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Data Flow Architecture

Data Flow Architecture. Objectives. Introduction to Data Flow Architecture Describe DFA in UML Application domain of DFA Benefit and limitation of DFA Demonstrate Batch sequential Pipe and filter in OS Java scripts. Overview. What is data flow architecture?

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Data Flow Architecture

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  1. Data Flow Architecture

  2. Objectives • Introduction to Data Flow Architecture • Describe DFA in UML • Application domain of DFA • Benefit and limitation of DFA • Demonstrate • Batch sequential • Pipe and filter in OS • Java scripts

  3. Overview • What is data flow architecture? • Whole system as transformation of successive sets of data. • System decomposed into modules. • Connection can be • IO Stream • Files, Buffers, Pipes • No interaction between modules • Modules do not need to know identity of each other

  4. Block diagram of Data Flow Architecture Note: the architecture may allow loops

  5. Categories of Data Flow Arch. • Many sub-categories exist • Batch Sequential • Pipe & Filter • Process Control • To adopt which one depends on the nature of the problem

  6. Batch Sequential • Traditional data processing model • Widely used in 1950’s – 1970’s • Example: mainframe computers using COBOL Note: deployment can differ even for the same batch-sequential arch.

  7. A Closer View Batch sequential in business data processing

  8. Summary • Applicable Design Domains: • Data are batched • Benefits: • Simple division between sub-systems • Each sub-system can be a stand-alone • Limitation: • No interactive interface • No concurrency and low throughput • High latency

  9. Pipe & Filters • Similar to Batch Sequence • Independent modules • Data connectors • Difference • Connectors are stream oriented • Concurrent processing

  10. Basic Concepts • Data Source • Data Sink • Filter: independent data stream transformer • Reads data from input data stream • Process data and write to output stream • Does not wait for batched data as a whole • Does not even have to know identity of i/o streams • Pipe: data conduit • Moves data from one filter to another • Two types: character or byte streams

  11. Data Flow Methods Three way to make data flow • Push only (Write only) • A data source may push data in a downstream • A filter may push data in a downstream • Pull only (Read only) • A data sink may pull data from an upstream • Filter may pull data from an upstream • Pull/Push (Read/Write) • A filter may pull data from an upstream and push transformed data in a downstream.

  12. Classification of Filters • Active Filter: • pulls in data and push out the transformed data (pull/push) • It works with a passive pipe that provides read/write mechanisms for pulling and pushing. • Example: UNIX pipe. • Passive filter • Lets connected pipe to push data in and pull data out. • The filter must provide read/write mechanisms in this case.

  13. Pipe & Filter In Unix • Unix provides pipe operation “|” • moves stdout from predecessor to the stdin of its successor • Example: • who | wc –l • Example: • $ mkfifo pipeA • $ mkfifo pipeB • $ grep a < pipeA >pipeB & • $ cat infile | tee pipeA | grep c |cat – pipeB | uniq –c

  14. Explanation of Example • $ mkfifopipeA • $ mkfifopipeB • $ grep a < pipeA >pipeB & • $ cat infile | tee pipeA | grep c |cat – pipeB | uniq –c

  15. Summary Pipe & Filter • Applicable Design Domain • System can be broken into a series of processing steps over data stream, in each step filter consumes and moves data incrementally. • Data format on the data stream is simple and stable, and easy to be adapted if it is necessary. • There are significant work which can be pipelined to gain the performance • Suitable for producer/consumer model

  16. Advantages • Concurrency is high. • Reusability is easy – plug and play. • Modifiability: Low due to coupling between filters • Simplicity: Clear • Flexibility: High, very modular design • Lower latency

  17. Disadvantages • Not suitable for dynamic interactions • Data standards (ASCII, XML?) • Overhead of data transformation among filters such as parsing is repeated in two consecutive filters • Difficult to configure a P&F system dynamically. • Error handling issue

  18. Process Control Model • Suitable for embedded System • Composed of • Sub-systems • Connectors • Two types of sub-systems • executor processor unit • controller unit • System depends on: Control Variables

  19. Data • Controlled variable: target controlled variable • E.g., Speed in a cruise control system • E.g., Temperature in an auto H/A system. • Input variable: measured input data • Manipulated variable: can be adjusted by the controller • E.g., motor rotation speed, etc.

  20. General Architecture

  21. Applicable Domains • Embedded software system involving continuing actions. • Needs to maintain an output data at a stable level. • The system has a set point which is the goal the system will reach and stay at that level.

  22. Pros and Cons • Benefits • Better for situations where no precise formula for deciding the manipulated variable • Can be completely embedded • Limitations: • Requires more sensors to monitor system states

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