Talk given in September month in several conferences

1. How can we know when a system is
working well?
@markburgess_osl 2016

2. Fault-resilience as promise theory project
An attempt to understand and formalise
Very hard to write down true statements
about systems, and provide non-use-case
specific advice.
My attempt to make progress:
http://markburgess.org/Faults.pdf

3. Five key words
• Who are “we”? — it’s subjective
• What does “know” mean? — know it like a friend
• What is “the system”? — where does it start and end?
• What is “working”? — what’s the basis for judgement?
• What is “well”? — can we quantify it?
“How can we know when a system is working well?”

4. “System” - the modularity misconception
• In a “system”, there are freedoms and constraints in balance.
• Is it one thing, or many components working together?
• NB,this is a question of scale and perspective.
• Microservices?
• Causation becomes entangled - the modularity myth

5. Things to understand in systems
• What is intended / what is claimed (promised) - fit for purpose
• What is actual / what is measured (assessed) - are we succeeding?
• My goals might be different from yours (subjective)
• Only the origin agent is authoritative about its intent (autonomy)
• How do assessments change with scale and perspective (relativity)

6. Understanding “What” happens (2000-2003)
• On what scale?
• Space
• Time
• Assessments … uncertainty
• Non-deterministic
• Dynamics, but something is missing …

7. “Well” - dynamically and semantically suitable
• It meets “our” expectations
• Who is “us”?
• Expectations are based on
• Assumptions
• Whose assumptions?
• The origin agent is the authoritative source
• But the receiver is responsible

8. DESIGNING SYSTEMS FOR COOPERATION
MARK BURGESS
THINKING IN
PROMISES
“This is where a great quote goes. Excellent book!” –Joe Blough
“We” - the subjective (2004-2017)
• Fitness for purpose
• Desired outcome
• Forms the measuring stick
for correct outcome
• Define agent
• Define promise
• Define assessment

9. The basics of promise theory
• An agent can only make a promise about its own behaviours
• Obliging others is an ineffective strategy.
• An agent can only assess others’ promises from its own perspective
• Any reliance (dependency) on another agent invalidates a promise

10. “Working” - faults and resilience (2015-)
• To define a fault, error, flaw, you
have to know what was intended
• But at what scale?
• What measuring stick?
• Agents and promises
• What are the consequences of
promises not kept?

11. Agent fidelity: practical definitions

12. What helps/prevents agents from keeping promises?

13. Can a system keep all its promises and still be
unpredictable?

14. “Know” system = continuous assessment (1999-2000)

15. Relationships are characterized by distributions (1999)

16. What is the promise here?

17. Cognitive computing - realtime machine learning

18. Knowledge management and scaling - (2007-2010)

19. The knowledge ladder transforms: time —> space
experiment - customize - productisation - commoditisation - utilities

20. Can we still still keep our promises if we scale the
system?
• Change of spacetime scale
• Space/bigger
• Time/faster
• Scaling is more than “make it bigger”
• Dynamics and semantics
• Do we trade functionality for size?

21. How do we know a system is working well?
• Know - a relationship between the system curator and its agents
• System - a collection of agents collaborating by promises
• Working - Promises made and kept
• Well - what stats about promise keeping?
If we can’t formalise these, we can’t answer this question

22. @markburgess_osl
DESIGNING SYSTEMS FOR COOPERATION
MARK BURGESS
THINKING IN
PROMISES
“This is where a great quote goes. Excellent book!” –Joe Blough
http://markburgess.org/Faults.pdf

23. Extras

24. Does it scale? — single agents and queues
• Wind-tunnel (dynamics)
• Dimensionless ratios - universal scaling
• 1 dimensional bias: queues

25. Scaling of agency - semantic spacetime (2014)

26. Modularity is not a scalable concept

27. Scaling to optimize…what?
• Microservices / modularity - optimizes human knowledge
• Monolith - optimizes localization
• Continuous delivery - optimises convergence
• What costs the most?
• What optimizes certainty?

28. Repair versus redundancy
Continuous delivery vs fault avoidance
• Rapid repair cycle is the best strategy for temporal continuity
• Repair does not change the bulk scaling assumptions - using
time agility instead of bulk for resilience (space —> time)
• Local time trumps space, because space is non-local time (!)