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
1.5 min

4. Block diagram of Data Flow Architecture
0.5 min
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
0.5 min

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

7. Batch sequential in business data processing
A Closer View
Batch sequential in business data processing

8. Summary Applicable Design Domains: Benefits: Limitation:
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
1.5 min
7.5 min now

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

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
1.5 min

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.
1.5 min

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.
1 min

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

14. Explanation of Example
2 min
$ mkfifo pipeA
$ mkfifo pipeB
$ 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
1.5 min

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
1.5 min

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
1.5 min
29.5 min now

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
1 min

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.
1.5 min

20. General Architecture
1 min

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.
1 min

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