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

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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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Network Usage for MapReduce Jobs

Hadoop Sort, 4GB per VM

Hadoop Word Count, 2GB per VM

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Network Usage for MapReduce Jobs

Hadoop Sort, 4GB per VM

Hadoop Word Count, 2GB per VM

Hive Join, 6GB per VM

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Network Usage for MapReduce Jobs

Hadoop Sort, 4GB per VM

Hadoop Word Count, 2GB per VM

Hive Join, 6GB per VM

Hive Aggregation, 2GB per VM10

Network Usage for MapReduce Jobs

Hadoop Sort, 4GB per VM

Hadoop Word Count, 2GB per VM

Hive Join, 6GB per VM

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

Virtual Cluster Model

Job1 Time

0 5 10 15 20 25 30

500

Bandwidth

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16

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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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. . .

Virtual Cluster Model

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?

Challenges in Realizing TIVC

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Q3: How to efficiently allocate TIVC?

Q4: How to enforce TIVC?

Challenges in Realizing 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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Challenges in Realizing 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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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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Challenges in Realizing 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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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

Challenges in Realizing 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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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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Challenges in Realizing 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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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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Challenges in Realizing 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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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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