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Instant Bidirectional QVT Transformation for Runtime Models. Runtime models. Models that represent the structure , state, configuration, etc., of an executing system Monitoring the system by reading the model Reconfiguring the system by writing th e model - PowerPoint PPT Presentation
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Instant Bidirectional QVT Transformation for Runtime Models
Runtime models
• Models that represent the structure, state, configuration, etc., of an executing system– Monitoring the system by reading the model– Reconfiguring the system by writing the model– With full semantics basis and supporting tools
• A hot topic in the models community– “A new trend” by Robert France’s survey on MDE– A special session in last MoDELS, a topic in this
Supporting runtime models
system Runtime system model
System meta-model
conforms to
RFID readers, tags, sensors…
Runtime application
model
Goods, storage…
Runtime application
model
Persons, things, rooms…
Different views according to different application concerns
Supporting runtime models
system Runtime system model
System meta-model
conforms to
Runtime application
model
Applicationmeta-modelrelation
conforms toGuided by
Causal connection
Approach overview
system Runtime system model
System meta-model in
MOF
conforms to
Runtime application
model
Applicationmeta-model in
MOF
Relation in QVT-R
conforms toGuided by
Instant QVT transformation
engine
Challenge
• Instant: – Traditional transformation is from model to model
• Inefficient when the models are big• System changes are small but frequent
– Require a new execution from change to change• With live models maintained as a reference
• Bidirectional:– Not bijective, one change may corresponds to many– Need a proper and determined semantics
Illustration
Batching transformationsr : SysRoot
name : pkusi
ar : AppRoot
name = pkusi
rm : Room
number = 1621temp = 16.0
ps : Person
name = Huiid = 102
th : Thing
name=phoneid = 104
root
room person thing
ownownerperson
locate
sn : Sensor
id = 1621temp=16.0Bright=620.0
rd : Reader
id = 1621
tg1 : Tag
id = 102
rootreader sensor
tag
reader
add:[rd, reader, tg2]
tg2 : Tag
id = 104
Why in-efficient: the impact of the change is not the whole model, but since the change is not considered, the impact scope is not utilized
Instant transformationsr : SysRoot
name : pkusi
ar : AppRoot
name = pkusi
rm : Room
number = 1621temp = 16.0
ps : Person
name = Huiid = 102
th : Thing
name=phoneid = 104
root
room person thing
ownownerperson
locate
sn : Sensor
id = 1621temp=16.0Bright=620.0
rd : Reader
id = 1621
tg1 : Tag
id = 102
rootreader sensor
tag
reader
add:[rd, reader, tg2]
tg2 : Tag
id = 104
set:[th, locate, rm]
About bidirectionalsr : SysRoot
name : pkusi
ar : AppRoot
name = pkusi
rm : Room
number = 1621temp = 16.0
ps : Person
name = Huiid = 102
th : Thing
name=phoneid = 104
root
room person thing
ownownerperson
locate
sn : Sensor
id = 1621temp=16.0Bright=620.0
rd : Reader
id = 1621
tg1 : Tag
id = 102
rootreader sensor
tag
reader
tg2 : Tag
id = 104
remove:[rd, reader, tg2]set:[th, locate, null]
destroy:[th]
Contributions of this paper
• Formal semantics of instant QVT transformation for runtime models
• Algorithms to implement instant QVT transformation
• An instant transformation engine based on mediniQVT
Formal semantics
• Abstraction:• Properties– Consistency:
– Stability:
– Restorability:
The algorithm
• Basic idea– Record the mapped model elements(traces)– Analyze the impact of the change• Influence no relations: stop• Influence a relation:
– Found the relevant traces, re-evaluate• Cause new groups of elements to be mapped
– Create new traces, record changes• Cause an existing trace to fail
– Delete the trace, roll-back the recorded changes
Detailed Algorithm
Evaluation
• Feasibility and effectiveness• Performance• Limitation
feasibility and effectiveness
• Main case study: Our SmartLab IOT system– System meta-model: rfid readers, tags, sensors, switches,
locks– Application meta-model: rooms, persons, things
• Scenarios:– Missing personal effects– Leaving the air-conditioner on– …
• Conforms to the expectations of scenario developers• Other case studies: JOnAS-C2, JOnAS-Client/Server
performance
• Made-up models conforming to IOT case– Control the scale– Eliminate other factors such as device invocations
Limitation
• For modeling– Every model element must has a key attribute– Only support unordered multiple properties
• For QVT– No “where” clause (but can use when)
Related work
• Runtime models– Usually based on code wrapping– One on TGG, but no discussion about runtime-model
specific semantics• Model transformation with semantics– On batching transformations
• Incremental transformation (supported by QVT)– Still model to model, not change to change
• Instant transformation– No transformation language, or TGG
Conclusion
• Runtime models and causal connections• A transformation-based approach to support
causal connections– Instant, bidirectional
• Semantics• Algorithms• Implementation and evaluation
Thank you for your attention
