The Only Constant is Change: Incorporating Time-varying ...€¦ · The Only Constant is Change:...

The Only Constant is Change: Incorporating Time-Varying Bandwidth

Reservations in Data Centers

Di Xie, Ning Ding, Y. Charlie Hu, Ramana Kompella

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Cloud Computing is Hot

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Private Cluster

Key Factors for Cloud Viability

• Cost

• Performance

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Performance Variability in Cloud

• BW variation in cloud due to contention [Schad’10 VLDB]

• Causing unpredictable performance

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0

100

200

300

400

500

600

700

800

900

1000

Local Cluster Amazon EC2

Bandwidth (Mbps)

Reserving BW in Data Centers

• SecondNet [Guo’10]

– Per VM-pair, per VM access bandwidth reservation

• Oktopus [Ballani’11]

– Virtual Cluster (VC)

– Virtual Oversubscribed Cluster (VOC)

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How BW Reservation Works

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. . .

Virtual Cluster Model

Time

Bandwidth

N VMs

VirtualSwitch

1. Determine the model 2. Allocate and enforce the model

0 T

B

Only fixed-BW reservationRequest <N, B>

Network Usage for MapReduce Jobs

Hadoop Sort, 4GB per VM

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Hadoop Word Count, 2GB per VM

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Hive Join, 6GB per VM

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Hive Aggregation, 2GB per VM10





Hive Aggregation, 2GB per VM11

Time-varying network usage

Motivating Example

• 4 machines,

2 VMs/machine,

non-oversubscribed

network

• Hadoop Sort– N: 4 VMs

– B: 500Mbps/VM

1Gbps

500Mbps50

0M

bp

sNot enough BW

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Motivating Example

• 4 machines,

2 VMs/machine,

non-oversubscribed

network

• Hadoop Sort– N: 4 VMs

– B: 500Mbps/VM

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1Gbps

500Mbps

Under Fixed-BW Reservation Model

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1Gbps

500MbpsJob3Job2


Job1 Time

0 5 10 15 20 25 30

500

Bandwidth

Temporally-Interleaved Virtual Cluster (TIVC)

• Key idea: Time-Varying BW Reservations

• Compared to fixed-BW reservation– Improves utilization of data center

• Better network utilization

• Better VM utilization

– Increases cloud provider’s revenue

– Reduces cloud user’s cost

– Without sacrificing job performance

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Challenges in Realizing TIVC

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. . .


Time

Bandwidth

N VMs

VirtualSwitch 0 T

B

Request <N, B>

Time

Bandwidth

0 T

B

Request <N, B(t)>

Q1: What are right model functions?

Q2: How to automatically derive the models?


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Q3: How to efficiently allocate TIVC?

Q4: How to enforce TIVC?


• What are the right model functions?

• How to automatically derive the models?

• How to efficiently allocate TIVC?

• How to enforce TIVC?

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• What are the right model functions?




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How to Model Time-Varying BW?

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Hadoop Hive Join

TIVC Models

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Virtual Cluster

Ban

dw

idth

Time

B

0 T1 T2 T

Bb

Ban

dw

idth

Time T31

B

0

Bb

T11 T12 T21 T22 T32 T

Ban

dw

idth

Time T31

B

0

Bb

T11 T12 T21 T22 T32 T T11T32

Hadoop Sort

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Hadoop Word Count

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v

Hadoop Hive Join

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Hadoop Hive Aggregation

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What are the right model functions?




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Possible Approach

• “White-box” approach– Given source code and data of cloud application,

analyze quantitative networking requirement

– Very difficult in practice

• Observation: Many jobs are repeated many times– E.g., 40% jobs are recurring in Bing’s production data

center [Agarwal’12]

– Of course, data itself may change across runs, but size remains about the same

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Our Approach

• Solution: “Black-box” profiling based approach

1. Collect traffic trace from profiling run

2. Derive TIVC model from traffic trace

• Profiling: Same configuration as production runs

– Same number of VMs

– Same input data size per VM

– Same job/VM configuration

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How much BW should we give to the application?

Impact of BW Capping

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Impact of BW Capping

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No-elongation BW threshold

Choosing BW Cap

• Tradeoff between performance and cost

– Cap > threshold: same performance, costs more

– Cap < threshold: lower performance, may cost less

• Our Approach: Expose tradeoff to user

1. Profile under different BW caps

2. Expose run times and cost to user

3. User picks the appropriate BW cap

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Only below threshold ones

From Profiling to Model Generation

• Collect traffic trace from each VM– Instantaneous throughput of 10ms bin

• Generate models for individual VMs

• Combine to obtain overall job’s TIVC model– Simplify allocation by working with one model

– Does not lose efficiency since per-VM models are roughly similar for MapReduce-like applications

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Generate Model for Individual VM

1. Choose Bb

2. Periods where B > Bb, set to Bcap

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BW

Time

Bcap

Bb

Maximal Efficiency Model

•

• Enumerate Bb to find the maximal efficiency model

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Volume Bandwdith Reserved

Volume Traffic nApplicatioEfficiency

BW

Time

Bcap

Bb



How to automatically derive the models?



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TIVC Allocation Algorithm

• Spatio-temporal allocation algorithm– Extends VC allocation algorithm to time dimension

– Employs dynamic programming

• Properties– Locality aware

– Efficient and scalable• 99th percentile 28ms on a 64,000-VM data center in

scheduling 5,000 jobs

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How to efficiently allocate TIVC?


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Enforcing TIVC Reservation

• Possible to enforce completely in hypervisor– Does not have control over upper level links

– Requires online rate monitoring and feedback

– Increases hypervisor overhead and complexity

• Observation: Few jobs share a link simultaneously– Most small jobs will fit into a rack

– Only a few large jobs cross the core

– In our simulations, < 26 jobs share a link in 64,000-VM data center

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Enforcing TIVC Reservation

• Enforcing BW reservation in switches

– Avoid complexity in hypervisors

– Can be implemented on commodity switches

• Cisco Nexus 7000 supports 16k policers

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How to efficiently allocate TIVC?

How to enforce TIVC?

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Proteus: Implementing TIVC Models

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1. Determine the model

2. Allocate and enforce the model

Evaluation

• Large-scale simulation

– Performance

– Cost

– Allocation algorithm

• Prototype implementation

– Small-scale testbed

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Simulation Setup

• 3-level tree topology– 16,000 Hosts x 4 VMs

– 4:1 oversubscription

• Workload– N: exponential distribution around mean 49

– B(t): derive from real Hadoop apps

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50Gbps

10Gbps

…

… …1Gbps

…

20 Aggr Switch

20 ToR Switch

40 Hosts

… … …

Batched Jobs

• Scenario: 5,000 time-insensitive jobs

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42% 21% 23% 35%

1/3 of each type

Completion time reduction

All rest results are for mixed

Varying Oversubscription and Job Size

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25.8% reduction for non-oversubscribed

network

Dynamically Arriving Jobs

• Scenario: Accommodate users’ requests in shared data center

– 5,000 jobs, Poisson arrival, varying load

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Rejected: VC: 9.5%

TIVC: 3.4%

Analysis: Higher Concurrency

• Under 80% load

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7% higher job concurrency

28% higher VM utilization

Rejected jobs are large

28% higher revenue

Charge VMs

VM

Tenant Cost and Provider Revenue

• Charging model

– VM time T and reserved BW volume B

– Cost = N (kv T + kb B)

– kv = 0.004$/hr, kb = 0.00016$/GB

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12% less cost for tenants

Providers make more money

Amazon target utilization

Testbed Experiment

• Setup– 18 machines

– Tc and NetFPGA rate limiter

• Real MapReduce jobs

• Procedure– Offline profiling

– Online reservation

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Testbed Result

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TIVC finishes job faster than VC,Baseline finishes the fastest

Baseline suffers elongation, TIVC achieves similar performance as VC

Conclusion

• Network reservations in cloud are important– Previous work proposed fixed-BW reservations– However, cloud apps exhibit time-varying BW usage

• We propose TIVC abstraction – Provides time-varying network reservations– Uses simple pulse functions– Automatically generates model– Efficiently allocates and enforces reservations

• Proteus shows TIVC benefits both cloud provider and users significantly

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