Providing High Availability Using Lazy Replication

Rivaka Ladin, Barbara Liskov, Liuba Shrira, Sanjay GhemawatPresented by Huang-Ming Huang

Outline Model Algorithm Performance Analysis Discussion

Replication Model

client

client

RM

RM

RM

FE

FE

Service

ReplicationManager

Front ends

Excerpt from “Distributed Systems – Concept and Design” by Coulouris, Dollimore and Kindberg

System Guarantees Each client obtains a consistent

service over time Relaxed consistency between

replicas Updates are applied with ordering

guarantees that make the replicas sufficiently similar.

Operation ClassificationRM

RMRM

FE FE

Client Client

query

val update

Query, prev Val, newUpdate, prev Update id

gossip

Excerpt from “Distributed Systems – Concept and Design” by Coulouris, Dollimore and Kindberg

Update operation classification Causal update Forced update : performed in the

same order (relative to one another) at all replicas.

Immediate update : performed at all replicas in the same order relative to all other operations.

Vector timestamp Given two timestamps

T = (t1,t2,,tn) S = (s1,s2,,sn) T ≤ S ≡ti≤si for all i merge(T,S)= (max(t1,s1),…,max(tn,sn))

Each part of the vector timestamp corresponds to each replica manager in the system.

RM components

Replica timestamp

Update log

Value Timestamp

Value

Timestamp table

Executed operation table

FE

FE

Other replicas

GossipMessages

Updates Operatio

n prev id

Replica Timestamp

Replica log

stable

updates

Excerpt from “Distributed Systems – Concept and Design” by Coulouris, Dollimore and Kindberg

Query The replica manager blocks the query q operation until the condition holds:

q.prev <= valueTS The replica manger returns valueTS back to FE. FE updates its own timestamp

frontEndTS := merge(frontEndTS, new)

(r1,r2,…,ri+1,…,rn)

Causal Update

(r1,r2,…,ri,…,rn)Update log

FE

ValueTSValue

Executed operation table

(p1,p2,…pn,)operation

id

ts=(p1,p2,…,pi+1,…,pn)

logRecord =(i, ts, u.op, u.prev, u.id)

ts

r.u.prev ≤ valueTS

merge(ValueTS, r.ts)apply(value.r.u.op)

executed r.u.id

Replication Manager i

Gossip messages Goal : bring the states of replication

managers up to date. Consists of :

Replication timestamp Update log

Upon receiving gossip Merge the arriving log with its own Apply any unexecuted stable updates Eliminate redundant log and executed

operation table entries

Control the size of update log Timestamp table

keeps recent timestamps from messages sent by all other replicas.

A log record r can be removed from the log when r.tsr.i < timestamp_table[j] r.i , for all j

Control the size of executed operation table Each update carries an extra time field FE returns an ACK

Contains FE’s clock time after receiving the response for an update from RM. RM inserts the received ACK to the log.

Control the size of executed operation table (con’t) A message m from FE is late if

m.time + δ< replica’s clock time An update is discard if it is late An ACK is kept at least until it is late Remove an entry c in executed operation table when

an ACK for c’s update is received all records for c’s update have been discarded.

Forced Update Use the primary to assign a global

unique identifier. The primary carries out a two

phase protocol for updates.

Two phase protocol Upon receiving an update, the

primary sends it to all other replicas.

Upon receiving responses from all most half of the backups, the primary commit the update by

insert the record to its log. Backups know the commitment

from gossip messages.

Fail Recovery New coordinator informs

participants about the failure. Participants inform coordinator

about most recent forced updates Coordinator assign UID with the

largest it knows after the sub-majority of replicas has responded.

Immediate Update Primary use 3 phase protocol.

Pre-prepare Prepare Commit

3 phase protocol

FEUpdate log

primary

backupbackup backup

update

Give me your log and

timestamp

logRecordUpdate id

Number of Messages for different operations Query : 2 Casual : 2 + (N-1)/K Forced : 2N/2+ (N-1)/K Immediate : 2N +2(N/2-1)+(N-1)K

N : the number of replicas K : the number of update/ack pairs in a gossip.

Capacity of a 3-replica system

Excerpt from “Providing high Availability Using Lazy Replication” by Ladin, Liskov, Shrira and Ghemawat

Capacity of the Unreplicated System

Excerpt from “Providing high Availability Using Lazy Replication” by Ladin, Liskov, Shrira and Ghemawat

Discussion No time guarantee for gossip messages

Not generally suitable for real-time application such as realtime conference updating shared document.

Scalability Timestamp space grows as number of replicas grow. can be increased by making most of the replicas read-only

Qustions?