Applying Benchmark Data To A Model for Relative Server Capacity

CMG 2013

Joseph Temple, LC-NS Consulting

John J Thomas, IBM

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Relative Server Capacity

“How do I compare machine capacity?” “What platform is best fit to deliver a given workload?”

Simple enough questions, but difficult to answer!

Establishing server capacity is complex Different platform design points Different machine architectures Continuously evolving platform generations

“Standard” benchmarks (SPECInt, TPC-C etc.) and composite metrics (RPE2, QPI etc.) help, but may not be sufficient

Some platforms do not support these metrics May not be sufficient to decide best fit for a given workload

We need a model to address Relative Server Capacity See “Alternative Metrics for Server RFPs” [J. Temple]

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Local Factors / Constraints

Non-FunctionalRequirements

TechnologyAdoption

StrategicDirection

CostModels

ReferenceArchitectures

Systemz

Systemx

Power WorkloadFit

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Fit for Purpose Workload Types

Mixed Workload – Type 1• Scales up• Updates to shared data

and work queues• Complex virtualization• Business Intelligence with

heavy data sharing and ad hoc queries

Parallel Data Structures – Type 3

Small Discrete – Type 4

Application Function Data Structure Usage Pattern SLA Integration Scale

Highly Threaded – Type 2

• Scales well on clusters• XML parsing• Buisness intelligence with

Structured Queries• HPC applications

• Scales well on large SMP• Web application servers• Single instance of an ERP

system• Some partitioned

databases

• Limited scaling needs• HTTP servers• File and print• FTP servers• Small end user apps

Black are design factors Blue are local factors

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Fitness Parameters in Machine Design

Can be customized to machines of interest. Need to know specific comparisons desired

These parameters were chosen to represent the ability to handle parallel, serial and bulk data traffic.This is based on Greg Pfister’s work on workload characterization in In Search of Clusters

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Key Aspects Of The Theoretical Model

Throughput (TP) Common concept: Units of Work Done / Units of Time Elapsed Theoretical model defines TP as a function of Thread Speed:

TP = Thread Speed x Threads− Thread Speed is calculated as clock rate x Threading multiplier / Threads per Core.

Threading multiplier is the increase in throughput due to multiple threads per core

Thread Capacity (TC) Throughput (TP) gives us an idea of instantaneous peak throughput rate

− In order to sustain this rate the load has to keep all threads of the machine busy In the world of dedicated systems, TP is the parameter of interest because it

tells us the peak load the machine can handle without causing queues to form However in the world of virtualized/consolidated workloads, we are stacking

multiple workloads on threads of the machine− Thread capacity is an estimator of how deep these stacks can be

Theoretical model defines TC as: TC = Thread Speed x Cache per Thread

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Throughput, Saturation, Capacity

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TP Measured ITR Capacity

TP Pure Parallel CPU ITR Other resources and Serialization ETR Load and Response Time

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Single Dimension Metrics Do Not Reflect True Capacity

The “standard metrics” do not leverage cache.This leads to the pure ITR view of relative capacity on the right.

Common Metrics:ITR TPETR ITR

Power advantagedz is not price competitive

Consolidation:ETR << ITR unless loads are consolidatedConsolidation accumulates working sets Power and z advantagedCache can also mitigate “Saturation”

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Bridging Two Worlds - I

There appears to be a disconnect between “common benchmark metrics” and “theoretical model metrics” like TP

Does this mean metrics like TP are invalid? No We see the effect of TP/TC in real world deployments

− a machine performs either better or poorer than what a common benchmark metric would have suggested

Does this mean benchmark metrics are useless? No They provide valuable data points

A better approach would be to try and bridge these two worlds in a meaningful way

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Bridging Two Worlds - II

Theoretical model calculates TP and TC using estimated values for thread speed Based on machine specifications

Example: TP calculation for POWER7 A key factor in TP calculation is Thread Speed, which in turn depends on the

value of the thread multiplier− But this factor is only an estimate. − We estimated the thread multiplier for POWER7 in SMT-4 mode was 2

However, using an estimate for thread speed assumes common path length and linear scaling

An inherent problem here – these estimates are not measured or specified using any common metric across platforms

− As an example, should the thread multiplier be the same for POWER7 in SMT-2 mode as Intel running with HyperThreading?

Recommendation: Refine factors in the theoretical model with benchmark results Instead of using theoretical values for thread speed, pathlength etc., plug in

benchmark observations

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Two Common Categories Of Benchmarks

Stress tests Measure raw throughput

− Measure the maximum throughput that can be driven through a system, focusing all system resources to this particular task

VM density tests Consolidation ratios (VM density) that can be achieved on a

platform Usually do not try to maximize throughput of a system

− They usually look at how multiple workloads can be stacked efficiently to share the resources on a system, while delivering steady throughput

Adjusting Thread Speed affects both TP and TC

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Example of a Stress Test, A Misleading One If Used In Isolation!

This benchmark result is quite misleading, it suggests a z core yields only 15% better ITR. But we know that z has much higher “capacity”

What is wrong here? System z design point is to run multiple workloads together, not a single

atomic application under stress This particular application doesn’t seem to leverage many of z’s capabilities

(cache, IO etc.) Can this benchmark result be used to compare capacity?

Online trading WAS ND workload

driven as a stress test

2ch/16co Intel 2.7GHz Blade

Linux on System z

16 IFLs

TradeLite workload

Peak ITR:3467 tps

Peak ITR:3984 tps

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Use Benchmark Data To Refine Relative Capacity Model

Calculate Effective thread speed from measured values What is the benchmarked thread speed? Normalizing thread speed and clock to a platform allows us to

calculate pathlength for a given platform This in turn allows us to calculate Effective thread speed

Doing this affects both TP and TC

Plug in Effective thread speed values into Relative Capacity calculation model

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Use Benchmark Data To Refine Relative Capacity Model - Results

ITR / Threads

Clock ratio / Threadspeed ratio

Effective Threadspeed * Total Threads * Cache/Thread

In this case, System z ends up with a 13.5x Relative Capacity factor, relative to Intel

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Online banking WAS ND workloads, each driving 22

transactions per second with light I/O

Common x86 hypervisor

2ch/16co Intel 2.7GHz Blade

PowerVM

2ch/16co POWER7+ 3.6GHz Blade

z/VM on zEC12

16 IFLs

Light workloads

48 VMs

per IPAS Intel blade

100 VMs

per 16-way z/VM

68 VMs

per IPAS POWER7+ blade

Consolidation ratios derived from IBM internal studies. Results will vary based on workload profiles/characteristics.

Example of a VM Density Test: Consolidating Standalone VMs With Light CPU Requirements

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Use Benchmark Data To Refine Relative Capacity Model - Results

Follow a similar exercise to calculate effective thread speed Each VM is driving a certain fixed throughput

− This test used a constant injection rate− If throughput varies (for example, holding a constant think time),

need to adjust for that Calculate benchmarked thread speed Normalize to a platform to get path length Calculate effective thread speed Plug into relative server capacity calculation

In this case, System z ends up with a 22.2x Relative Capacity factor relative to Intel

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Math Behind Consolidation

Roger’s Equation:

Uavg = 1/(1+HR(avg))

Where

HR(avg) = kcN1/2

For consolidation, N is the number of loads (VMs)

k is a design parameter (Service Level)

c is the variability of the initial load

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Larger Servers With More Resources Make More Effective Consolidation Platforms

Most workloads experience variance in demand

When you consolidate workloads with variance on a virtualized server, the variance of the sum is less (statistical multiplexing)

The more workloads you can consolidate, the smaller is the variance of the sum

Consequently, bigger servers with capacity to run more workloads can be driven to higher average utilization levels without violating service level agreements, thereby reducing the cost per workload

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A Single Workload Requires a Machine Capacity Of 6x the Average Demand

Server utilization = 17%

Average Demand

m=10/sec

Assumes coefficient of variation = 2.5, required to meet 97.7% SLA

6x Peak To Average

Server Capacity Required

60/sec

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Consolidation Of 4 Workloads Requires Server Capacity Of 3.5x Average Demand

Server utilization = 28%

Average Demand

4*m =40/sec

Server Capacity Required140/sec

Assumes coefficient of variation = 2.5, required to meet 97.7% SLA

3.5x Peak To Average

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Consolidation Of 16 Workloads Requires Server Capacity Of 2.25x Average Demand

Server utilization = 44%

Average Demand 16*m =160/sec

Server Capacity Required360/sec

Assumes coefficient of variation = 2.5, required to meet 97.7% SLA

2.25x Peak To Average

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Consolidation Of 144 Workloads Requires Server Capacity Of 1.42x Average Demand

Server utilization = 70%

Average Demand 144*m = 1440/sec

Server Capacity Required2045/sec

Assumes coefficient of variation = 2.5, required to meet 97.7% SLA

1.42x Peak To Average

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Let’s Look At Actual Customer Data

Large US insurance company

13 Production POWER7 frames Some large servers, some small servers

Detailed CPU utilization data 30 minute intervals, one whole week For each LPAR on the frame For each frame in the data center

Measure peak, average, variance

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Detailed Data Example: One FramePAF5PDC

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res All

Guidewire

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Workloads vs. Peak-to-Average(Final Theoretical Model Overlaid)

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ioCustomer Data Confirms Theory

Servers with more workloads have less variance in their utilizationand less headroom requirements

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Consolidation Observations

There is a benefit to large scale servers The headroom required to accommodate variability goes up

only by sqrt(n) when n workloads are pooled The larger the shared processor pool is, the more statistical

benefit you get Large scale virtualization platforms are able to consolidate

large numbers of virtual machines because of this

Servers with capacity to run more workloads can be driven to higher average utilization levels without violating service level agreements

CMG 2013 27

Summary

We need a theoretical model for relative server capacity comparisons

Purely theoretical models need to be grounded in reality Atomic benchmarks can sometimes be quite misleading in

terms of overall system capability Refine theoretical models with benchmark measurements Real world (customer) data trumps everything!

Validates or negates models Customer data validates sqrt(n) model for consolidation