University:
California State University, Northridge
Course:
COMP 684 | Software Architecture and Design
Academic year:

2024
Views:

436

Pages:

19
Author:

Chace Holden

Dataflow Systems Style – Process Control Process Control: Components & Connectors  The following slides offer only an introduction to the process control style.  The process control style is modeled on the process control systems that are common in other engineering disciplines (e.g. chemical engineering).  Motivating example: furnace Process Control: Example Open-loop temperature control. Process Control: Example Closed-loop temperature control. Process Control: Example Applied to software Process Control: Components & Connectors Input variables (Feed-forward control) Process Controller Set point Changes to manipulated variables (Feedback control) Controlled variables Process Control: Key Characteristics  Organization:  Computational elements (important separation)    Process definition. Control algorithm. Data elements:   Process variables. Set point.  Control algorithm uses information about actual system state to tune the process, driving the actual state towards the intended state. Process Control: Strengths  Well suited for the control of continuous processes particularly where the control algorithm is subject to change (possibly even at run-time). Process Control: Weaknesses  Not easily applicable when multiple interacting processes (and controls) are needed. Process Control: Examples Process Control: Examples  Cruise control system  Heating system Style – Pipes and Filters: Components & Connectors  Components: Filter  Completely independent entities  Consume streams of inputs and produce streams of outputs  Context independent (do not know what produced the input or will consume the output)  Output starts before input is (entirely) consumed (incremental transformation of data)  Connectors: Pipes  Carrier of data streams  Binding between component ports Pipes and Filters: Key Characteristics  Data Flow Architecture  Model assumes concurrent processing.  Filters are independent.  Do not share data.  Do not know identity of neighbors.  Individual filter behavior: simple I/O relation  System behavior = simple composition of individual filter behaviors. Pipes and Filters: Strengths  System easy to understand   System = simple composition of individual filters No complex interaction between components  Reuse, Enhancement & Maintainability  Easy to add filters & replace existing ones (why?)  Concurrency can be easily exploited. Pipes and Filters: Weaknesses  Can lead to batch sequential processing  Not well suited to interactive applications  Use of this style may  Force least common denominator in data format  Can lead to extra work for filters to assemble and organize data  Can lead to decreased performance and increased filter complexity Pipes and Filters: Variations  Pipelines Linear sequences of filters Batch Sequential Systems  Pipeline; but each step runs to completion before the next starts Bounded pipes  Restriction on the amount of data that can temporarily reside/buffered on/in a pipe Typed pipes  Restricts the data passed between two filters to a welldefined type “UI pipes”  Each filter is associated with UI to allow the user to set parameters of operation      Pipes and Filters: Examples Pipes and Filters: Examples  Unix shell: e.g. ls | grep “my” | wc –l  Traditional view of compilers  Also has applications in     Signal processing Parallel programming Functional programming Distributed systems