Software-defined and Hyper-converged - Buzz or Boom?

0 Copyright 2016 FUJITSU

Fujitsu Forum 2016

#FujitsuForum


Software-defined and Hyper-converged – Buzz or Boom?

Frank Reichart

Senior Director Storage, Global Product Marketing, Fujitsu

Markus Leberecht

Senior Data Center Solutions Architect,Intel

Gernot Fels

Head of Integrated Systems,

Global Product Marketing, Fujitsu


The impact of a digitalization on DC infrastructures

New dimensions of fast and flexible scalability are to be served

Will be gathered

Will be transported

Will be processed

Will be stored

More data

Digital world

Servers Storage Networks


Digitalization demands a new DC Architecture

New dimensions of fast and flexible scalability

Servers Storage Networks

Any Application

SDDC Platform

any x86 server

any Storage

any IP Network

Data Center Virtualization

New Architecture needed The Software Defined Data Center

Complete virtualization / abstraction of the stack

Components are interchangeable

Fast and automated provisioning

Self-service provisioning via portals

Metering and charging

Quality-of-service mgmt.


Software-defined Data Center – Building Blocks

Agile NETWORKING technology

Agile and hyper-scale STORAGE systems

Agile aggregation of COMPUTE power

Software-defined networks

Software-defined storage

Software-defined computing

ORCHESTRATION PLATFORM HYBRID IT MANAGEMENT


Server-Architecture for the SDDC

Server Requirements for the SDDC

Modular design for fast scalability

Online expansion of processing power

Combined compute and storage nodes for hyper-converged scenarios

Flexible and dynamic connectivity

Move from blade-design to modular server design

High-density to save DC space

Low energy, low heat PRIMERGY CX cloud server

http://mediaportal.ts.fujitsu.com/pages/view.php?ref=34137&search=!collection14298+PRIMERGY+CX&offset=0&order_by=field12&sort=DESC&archive=0&k=

6

Intel® Power and Thermal Telemetry Framework

Datacenter infrastructure

management focusing on

the area of linking facility

and IT devices (e.g.,

power, capacity, health)

leveraging OOB

technologies.

Management includes

telemetry monitoring,

aggregation, reporting,

analysis and controls.

Targeted customers:

facility + IT (bridging of

facility and IT)

Intel® DCM Middleware (Web Service API)

CONTROL MONITOR TREND

ANALYZE REPORT

ISV Solution / Data Center Manager Console

Hardware Protocols

Node Manager IPMI

iDRAC IPMI

iLO/DCMI IPMI

IMM IPMI

CMC HTTPS/WS-MAN

OA SSH/CLI

IMM SSH/CLI

SNMP

PDU and UPS Blade Servers Rack Servers

IPMI = Intelligent Platform Management Interface

IMM = Integrated Management Module

SNMP = Simple Network Management Protocol

WS-MAN = Web Services-Management

iDRAC = Integrated Dell Remote Access Controller CMC = Chassis Management Controller CLI = Command Line Interface DCMI = Data Center Manageability Interface

iLO = Integrated Lights-out OA = Onboard Administrator SSH = Secure Shell

Using Intel® Data Center Manager for Improved

Efficiency and Disaster Recovery

7

Monitor

Track actual power

and temperature

data directly from

the servers

Survive

Automatically

reduce power to

extend operations

during power or

cooling events

Reduce

Reduce total power

& carbon footprint

with strategic

power capping

Maximize

Increase rack

density with

confidence by

limiting node

power

Schedule

Schedule

workloads to

evenly distribute

power & thermal

loading for greater

efficiency

Respond

Deliver server

power, thermal &

utilization directly

to facility control

for rapid response

How can Telemetry

help reduce OPEX?

8

Increase rack density to

utilize

allocated power

Identify ghost servers to

reduce wasted

power and space

Optimize thermals

for higher cooling

efficiency

Allowed customers to

increase rack density

by 25-30%.

Average savings

$300,000 - $650,000

4°C CRAC temp

increase expected to

save 25-32% in power

consumption for

cooling.

Average savings

$200,000 - $225,000

Identified 10-15%

of underutilized

servers on average,

and virtualized those

systems.

Average savings

$80,000 - $100,000

Average data center based on 3MW with ~300 racks


Hyper-scale Storage-Architecture for the SDDC

Storage Requirements for the SDDC

Decoupling data management from the underlying hardware – software defined storage

Clustered architecture for linear scalability of capacity and performance

Using Open-source storage platforms (e.g. Ceph) to avoid lock-in and to ensure longevity

Online refresh of storage to extend lifecycle - avoiding storage migration

Support of object storage


Network Architecture for the SDDC

Source: Brocade

Hierarchical Network Fabric Network

VCS Fabric


Management Layers for the SDDC

ORCHESTRATION PLATFORM HYBRID IT MANAGEMENT

Software-defined network fabric Software-defined, hyper-scale storage Modular servers

Hypervisors/Containers

VCS Fabric

http://mediaportal.ts.fujitsu.com/pages/view.php?ref=34137&search=!collection14298+PRIMERGY+CX&offset=0&order_by=field12&sort=DESC&archive=0&k=


Integrated Systems – the fast Track to SDDC

The challenge The solution

Building SW-defined infrastructures creates substantial integration efforts

Integrated systems offer a short-cut through pre-integration

VMware VCF Red Hat OpenStack VMware VSAN Examples:


The two shades of SDDC

Functional approach Hyper-converged approach

Discrete software-defined compute, storage and network entities

Ideal for general purpose infrastructures

Software-controlled building blocks with integrated network, storage and compute functions

Ideal for specific purpose scenarios

ORCHESTRATION

NETWORK

COMPUTE

STORAGE

ORCHESTRATION

NETWORK

COMPUTE

STORAGE

NETWORK

COMPUTE

STORAGE

NETWORK

COMPUTE

STORAGE


A Deeper Dive into Hyper-converged


Hyper-converged infrastructure

Characteristics

Interconnected standard servers

Local DAS, no external storage

Virtual instead of physical SAN

Data services (e.g. replication, deduplication)

HA built-in

Unified central management

Scale-out (performance, capacity)

Auto-discovery

SW-defined

Network

Compute Virtualization Layer

Storage Virtualization Layer

Node Node Node

App

Compute

Storage

App

Compute

Storage

App

Compute

Storage

Virt

ual S

AN


Hyper-converged infrastructure

Characteristics

Interconnected standard servers

Local DAS, no external storage

Virtual instead of physical SAN

Data services (e.g. replication, deduplication)

HA built-in

Unified central management

Scale-out (performance, capacity)

Auto-discovery

SW-defined

Benefits

Less components, less space

Less energy and cooling

Simplicity (less admin efforts)

Lower skills (lower-paid staff)

Flexible alignment to business

Great responsiveness

Business continuity

Usually lower TCO

Hyper-converged – Jack of all trades?

Network

Compute Virtualization Layer

Storage Virtualization Layer

Node Node Node

App

Compute

Storage

App

Compute

Storage

App

Compute

Storage

Virt

ual S

AN


Working principle

Classical Hyper-converged

OS

App

VM


Working principle


OS

App

VM


Working principle


OS

App

VM

OS

App

VM


Working principle


OS

App

VM

OS

App

VM

VM does not necessarily run where its data is Locality relevant for read, not for write


What happens, if a server fails

OS

App

VM




OS

App

VM


X




OS

App

VM




OS

App

VM

OS

App

VM




OS

App

VM

OS

App

VM

X




OS

App

VM

OS

App

VM




OS

App

VM

OS

App

VM

Data mirroring




OS

App

VM

OS

App

VM

Everything goes on running. Just a VM restart.


How long will data mirroring take?

Ethernet speed 1 GbpsMax. throughput 80% 0,8 Gbps 0,1 GB / sec 0,0001 TB / sec

Transfer volume 1 TBTransfer time 10000 sec 167 min 2,78 hrs 2 hrs 47 min


Ethernet speed 10 GbpsMax. throughput 80% 8 Gbps 1 GB / sec 0,001 TB / sec




And what if …


OS

App

VM


And what if …


OS

App

VM

X


And what if …


OS

App

VM


And what if …

OS

App

VM


X


And what if …

OS

App

VM


All VM stopped

Out of Operation

Out of Order


And what if …


OS

App

VM

OS

App

VM

All VM stopped

Out of Operation

Out of Order


And what if …


OS

App

VM

X

OS

App

VM

All VM stopped

Out of Operation

Out of Order


And what if …


OS

App

VM

OS

App

VM

All VM stopped

Out of Operation

Out of Order


And what if …


OS

App

VM

X

Data mirroring

OS

App

VM

All VM stopped

Out of Operation

Out of Order


And what if …


OS

App

VM

OS

App

VM

Out of Operation

Out of Order

All VM stopped

Out of Operation

Out of Order


And what if …


OS

App

VM

OS

App

VM

Out of Operation

Out of Order

Out of Operation

Out of Order

All VM stopped

Other VM not depending on data mirroring can continue running

?

In both cases there are bottlenecks.


How to counteract?

OS

App

VM

It is just a matter of investment: What am I willing to pay for which SLA.


OS

App

VM

Multi-mirroring


Classical Hyper-converged Classical Hyper-converged Classical Hyper-converged

IP switch IP switch IP switch IP switch IP switch IP switchIP switch IP switch IP switch IP switch IP switch IP switch

Server ServerServer Server Server Server Server ServerServer Server Server Server Server Server

Server Server Server ServerServer

Storage Storage Storage

-19% Less HW, more SW -5% Less HW, more SW 0% Less HW, more SW

20 VM 50 VM 200 VM (DR)

Configuration examples and CAPEX

Often less CAPEX with classical. Usually less OPEX with hyper-converged.


Architectures in comparison

Classical LAN, server, SAN, storage

LCM for multiple components (different LC)

2 separate networks to maintain

HW-defined

Scalability by adding multiple components, primarily separately

Storage activities are separated from servers

Established for many years

Hyper-converged LAN, server (includes storage)

LCM for less components

More powerful LAN required

SW-defined

Scalability by adding servers, primarily linear (compute & storage)

Servers are involved in storage activities

To be considered when sizing compute resources

Entered the market some 3 years ago


What do these differences mean for you?

Classical To be used for general purposes

Physical, virtual, multi-hypervisor

HW dependence, primarily related to storage

Well-known SW licensing (e.g. DB)

Higher operational complexity

Possibly higher HW cost, but lower SW cost instead

Mainly well established vendors

Hyper-converged Less usable for general purposes

Virtualization is quasi a must

SW dependence (related to virtualization SW)

Commercial restrictions (e.g. DB licensing)

Lower operational complexity

Possibly higher SW cost, but lower HW cost instead

Established vendors and newcomers


What do these differences mean for you?

Classical To be used for general purposes

Physical, virtual, multi-hypervisor

HW dependence, primarily related to storage

Well-known SW licensing (e.g. DB)

Higher operational complexity

Possibly higher HW cost, but lower SW cost instead

Primarily well established vendors

Hyper-converged Less usable for general purposes

Virtualization is quasi a must

SW dependence (related to virtualization SW)

Commercial restrictions (e.g. DB licensing)

Lower operational complexity

Possibly higher SW cost, but lower HW cost instead

Established vendors and newcomers

Both approaches provide unique advantages. Good reasons for coexistence.


Classical or hyper-converged?

Workloads need scale-out / scale-up?

Compute & storage needs scale in tandem?

Workloads require a fixed amount of resources

Granular expansion on component level?

Workloads benefit from data services?

Benefit from unified management?

Existing staff roles?

Utilize existing storage?

Repurposing

Monolithic app?

Mixed operation (multi-hypervisor / physical)?

Optimal support by physical platform?

CAPEX and OPEX

Use cases matter. Business requirements determine the optimum approach.

Recommendation: Decide per each use case; Go for hyper-converged, if benefits outweigh drawbacks.


Savings our customers achieved

DC space 50%Power consumption 50%Cooling requirements 25%Initial support cost 25%Maintenance cost 40%Time to expand infrastructure 85%Upgrade time (SW, firmware) 65%Time to deploy VM (from request to delivery) 60%Travel time (ROBO) 99%

Advantages of hyper-converged may translate to savings in cost and time.


Summary

HCIs can accelerate the way to the SDDC but are not necessary less costly

Integrated systems can heavily decrease implementation time and risk

The new software-defined, “Fast IT” will emerge alongside the traditional, “Robust IT” resulting in a bimodal IT

The SDDC impacts server, storage and network architectures

Main drivers for SDDC are needs for fast provisioning/scalability

Neither hardware or software defines your data center, it is defined by your business

Software-defined and Hyper-converged - Buzz or Boom?

Technology

Transcript of Software-defined and Hyper-converged - Buzz or Boom?