This document presents the πRT-calculus, a calculus for modeling mobile real-time processes. It extends the π-calculus with a timeout operator to model real-time aspects. The document covers the syntax and semantics of the π-calculus and πRT-calculus. It also discusses design choices like having a global clock and discrete time. An example of a mobile video streaming system is used to illustrate the πRT-calculus. The document concludes by discussing future work, like developing timed bisimulation and extending to continuous time.
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45 min talk about collecting home network performance measures, analyzing and forecasting time series data, and building anomaly detection system.
In this talk, we will go through the whole process of data mining and knowledge discovery. Firstly we write a script to run speed test periodically and log the metric. Then we parse the log data and convert them into a time series and visualize the data for a certain period.
Next we conduct some data analysis; finding trends, forecasting, and detecting anomalous data. There will be several statistic or deep learning techniques used for the analysis; ARIMA (Autoregressive Integrated Moving Average), LSTM (Long Short Term Memory).
This slide explain complexity of an algorithm. Explain from theory perspective. At the end of slide, I also show the test result to prove the theory. Pleas, read this slide to improve your code quality .
This slide is exported from Ms. Power
Point to PDF.
EuroPython 2017 - PyData - Deep Learning your Broadband Network @ HOMEHONGJOO LEE
45 min talk about collecting home network performance measures, analyzing and forecasting time series data, and building anomaly detection system.
In this talk, we will go through the whole process of data mining and knowledge discovery. Firstly we write a script to run speed test periodically and log the metric. Then we parse the log data and convert them into a time series and visualize the data for a certain period.
Next we conduct some data analysis; finding trends, forecasting, and detecting anomalous data. There will be several statistic or deep learning techniques used for the analysis; ARIMA (Autoregressive Integrated Moving Average), LSTM (Long Short Term Memory).
This slide explain complexity of an algorithm. Explain from theory perspective. At the end of slide, I also show the test result to prove the theory. Pleas, read this slide to improve your code quality .
This slide is exported from Ms. Power
Point to PDF.
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Part 1: https://youtu.be/SW_WS6ULsdY
Part 2: https://youtu.be/bq3ECNvPwOU
You can find this slides, as well as the code examples, at https://github.com/vincenzo-gulisano/debs2020_tutorial_event_time and at SlideS
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Discretizing of linear systems with time-delay Using method of Euler’s and Tu...IJERA Editor
Delays deteriorate the control performance and could destabilize the overall system in the theory of discretetime
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control system by choosing various of energy loss level. Illustrative example is simulated to demonstrate
the effectiveness of the developed method.\
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In this talk we will demonstrate some time based pattern we discovered while working on a security analytics use case that uses Sessionisation. In the talk we will demonstrate such patterns based on an open source malware attack datasets that is available publicly.
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Understanding Globus Data Transfers with NetSageGlobus
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Designing for Privacy in Amazon Web ServicesKrzysztofKkol1
Data privacy is one of the most critical issues that businesses face. This presentation shares insights on the principles and best practices for ensuring the resilience and security of your workload.
Drawing on a real-life project from the HR industry, the various challenges will be demonstrated: data protection, self-healing, business continuity, security, and transparency of data processing. This systematized approach allowed to create a secure AWS cloud infrastructure that not only met strict compliance rules but also exceeded the client's expectations.
Multiple Your Crypto Portfolio with the Innovative Features of Advanced Crypt...Hivelance Technology
Cryptocurrency trading bots are computer programs designed to automate buying, selling, and managing cryptocurrency transactions. These bots utilize advanced algorithms and machine learning techniques to analyze market data, identify trading opportunities, and execute trades on behalf of their users. By automating the decision-making process, crypto trading bots can react to market changes faster than human traders
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Accelerate Enterprise Software Engineering with PlatformlessWSO2
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The alleged breach affected Europol agencies CCSE, EC3, Europol Platform for Experts, Law Enforcement Forum, and SIRIUS. Infiltration of these entities can disrupt ongoing investigations and compromise sensitive intelligence shared among international law enforcement agencies.
However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
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A calculus of mobile Real-Time processes
1. A calculus of mobile Real-Time processes
A calculus of mobile Real-Time
processes
lotfi.larbaoui@polymtl.ca
16 novembre 2017
2. A calculus of mobile Real-Time processes
Introduction
The π-calculus
General design choices and properties
The πRT-calculus
Example
Conclusion
3. A calculus of mobile Real-Time processes
Introduction
Introduction
The π-calculus
General design choices and properties
The πRT-calculus
Example
Conclusion
4. A calculus of mobile Real-Time processes
Introduction
Modelling Real-time system :
c Building models which faithfully represent complex system is a
non trivial problem and a prerequisite to the application of formal
analysis .d Joseph Sifakis 2001
Process calculi (CCS and pi-calculus etc) :
c A mathematical treatment of communicating and concurrent
entities that could rival the known theories of sequential
programming .d Robin Milner 2010
Real-time process algebra : πRT-calculus
Modeling the real-time aspect of systems in a mobile
environment and reasoning about dynamic temporal behaviour
and dynamic configurations of systems.
5. A calculus of mobile Real-Time processes
Introduction
Background
6. A calculus of mobile Real-Time processes
The π-calculus
Introduction
The π-calculus
General design choices and properties
The πRT-calculus
Example
Conclusion
7. A calculus of mobile Real-Time processes
The π-calculus
Syntax
P ::=
| 0 Inaction
| αi .P Action
| P1 + P2 Choice
| P1|P2 Parallel composition
| (νx).P Scoping
| [x = y]P Match
| A(x1, ..., xn) Agent identifier
| !P replication
Names : x , y , z
The actions αi are : τ Internal , ¯xy Output a name ,
x(y) Input a name ,¯x(y) Output a reference
8. A calculus of mobile Real-Time processes
The π-calculus
Semantics
The semantics of π-calculus is defined by :
1. Reduction binary relations : P → Q
2. Labeled transition : P
α
−→ Q
The inference rules defining the reduction relations and labeled
transition.
The semantics of the reductions use the notion of Structural
congruence
9. A calculus of mobile Real-Time processes
The π-calculus
Structural congruence
10. A calculus of mobile Real-Time processes
The π-calculus
Transition rules
11. A calculus of mobile Real-Time processes
The π-calculus
Transition rules
12. A calculus of mobile Real-Time processes
The π-calculus
Interactions between processes
13. A calculus of mobile Real-Time processes
General design choices and properties
Introduction
The π-calculus
General design choices and properties
The πRT-calculus
Example
Conclusion
14. A calculus of mobile Real-Time processes
General design choices and properties
Model Features
Global clock
Discrete time
Separation of actions and time events : interleaving model
Synchronous time event : Time progresses in a synchronous
fashion
Time transmission : can result in dynamic temporal behaviour
of processes.
15. A calculus of mobile Real-Time processes
General design choices and properties
Model Properties
Maximal progress :
if P
τ
−→ P for some P , then for t > O, P
(t)
−−→ P for no P .
Timelock freeness :
for all agent P and P
α
−→ P for some P and α = τ , then for
t > 0, P
(t)
−−→ P for some P .
Time visibility :
for t > 0, if P
(t)
−−→ P , then for any name x free in P ,
(νx)P
(t)
−−→ (νx)P
16. A calculus of mobile Real-Time processes
General design choices and properties
Model Properties
Time determinacy :
for t > 0, whenever P
(t)
−−→ P and P
(t)
−−→ P , then P ≡ P
Time continuity :
for t > 0 and u > 0 , P
(t+u)
−−−−→ P iff there exists P such that
P
(t)
−−→ P and P
(u)
−−→ P
Action persistency :
for t > 0, if P
(t)
−−→ P and P
α
−→ Q then P
α
−→ Q for some Q
17. A calculus of mobile Real-Time processes
General design choices and properties
Atomic actions and Idling
Non-controllable action (above) and controllable action (below)
18. A calculus of mobile Real-Time processes
The πRT-calculus
Introduction
The π-calculus
General design choices and properties
The πRT-calculus
Example
Conclusion
19. A calculus of mobile Real-Time processes
The πRT-calculus
πRT-calculus
P ::=
| 0 Inaction
| αi .P Action
| P1 + P2 Choice
| P1|P2 Parallel composition
| P
t
Q The timeout operator
| (νx).P Scoping
| [x = y]P Match
| A(x1, ..., xn) Agent identifier
| !P replication
Names : x , y , z . - The actions αi are : τ Internal , ¯xy Output a
name , x(y) Input a name ,¯x(y) is not defined ! ! !.
20. A calculus of mobile Real-Time processes
The πRT-calculus
Semantics
21. A calculus of mobile Real-Time processes
The πRT-calculus
Operational semantics
22. A calculus of mobile Real-Time processes
The πRT-calculus
Operational semantics
23. A calculus of mobile Real-Time processes
Example
Introduction
The π-calculus
General design choices and properties
The πRT-calculus
Example
Conclusion
24. A calculus of mobile Real-Time processes
Example
Network configuration for the mobile streaming video
player
25. A calculus of mobile Real-Time processes
Example
Ports of the video player
Player
def
= attach(t, r).Attached(t, r)
Attached(t, r)
def
= 0
t
Playing(r)
Playing(r)
def
= video.(0
1
.τplay.Playing(r)) + attach(t, r).rel.Attached(t, r)
26. A calculus of mobile Real-Time processes
Example
Ports of the routers
Router(t, rel)
def
= attach t, rel .rel.Router(t, rel)
RouterA
def
= Router(3, rel1)
RouterB
def
= Router(5, rel2)
27. A calculus of mobile Real-Time processes
Example
Port of the video server
Server
def
= video.Server
28. A calculus of mobile Real-Time processes
Example
The whole system is encoded from the user’s point of view and is
as follows :
System
def
= (νvideo)(Server|(νattach, rel1, rel2)
(Player|RouterA|RouterB))
.
29. A calculus of mobile Real-Time processes
Example
The whole system is encoded from the user’s point of view and is
as follows :
System
def
= (νvideo)(Server|(νattach, rel1, rel2)
(Player|RouterA|RouterB))
.
Suppose the player first attaches to router A. Router A transmits
the transmission delay from the server which is 3 time units plus a
release link to the player.
System
τ
→ (νvideo)(Server|(νattach, rel1, rel2)
(Attached(3, rel1)|rel1.Router(3, rel1)|RouterB))
.
30. A calculus of mobile Real-Time processes
Example
The whole system is encoded from the user’s point of view and is
as follows :
System
def
= (νvideo)(Server|(νattach, rel1, rel2)
(Player|RouterA|RouterB))
.
Suppose the player first attaches to router A. Router A transmits
the transmission delay from the server which is 3 time units plus a
release link to the player.
System
τ
→ (νvideo)(Server|(νattach, rel1, rel2)
(Attached(3, rel1)|rel1.Router(3, rel1)|RouterB))
.
System
(3)
→ System
.
31. A calculus of mobile Real-Time processes
Example
The whole system is encoded from the user’s point of view and is
as follows :
System
def
= (νvideo)(Server|(νattach, rel1, rel2)
(Player|RouterA|RouterB))
.
Suppose the player first attaches to router A. Router A transmits
the transmission delay from the server which is 3 time units plus a
release link to the player.
System
τ
→ (νvideo)(Server|(νattach, rel1, rel2)
(Attached(3, rel1)|rel1.Router(3, rel1)|RouterB))
.
System
(3)
→ System
.
where
System
def
= (νvideo)(Server|(νattach, rel1, rel2)
(Playing(rel1)|rel1.Router(3, rel1)|RouterB))
32. A calculus of mobile Real-Time processes
Example
After the transmission delay, the player gets the first video clip,
decodes and processes it for 1 time unit and then plays the clip to
the user.
System
τ
→ (νvideo)(Server|(νattach, rel1, rel2)
(0
1
.τplay.Playing(rel1))|
rel1.Router(3, rel1)|RouterB))
.
33. A calculus of mobile Real-Time processes
Example
After the transmission delay, the player gets the first video clip,
decodes and processes it for 1 time unit and then plays the clip to
the user.
System
τ
→ (νvideo)(Server|(νattach, rel1, rel2)
(0
1
.τplay.Playing(rel1))|
rel1.Router(3, rel1)|RouterB))
.
(1)
→ (νvideo)(Server|(νattach, rel1, rel2)
(τplay.Playing(rel1))|rel1.Router(3, rel1)|RouterB))
.
τ
→ (νvideo)(Server|(νattach, rel1, rel2)
(play.Playing(rel1))|rel1.Router(3, rel1)|RouterB))
play
→ System
34. A calculus of mobile Real-Time processes
Example
The player moves away from router A and attaches to router B.
Router B transmits the new transmission delay from the server
which is now 5 time units plus a release link to the player.
.
35. A calculus of mobile Real-Time processes
Example
The player moves away from router A and attaches to router B.
Router B transmits the new transmission delay from the server
which is now 5 time units plus a release link to the player.
.
System
τ
→ (νvideo)(Server|(νattach, rel1, rel2)
(rel1.Attached(5, rel2)|
rel1.Router(3, rel1)|rel2.Router(5, rel2)))
.
36. A calculus of mobile Real-Time processes
Example
The player moves away from router A and attaches to router B.
Router B transmits the new transmission delay from the server
which is now 5 time units plus a release link to the player.
.
System
τ
→ (νvideo)(Server|(νattach, rel1, rel2)
(rel1.Attached(5, rel2)|
rel1.Router(3, rel1)|rel2.Router(5, rel2)))
.
τ
→ (νvideo)(Server|(νattach, rel1, rel2)
(Attached(5, rel2)|
RouterA|rel2.Router(5, rel2)))
(5)
→ System
System
def
= (νvideo)(Server|(νattach, rel1, rel2)
(Playing(rel2)|RouterA|rel2.Router(5, rel2)))
37. A calculus of mobile Real-Time processes
Example
When the player moves away from router B and back to router A
again :
.
System
τ
→ (νvideo)(Server|(νattach, rel1, rel2)
(0
1
.τplay.Playing(rel2))|
RouterA|rel2.Router(5, rel2)))
.
38. A calculus of mobile Real-Time processes
Example
When the player moves away from router B and back to router A
again :
.
System
τ
→ (νvideo)(Server|(νattach, rel1, rel2)
(0
1
.τplay.Playing(rel2))|
RouterA|rel2.Router(5, rel2)))
.
(1)
→ (νvideo)(Server|(νattach, rel1, rel2)
(τplay.Playing(rel2))|RouterA|rel2.Router(5, rel2)))
.
39. A calculus of mobile Real-Time processes
Example
When the player moves away from router B and back to router A
again :
.
System
τ
→ (νvideo)(Server|(νattach, rel1, rel2)
(0
1
.τplay.Playing(rel2))|
RouterA|rel2.Router(5, rel2)))
.
(1)
→ (νvideo)(Server|(νattach, rel1, rel2)
(τplay.Playing(rel2))|RouterA|rel2.Router(5, rel2)))
.
τ
→ (νvideo)(Server|(νattach, rel1, rel2)
(play.Playing(rel2))|RouterA|rel2.Router(5, rel2)))
play
→ System
40. A calculus of mobile Real-Time processes
Example
After the transmission delay, the player can start receiving and
playing the stream :
.
41. A calculus of mobile Real-Time processes
Example
After the transmission delay, the player can start receiving and
playing the stream :
.
System
τ
→ (νvideo)(Server|(νattach, rel1, rel2)
(rel2.Attached(3, rel1)|
rel1.Router(3, rel1)|rel2.Router(5, rel2)))
.
42. A calculus of mobile Real-Time processes
Example
After the transmission delay, the player can start receiving and
playing the stream :
.
System
τ
→ (νvideo)(Server|(νattach, rel1, rel2)
(rel2.Attached(3, rel1)|
rel1.Router(3, rel1)|rel2.Router(5, rel2)))
.
τ
→ (νvideo)(Server|(νattach, rel1, rel2)
(Attached(3, rel1)|
rel1.Router(3, rel1)|RouterB))
(3)
→ System
43. A calculus of mobile Real-Time processes
Conclusion
Introduction
The π-calculus
General design choices and properties
The πRT-calculus
Example
Conclusion
44. A calculus of mobile Real-Time processes
Conclusion
Conclusion
The πRT-calculus is a temporal extension of a π-calculus by
introducing a timeout operator.
To continue :
Developing timed bisimulation equivalence relation
Extending the π-calculus with continuous time
Defin the broadcast commnication in the πRT-calculus .
This formalism :
is simple real-time process algebra
is not suitable for real-time programming
their mechanisms are too abstract
45. A calculus of mobile Real-Time processes
Conclusion
References
Robin Milner
A Calculus of Communicating Systems
Springer-Verlag, 1980
Davide Sangiorgi,David Walker
The Pi-Calculus : A Theory of Mobile Processes
Cambridge University Press, 2003
Xavier Nicollin, Joseph Sifakis
An Overview and Synthesis on Timed Process Algebras
Proceedings of CAV 91, LNCS 575, 1991.
Jeremy Y. Lee John Zic
On Modeling Real-time Mobile Processes
Australian Computer Science Communications Vol. 24 (2002)
