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Providing High Availability Using Lazy Replication. Rivaka Ladin, Barbara Liskov, Liuba Shrira, Sanjay Ghemawat Presented by Huang-Ming Huang. Outline. Model Algorithm Performance Analysis Discussion. Replication Model. RM. client. FE. RM. Service. Front ends. RM. client. FE. - PowerPoint PPT Presentation
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Providing High Availability Using Lazy Replication
Rivaka Ladin, Barbara Liskov, Liuba Shrira, Sanjay Ghemawat
Presented 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 Classification
RM
RMRM
FE FE
Client Client
query
valupdate
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
ValueTS
Value
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 ta
ble 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 g
ossip.
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 g
row. can be increased by making most of the replicas
read-only
Qustions?