DCV: A Causality Detection Approach for

Large-scale Dynamic Collaboration

Environments

Jiang-Ming YangMicrosoft Research Asia

Ning Gu, Qi-Wei Zhang, Jiang-Ming Yang and Wei Ye

Fudan University

Agenda

Introduction Direct Causal Vector Timestamp (DCV) Causality Detection Algorithms Discussion Future Work

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Agenda

Introduction Direct Causal Vector Timestamp (DCV) Causality Detection Algorithms Discussion Future Work

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Group Editors

Enable a group of users to view and edit a same document simultaneously from geographically dispersed sites connected by communication networks

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Wiki

A wiki is such a website — every visitor is allowed and able to add new pages to it, remove existing pages, or otherwise edit and change the content of existing pages

Wiki is becoming more and more popular, as a novel and convenient collaboration medium

Samples: Wikipedia, WikiNews, WikiTravel, etc

Adapting existing single-user wiki page editors to full-replicated group editors.

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Realtime group editing in Wiki: The Problems The collaborative environments in wikis are typically

large-scale and dynamic collaboration environments.

Large number of participants

Highly dynamic

Unreliable

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Existing solutions : Vector timestamp

Traditional vector logical clock timestamp

Each item corresponds to a collaboration participant, and records the number of operations generated by that participant that are causally preceding O.

causal relationship between any two operations can be easily determined

Size of vector logical clock linearly depends on the number of participants

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Existing solutions : Dynamic timestamp

Using associative vectors indexed by participant identifier, thus allowing the system to dynamically add new timestamp items or discard old timestamp items during the collaboration session.

Creating vector items just for those participants who have written.

if some participants midway leave the collaboration session, watching their corresponding timestamp items and removing them once they have become insignificant.

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Existing solutions : Vector compression

Related WorksSun-Cai approach, NICE approach

Single-point failure. Have an adverse impact on conflict handle.

SOCK4, TIBOT Cooperating sites must be well-connected. The communication channels

among them must be stable and reliable. Also have adverse impacts on conflict handle.

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Agenda

Introduction Direct Causal Vector Timestamp (DCV) Causality Detection Algorithms Discussion Future Work

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Causal Vector

Causal Relation (->)Given two operations Oa and Ob, Oa->Ob iff:

Oa and Ob are generated at a same site, and the generation of Oa happened before Ob

Oa and Ob are generated at different sites i and j, and the execution of Oa at site j happened before the generation of Ob

There exists an operation Ox, such that Oa->Ox and Ox->Ob

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Causality Preservation

o11, o21 and o31 are concurrent with each other.

o12 is causally dependent on three operations o11, o21 and o31.

o22 is causally dependent on four operations o11, o21, o31 and o12.

o32 is only causally dependent on o31

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Direct Causal Relation

Direct Causal Relation ()Given two operations Oa and Ob, Oa Ob iff:

Oa -> Ob And there exists no operation Ox satisfying Oa->Ox and Ox->Ob

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Causality Preservation

o12 is direct causally dependent on three operations o11, o21 and o31.

o22 is direct causally dependent on o12.

o32 is direct causally dependent on o31

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Direct Causal Vector(DCV)

Direct Causal Vector (DCV)Given an operation O ， {O1, O2, …, Ok}O ， O1(s1, n1), …,

Ok(sk, nk), DCV(O)=[(s1, n1), (s2, n2), …, (sk, nk)]

The direct causal vector of o11, o21, o31, o12, o22 and o32 is [ ], [ ], [ ], [(1, 1), (2, 1), (3, 1)], [(1, 2)] and [(1, 3)] respectively.

Given two operations, their direct causal relationship can be determined directly from their direct causal vectorsGive two operations Oa and Ob, Oa Ob iff there exists an

item for Oa in DCV(Ob)

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Agenda

Introduction Direct Causal Vector Timestamp (DCV) Causality Detection Algorithms Discussion Future Work

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Agenda

Introduction Direct Causal Vector Timestamp (DCV) Causality Detection Algorithms

Causality PreservationConcurrent SeparationCausality Cache

Discussion Future Work

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Agenda

Introduction Direct Causal Vector Timestamp (DCV) Causality Detection Algorithms

Causality PreservationConcurrent SeparationCausality Cache

Discussion Future Work

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Causality Preservation

Problem Description:Given an unexecuted remote operation, how to determine

whether all the operations causally preceding it have already been executed?

Solution:Given a remote operation O, suppose all the operations

executed before respect their causal order. O is causally ready iff all the operations causally precede o directly have already been executed.

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Agenda

Introduction Direct Causal Vector Timestamp (DCV) Causality Detection Algorithms

Causality PreservationConcurrent SeparationCausality Cache

Discussion Future Work

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Concurrent Separation

Problem Description:Given an unexecuted remote operation Or that is causally ready,

how to separate all the history operations that are concurrent to it from the operation history?

Solution:Suppose there are two operation Oa and Ob in history, Oa->Ob, it

will not check Oa until Ob is proved to be concurrent with Or

do not check a history operation until all the history operations that are causally dependent on it have been proved to be concurrent with Or

When checking a operation Oh, Oh is concurrent to Or iff not OhOr

There is no operation Ox, which satisfy Oh->Ox->Or

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Concurrent Separation

Suppose a remote operation O41 is received at site 3 shortly after the execution of o22 on that site. DCV(O41)= [(1, 1), (3,

2)] .O32 and O22 is checked first,

because there is no other history operation causally depends on them. O22 is proved to be concurrent with O41.

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Concurrent Separation

Suppose a remote operation O41 is received at site 3 shortly after the execution of o22 on that site. DCV(O41)= [(1, 1), (3,

2)] .O32 and O22 is checked first,

because there is no other history operation causally depends on them. O22 is proved to be concurrent with O41.

O12 is checked in the second round, it is proved to be concurrent with O41.

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Concurrent Separation

Suppose a remote operation O41 is received at site 3 shortly after the execution of o22 on that site. DCV(O41)= [(1, 1), (3, 2)] .

O32 and O22 is checked first, because there is no other history operation causally depends on them. O22 is proved to be concurrent with O41.

O12 is checked in the second round, it is proved to be concurrent with O41.

Now, O11 and O21 is ready to be checked, and O21 can be determined concurrent with O41

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Concurrent Separation

Suppose a remote operation O41 is received at site 3 shortly after the execution of o22 on that site. DCV(O41)= [(1, 1), (3,

2)] .

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Agenda

Introduction Direct Causal Vector Timestamp (DCV) Causality Detection Algorithms

Causality PreservationConcurrent SeparationCausality Cache

Discussion Future Work

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Causality Cache

Problem Description:How to determine the causal relationship between two arbitrary

operations in the operation history? Solution:

Every time before executing a remote operation Or, all the earlier executed operations that are concurrent with it are separated out in set SCr in advance. Cache this result for future use.

If there are more than one operations in SCr that are generated at a same cooperating site, just keep the earliest generated one.

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Causality Cache

Suppose a remote operation O41 is received at site 3 shortly after the execution of o22 on that site. DCV(O41)= [(1, 1), (3,

2)] .There are three operations

concurrent with O41

O12, O21, O22

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Causality Cache

Suppose a remote operation O41 is received at site 3 shortly after the execution of o22 on that site. DCV(O41)= [(1, 1), (3,

2)] .Two Groups based on their site

Site-1 : O12

Site-2 : O21, O22

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Causality Cache

Suppose a remote operation O41 is received at site 3 shortly after the execution of o22 on that site. DCV(O41)= [(1, 1), (3, 2)] .

Catch={O12, O21}

Site-1 : O12

Site-2 : O21, O22

O21∊ Catch

=> O21||O41

=> O22||O41

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Agenda

Introduction Direct Causal Vector Timestamp (DCV) Causality Detection Algorithms Discussion Future Work

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Disscussion

After a person leave the collaboration session, the size of direct causal vector timestamp of later generated operations will automatically shrink.

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Disscussion

Feature of DCV approachThe size of direct causal vector timestamp of an operation O

approximates the number of participants active at editing the shared object recently before the generation of operation O. It is not pre-allocated for each potential participants an item in the direct

causal vector timestamp. After a person leave the collaboration session, the size of direct causal

vector timestamp of later generated operations will automatically shrink.

The time complexity, storage complexity of our algorithms also just linearly depends on the number of collaboration participants that are currently active at editing.

DCV approach is much more scalable. It is not rely on a stable network. And it has no constraint on users’ collaboration mode.

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Agenda

Introduction Direct Causal Vector Timestamp (DCV) Causality Detection Algorithms Discussion Future Work

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Future Work

Compression of Direct Causal Vector in highly active large-scale collaboration environments

Experiments of the effects ……

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Thanks!