1 Applications ? Trading Consistency for Performance Applications - - PDF document

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Continuous Consistency Continuous Consistency and Availability and Availability

Haifeng Yu

CPS 212 Fall 2002

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Consistency in Replication Consistency in Replication

! Replication comes with consistency cost: ! Reasons for replication: Better performance and availability

client client client server server server

! Replication transforms client-server

communication to server-server communication:

• Decrease performance
• Decrease availability

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Strong Consistency and Optimistic Consistency Strong Consistency and Optimistic Consistency

! Traditionally, two choices for consistency level:

• Strong consistency: Strictly “in sync”
• Optimistic consistency: No guarantee at all
• Associated tradeoffs with each model

Availability / Performance / Scalability Consistency Optimistic Consistency Strong Consistency

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Problems with Binary Choice Problems with Binary Choice

!

Strong consistency incurs prohibitive overheads for many WAN apps

• Replication may even decrease performance, availability and scalability

relative to a single server!

!

Optimistic consistency provides no consistency guarantee at all

• Resulting in upset users: Unbounded reservation conflicts
• Potentially render the app unusable: If traffic data is more than 1 hour stale,

probably of little use

!

Applications cannot tune consistency level based on its environment

• Need to adapt to client, service and network characteristics

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Continuous Consistency Continuous Consistency

! Consistency is continuous rather than binary for many

WAN apps

• These apps can benefit from exploiting the consistency

spectrum between strong and optimistic consistency.

Availability / Performance / Scalability Consistency Optimistic Consistency Strong Consistency Consistency Continuous Consistency Availability / Performance / Scalability

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Quantifying Consistency Quantifying Consistency

! Many ways:

• Staleness (TTL in web caching): Invalidate
• Limit number of locally buffered writes

buffered updates

To Other Replicas

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Applications ? Applications ?

! Applications:

• Web caching
• Airline reservation
• Distributed games
• Shared editor

! Non-Applications:

• Some scientific computing problems
• Banking system
• Any application that has binary output

! Application’s nature determines whether continuous consistency

is applicable

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Trading Consistency for Performance Trading Consistency for Performance

! Airline reservation: running at Berkeley, Utah, Duke 10 20 30 40 50 0% 50% 100% Inconsistency Throughput (updates/sec)

Strong Consistency Optimistic Consistency [Yu’02, TOCS]

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The Cost of Increased Performance The Cost of Increased Performance

! Increased performance comes with a cost

• Adaptively trade consistency for performance based on client,

network, and service conditions

0% 5% 10% 15% 20% 25% 0% 20% 40% 60% 80% 100% Inconsistency

• Resv. Conflict Rate

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Model vs. Protocol Model vs. Protocol

! Continuous consistency model is a spec. ! Protocol is anything that can enforce the spec.

• Corollary: Strong consistency protocol is a protocol for any model

! Many protocols for a specific model, some are good, others are

not

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Designing a Continuous Consistency Model Designing a Continuous Consistency Model

! Model is a spec, thus quantifying consistency (in a bad way) is

trivial

! Only applications know its definition of consistency

• Airline reservation vs. distributed games

! What is a “good” continuous consistency model?

• Can be used by diverse apps
• Practical

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Distributed Consensus and Leader Election Distributed Consensus and Leader Election

! What does “continuous consistency” mean ?

• Allow at most k decision values
• Allow at most k leaders

! Helps overcome some impossibilities

• Unique decision value requires ½ majority
• K decision values allow any partition with 1/(k + 1) nodes to decide

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Group Membership Service Group Membership Service

! Def: Keep track of which nodes belong to which group ! Traditionally, group membership only maintain a single group

• Primary-partition membership services
• Corresponds to strong consistency

! Recently, partitionable membership services

• Still active area of research
• Corresponds to optimistic consistency

! Continuous consistency:

• Allow at most k groups
• Again, helps overcome the ½ majority limitation

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Continuous Consistency Summary Continuous Consistency Summary

! WAN replication needs dynamically tunable consistency ! Tradeoff between consistency and performance ! How to design a continuous consistency model ! Continuous consistency in other context ! Next: Availability 15

What is Availability ? What is Availability ?

! No well-accepted availability metric for Internet services ! “Uptime” metric can be misleading for Internet services

• Server may be inaccessible because of network partition

! Available: “present or ready for immediate use”

• From Webster’s Collegiate Dictionary
• What does “immediate” mean?
• Time-out

! Availability = (accepted accesses) / (submitted accesses)

• Implicit time-out in the definition

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Perform Perform-

• ability

ability

! User satisfaction is not binary

• What if a partial result is returned before time-out ?
• What if the result is sent back after an hour, or a day ?
• Availability is related to performance

! Performability = reward function (quality and timeliness of

result)

! Determining reward function is hard ! 17

Availability of an Internet Service Availability of an Internet Service

! We use user-observed availability in our study:

Availability = (accepted accesses) / (submitted accesses)

Server client

×

2% [Chandra et.al., USITS’01]

reject due to server failure

×

0.1% [MS press release,Jan’01]

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Effects of Replication Effects of Replication

! Consistency may force a replica to reject an otherwise

acceptable request

• Network Failure Rate Replica Rejection Rate

client

×

< 2%

× reject

Replica Replica reject

×

communication to maintain consistency failed > 0.1%

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Limitations of Strong Consistency Limitations of Strong Consistency

: Replicas : Clients Option 1: accept reads accept reads reject writes reject writes Option 2: accept reads reject reads accept writes reject writes

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Effects of Continuous Consistency Effects of Continuous Consistency

Option 1: accept reads accept reads reject writes reject writes New Option 1: accept reads accept reads accept first 10 writes accept first 5 writes

allow replica to buffer 5 writes

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Effects of Continuous Consistency Effects of Continuous Consistency

Option 2: accept reads reject reads accept writes reject writes New Option 2: accept reads accept first few reads accept writes accept first 5 writes

allow replica to buffer 5 writes

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Consistency Impact is Consistency Impact is Inherent Inherent

Availability Inconsistency Hard Bound

0% Consistency 100% Availability 100% Consistency

! Hard bound always exist ! We always know the to end points, but may not know the exact

shape of the curve

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Effects of Consistency Protocol Effects of Consistency Protocol

! Achieved availability also depends on protocol

• Design better protocols
• Job of system designers

Availability Inconsistency Upper Bound Protocol A Protocol B

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Availability Optimizations Availability Optimizations

! Technique should not be tied to model ! Focus on two techniques:

• Retiring replicas
• Aggressive write propagation

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Limitations of Strong Consistency Limitations of Strong Consistency

: Replicas : Clients Option 1: accept reads accept reads reject writes reject writes Option 2: accept reads reject reads accept writes reject writes

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Retiring Replicas Retiring Replicas

! Obviously, such decision may not be optimal unless we have

future knowledge

• Importance of prediction

! Even with future knowledge, it is hard ! In option 2, all replicas much reach an agreement

• Leader election
• We are experiencing partitions
• One option: Voting
• What if we don’t have majority?

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Aggressive Write Propagation Aggressive Write Propagation

! Applicable to continuous consistency ! Continuous consistency gives us “buffers” that can be utilized in

case of network partition

! Keep the buffer empty:

• Cannot predict the occurrence of network partitions
• Propagate writes more aggressively
• Cut down the amount of inconsistency accumulated in times of

good connectivity

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Effects of Aggressive Propagation Effects of Aggressive Propagation

!

Baseline: Propagate writes only when necessary (lazily)

!

Aggressive: When necessary and every 3 seconds 0.993 0.994 0.995 0.996 0.997 0.998 Inconsistency Availability Avail Upper Bound Aggressive Baseline 8 replicas with measured faultload From [Yu’01, SOSP]

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More Aggressive Propagation More Aggressive Propagation

! Aggressive write propagation does not work in all cases ! Availability optimizations can incur more communication

• Best availability achieved when we use a strong consistency

protocol

! Speaks of availability / performance tradeoffs 30

Availability of Other Systems Availability of Other Systems

! Consensus and leader election

• Blocks without majority

! Group membership

• Blocks without majority

! Relaxing consistency enables them to make progress

• Open Question: But will these systems still be useful ?

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Availability Summary Availability Summary

! Availability definition ! Inherent impact of consistency on availability ! Availability also depends on consistency protocols ! Availability optimizations:

• Replica retirement
• Aggressive write propagation

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Why can we easily approach the upper bound? Why can we easily approach the upper bound?

! Simple protocols in our study can approach the upper bound

closely

• Remember reaching the upper bound in general needs future

knowledge

! Related to the characteristics of the faultloads we measured and

simulated

• Most partitions are singleton partitions
• Most transitions are:

fully-connected → singleton partition → fully-connected

! These characteristics are consistent with

• Internet hierarchical architecture

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Dual Effects of Replication Scale on Dual Effects of Replication Scale on Availability Availability

! Consistency may force a replica to reject a request ! Adding more replicas:

• Network Failure Rate Replica Rejection Rate

! Availability = (1 - Network Failure Rate) * ( 1 - Rejection Rate)

• Too large or too small replication scale can hurt availability

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Optimal Replication Scale Optimal Replication Scale

! Optimal replication scale: Adding more replicas can hurt!

• Increase in “replica rejection rate” outweighs decrease in “network

failure rate”

! Optimal replication scale depends on

• Consistency level
• Network failure rate among replicas

0.984 0.986 0.988 0.99 0.992 0.994 0.996 0.998 1

1 2 3 4 5 6 7 Number of Replicas Availability Upper Bound Failure Rate = 1% , Numerical Error = 250 Failure Rate = 1% , Numerical Error = 0 Failure Rate = 5% , Numerical Error = 250 Failure Rate = 5% , Numerical Error = 0