1. Reinhard Schwarz and Friedmann Mattern
Detecting Causal Relationships in Distributed Computations: In Search of the Holy Grail
Reinhard Schwarz and Friedmann Mattern

2. Motivation
To fully understand a distributed system, understanding causal and temporal relations between events is important
Lack of adequate tools for the design and analysis of distributed systems
Distributed systems are almost ubiquitous so there is an urgent demand for such tools

3. Challenges
Traditional models based on the notion of global time are insufficient
There exists no well-established, agreed-upon formalism for reasoning about causality
System models in literature often lack conciseness and differ substantially
Inherent complexity of causality structure

4. Common Misconceptions
They don’t take into account that different observers typically observe different global states
States and state transitions, or atomic and non-atomic events are often confused

5. System Model

6. Vector Time

7. Vector Time
By using vector time, it is possible to faithfully represent causality.
Although several refinements to the basic vector time approach are feasible, timestamps characterizing causality seem intrinsically complex.

8. Lamport Time

9. Dependency vectors

10. Concurrency Maps

11. Observations of Global State

12. Conclusions
their discussion shed light on the main problems and fundamental limitations arising in the area of research
none of the presented schemes is sufficiently mature to serve as a general-purpose mechanism for the analysis of causality
distributed computations are intrinsically difficult to understand
perhaps a simple way to describe their behavior does not even exist.