Presentation by: Aater Suleman, PhD

BACKGROUND

Hardware designer come parallel programmer

Core microarchitecture and many core design

Worked on parallel programming, compilers, task scheduling

Distributed application performance

The views are my own and do not represent those of my current and

past employers

PERFORMANCE OPTIMIZATION

CPU RAM Disk NIC

Optimization

App

Middleware (Hadoop,

Sector/Sphere, etc)

OS/Hypervisor

System Hardware

MICROSERVERS IN BIGDATA

AND THE MICROSERVER WORLD IS JUST

AROUND THE CORNER

Shipments of microservers will rise threefold this year

Microservers would change the face of computing

Estimates of adoption between now and 2015 vary, but

are as high as 49% compound growth rate for Micro

server adoption

BIG DATA NEWS AND GOSSIP WORLD EXCLUSIVES

THE

BIG DATA news

Going green with micro

servers

AV

AIL

AB

LE

MIC

RO

SE

RV

ER

S

MICROSERVERS

NE

ED

FO

R U

SE

MICRO SERVERS AVAILABLE

TODAY

Marvell, TI, nVidia

Intel ATOM based servers

Intel Server Calxeda Server

HOW ARE THEY DIFFERENT? N

EE

D F

OR

US

E

MICROSERVERS A

VA

ILA

BL

E M

ICR

O S

ER

VE

RS

Power-efficient

cores

Disk BW/comp

ute

Network bandwidth/compute

Computes/TCO-$

It is not that simple …

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

16 32 64 128 256 512 1024 2048 4096 8192 16384Bandwidth Requirement

NB

CB

Micro server becomes feasible due to cost

* CB – CORE BOUND ; NB – NETWORK BOUND

Traditional Server

Micro server

Syst

em

Ca

pa

city

WHEN TO USE MICROSERVERS?

When app is bandwidth bound and not CPU bound

When app scales well

When cost and throughput are more important

than latency

Data Size

CPU/

Memory

x

Data Size z

Disk

Bandwidth

Data Size y

Network

BW

Capacity =MIN x, y, z( )

1. Difference between x, y, z represents inefficiency

2. Traditional servers had these fixed

3. Microservers will have more choices

BENCHMARKS

Porting is not always feasible

Use performance monitoring to characterize app

Architecture independent benchmarks that test

sub-systems in isolation

SPECInt Rate for CPU/Memory

FIO (JBOD configuration) for disk

Iperf for Network

Compare Actual Cost (dollars)

Number servers = Requirement/capacity

Total Cost of ownership = (cost per server) x

number of servers

Don’t forget to future-proof the analysis

The requirements will change

What looks good today won’t look good tomorrow

EXPECT

Lots of differentiated platforms

New approaches

Asymmetric Clusters

Dedicated Networks

Shared local disks with remote cores

Optimized appliances

GPGPUs

Hardware accelerators

RECOMMENDATIONS

Keep Microservers on your Big Data roadmap

Keep their strengths and weaknesses in your mind

while you code

Keep your eyes and ears open to things that can

make a good benchmark

More on this on my blog :

futurechips.org

I am just a click away on

www.linkedin.com/pub/aater-suleman/3/21b/ba4

maater@gmail.com