Huawei

Converged Infrastructure Vision

Santiago Julián, IT Product Manager, Enterprise Business Group

Madrid, Oct 24th, 2013

1

Data Center Development Trends

Converged infrastructure benefits:

1. Adapts to service changes: elastic system expansion reduces service risks

2. Enhances application performance: Computing, storage, and networking resources are optimized.

3. Shortens service online time: The resources pool supply shortens the time required for infrastructure construction from several

months to several hours.

4. Reduces OPEX and IT costs: Unified management for infrastructure and automatic O&M reduces OPEX and IT costs.

Research by Gartner indicates that 30% of data centers will use converged infrastructure by 2015.

Server-centric

• LAN Switch • UNIX, PC Server • Internal storage

• SAN Switch • Independent RAID

array • Separated SAN

network and LAN network

Storage- and Server- centric

• Data Center Ethernet

• FCoE • Unified data

center management

Unified network VM-centric

• A VM is a computing unit • Centralized storage and

dispatching storage on VMs

• Networks are moving towards I/O virtualization

• VM Mobility

Completely automatic architecture

• All computing, storage, and networking resources centrally managed and allocated

• Application transparency

2

Data-Centric Computing Centers and Network Centers

Data centers need to shift the focus of data lifecycles to form a new balance between computing, storage, and networking resources when

application performance bottlenecks occur.

SSD flash storage technology is widely used.

The data center layer 2 network (low latency, lossless Ethernet), virtual network, elastic cloud network, and software defined networking (SDN)

have recently increased in popularity.

Main latency

bottlenecks

Latency: Bandwidth

Latency: Bandwidth

Latency: Bandwidth

Latency: Bandwidth

Latency: Bandwidth

Latency: 1us Bandwidth: 40 Gbit/s

Latency: Bandwidth

Response time: Bandwidth

Port to port Latenc0y: 0.1us Bandwidth: 40 Gbit/s

Port to port Latency: <0.8us Bandwidth: 4-8 Gbit/s

SSD card/disks

Disks

Infiniband card

Ethernet card

FCOE card

Port to port Latency: 2~10us Bandwidth: 1-10 Gbit/s

3

WSC Architecture for Tiered Data Access & Storage

For Data Center with Cloud Computing Scale, the latency for remote RAM/SSD access is far less than local

HDD storage with 2 orders of magnitude difference, while the capacity and bandwidth are at the same level.

-- Abstracted from Google's "Data Center as a Computer".

4

Converged Infrastructure Development

Network

CPU Memory

External

storage

I/O acceleration (proprietary)

Network

CPU Memory

External

storage

I/O acceleration (proprietary)

Virtualization Network

CPU Memory

Storage

I/O acceleration (proprietary)

I/O acceleration (proprietary

or shared)

Unified physical machine

and virtual machine

management High-speed network

CPU Memory Storage

I/O acceleration (shared)

Unified physical and virtual resource pool management

Management platform (monitoring, configuration, automation, and

deployment)

Application deployment template

Traditional infrastructure

Virtualization Converged infrastructure

Converged architecture Converged infrastructure

Fabric-based converged infrastructure (Cloud)

System efficiency and agility increased; OPEX reduced

Serv

er

Serv

er

Hardware

Software

5

Computing and storage separation

1980 - 1990 1990 - 2011

PC/server

Computing and storage

convergence

CPU

MEM

Southbridge

chip

HBA

PCI/PCI-E

SAS/SATA/SCSI

Disk Disk

Disk

Computing node

Computing and storage separation (highly reliable virtualization

integration scenario with large capacity)

CPU

MEM

Southbridge

chip

NIC

PCI/PCI-E

Storage node

RAID Controller

MEM

Southbridge

chip

HBA

PCI/PCI-E

Disk Disk

Disk

+

NIC

Ethernet

SAS/SATA/SCSI

Unbalanced development of computing and storage performances leads to the architectural

separation of computing and storage. The difference between transistor-based computing and

disk-based processing keeps increasing, with data storage attracting more concerns. As a result,

computing and storage are separated to improve resource utilization.

6

2012 + --?

X86 CPU

MEM

HBA

PCI/PCI-E

SAS/SATA/SCSI

Disk Disk

Disk

+

NIC

Computing and storage integration node

X86 CPU

MEM

Southbridge chip

HBA

PCI/PCI-E

SAS/SATA/SCSI

Disk Disk

Disk

NIC

Computing and storage integration node

Cn: Computing blade

Sn: Storage blade Computing blade slots are compatible with storage blade slots.

Mixed insertion of computing blade and storage blade is

supported.

ARM CPU

MEM

Southbridge chip

HBA

PCI/PCI-E

SAS/SATA/SCSI

Disk Disk

Disk

+

NIC

Storage node

X86 CPU

MEM

Southbridge chip

HBA

PCI/PCI-E

SAS/SATA/SCSI

Disk Disk

Disk

NIC

Computing node

Mode 2 OSCA (computing blade+ARM storage blade)

Hardware architecture with integrated computing and storage

Balance server

Balance server

Balance server

...

Balance server

Mode 1 OSCA (computing blade+DAS storage blade) or RH2288

With the use of P2P block storage mechanism in cloud OSs, resource pools and the virtualization technology are applicable to the DAS in the server cluster. The physical separation of computing and storage evolves to the physical convergence and logical separation of architecture to support typical enterprise application consolidation with the minimum cost.

Most IT applications for business transactions of enterprises focus on the instant processing of information instead of the storage or

archiving of data. The convergence of storage and computing can meet this basic requirement of applications.

Southbridge chip

7

IO Performance Enhancement enabled by Scale-Out Computing-Storage Convergence

Page 7

App App

Cache

HDD

FusionCube

App App

Cache

HDD

App App

Cache

HDD

Server2 Server1 Server3

Cache

HDD HDD HDD

SAN

App App App App App App

Server2 Server1 Server3

Server+ SAN

For random IOPS：

Based on Dsware distributed storage engine, total capacity of server cache is more

than 5 times larger than that of centralized SAN controller, thus result in 3-5 times

better hot-data hit-rate and on-line IOPS performance；

SATA can be adopted to substitute SAS to achieve comparable performances with

half the cost

P2P 10GE

PCI-E

GE / FC

SAS

For sequential IOPS (esp. large Files):

Much more number of HDD can serve the same App or VM

instance under the flat P2P architecture, with peak burst MBPS

escalated 3-5 times；

8

Scale-out Distributed Storage

• High Performance Parallel I/O

10X total IO throughput

3-5X IOPS improvement

• High Reliability Replications cross nodes

Quick data rebuild (30min

vs. 12hrs for 1 TB)

• High Scalability Up to 2000 nodes

Linearly scalable in both

capacity and performance

Storage Blades

Server

Hypervisor

Server Server

volume1

Server

volume2

Server

volume3

FusionStorage

VM

Client Client Client

VM VM VM VM VM VM VM VM VM

I/O I/O

FusionSphere FusionSphere FusionSphere Hypervisor

FC/IP network

HDD

SAN/NAS

9

Typical applications of converging computing/storage resources in enterprise appliances

Converged infrastructure Appliance

Converged Fabric (10GE/IB/PCI-E)

Cloud OS DSware

Cloud OS

Converged Fabric (10GE/IB/PCI-E)

DSware

VDI CRM ERM Email ... BOSS

Aggregated multiple small-granularity independent

applications

Virtualization Virtualization

MEM

SSD

DISK

Exclusive single large-granularity distributed

application

HANA/Oracle RAC/

Hadoop Node1

HANA/Oracle RAC/

Hadoop Noden ...

10

iNIC Card

SSD卡

GPU

Computing

Storage

Network

Virtualization

Platform

Unified Central

Management

Distributed

Storage

FusionCube – A True Converged Infrastructure

11

• Start from small

• One chassis - Half

configuration with 4

full-width blades

• Up to 8 chassis concatenation

w/o external switches

• Up to 4096 vCores, 96TB RAM,

and 1.9PB Storage

• Scale out with external switches

• Auto-discover, auto-configure

• Up to 20*3*512 = 30,720 vCores,

240TB RAM and 6PB Storage

Scale On Demand Smoothly

Single

Chassis

One Rack with 3

Chassis

Multiple

Racks

Up to 20

Racks

Copyright©2012 Huawei Technologies Co., Ltd. All Rights Reserved.

The information in this document may contain predictive statements including, without limitation, statements regarding the future financial and operating results, future product

portfolio, new technology, etc. There are a number of factors that could cause actual results and developments to differ materially from those expressed or implied in the predictive

statements. Therefore, such information is provided for reference purpose only and constitutes neither an offer nor an acceptance. Huawei may change the information at any time

without notice.

HUAWEI ENTERPRISE ICT SOLUTIONS A BETTER WAY