Providing High Availability Using Lazy Replication Rivaka Ladin, Barbara Liskov, Liuba Shrira,...
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Transcript of Providing High Availability Using Lazy Replication Rivaka Ladin, Barbara Liskov, Liuba Shrira,...
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?