Component
P
Inputs Outputs
Descriptor in RDF!of its behavior
The component !implementation
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
Meandre: Data Driven Execution
• Dataflow Approach
– May have zero to many inputs
– May have zero to many outputs
– Performs a logical operation when data is available
• The component define its firing policy
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
Meandre: Component Metadata
• Describes a component
• Separates:
– Components semantics (black box)
– Components implementation (Java, Python, Lisp)
• Provides a unified framework:
– Basic building blocks or units (components)
– Complex tasks (flows)
– Standardized metadata
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
Meandre: Semantic Web Concepts
• Relies on the usage of the resource description framework (RDF)
• 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.
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
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
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
Meandre: The Dataflow Component
• Data dictates component execution semantics
Component
P
Inputs Outputs
Descriptor in RDF!of its behavior
The component !implementation
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
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
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
Wrapping With Components
• Component provides inputs, outputs, properties
• You code
– Inside!
– Call from!
– A WS front end
– Interactive application
– Request/response cycles
Meandre: Flow (Complex Tasks)
• A flow is a collection of connected components
Read
P Merge
P
Do
P
Show
P
Get
P
Dataflow execution The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
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
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
Meandre: Workbench Existing Flow
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
Flows
Components
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.
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
• ZigZag code that represents example flow:
Meandre: ZigZag Script Language
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
# # Imports the three required components and creates the component aliases # import <http://localhost:1714/public/services/demo_repository.rdf> 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 # # Creates four instances for the flow # push_hello, push_world, concat, print = PUSH(), PUSH(), CONCAT(), PRINT() # # Sets up the properties of the instances # push_hello.message, push_world.message = "Hello ", "world!" # # Describes the data-intensive flow # @phres, @pwres = push_hello(), push_world() @cres = concat( string_one: phres.string; string_two: pwres.string ) print( object: cres.concatenated_string ) #
Meandre: ZigZag Script Language
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
# # Describes the data-intensive flow # @pu = push() @pt = pass( string:pu.string ) print( object:pt.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
• Automatic Parallelization
– Adding the operator [+AUTO] to middle component
– [+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].
Meandre: ZigZag Script Language
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
# Describes the data-intensive flow # @pu = push() @pt = pass( string:pu.string ) [+AUTO] print( object:pt.string )
• Automatic Parallelization
– Adding the operator [+4] would result in a directed grap
Meandre: ZigZag Script Language
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
# Describes the data-intensive flow # @pu = push() @pt = pass( string:pu.string ) [+4] print( object:pt.string )
# Describes the data-intensive flow # @pu = push() @pt = pass( string:pu.string ) [+4!] print( object:pt.string )
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
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
And Behind The Scenes?
• Architecture designed to scale
• Infrastructure
– Laptop
– Server
– Cluster
• Tools
– Talk to the infrastructure
– Workbench, ZigZag
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)
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
Meandre: Basic Single Server
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
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.
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
Meandre MDX: The Picture
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
MDXBa
ckbo
ne
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.
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
Meandre MDX: The Architecture
• 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.
The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation
Meandre: !Semantic-Driven Data-Intensive !
Flows in the Clouds
Xavier Llorà!
National Center for Supercomputing Applications!University of Illinois at Urbana-Champaign!
[email protected] The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation