Emc_sdatacenter_how to Build a Scale-out Infrastructure

7/27/2019 Emc_sdatacenter_how to Build a Scale-out Infrastructure

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HOW TO BUILD A

SCALE-OUT

INFRASTRUCTURE


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Scale-up or scale-out: What fts best inyour data center?

Despite IT wishes,scale out architec-ture growing in datacenters

HOW TO BUILD A SCALE-OUT INFRASTRUCTURE

SCALE-UP OR SCALE-OUT: WHAT FITS BEST IN YOUR DATACENTER?

Every data center uses servers to supply computing resources processing

cycles, memory space, network and disk I/O that workloads need to unction.

As workloads prolierate and computing demands increase, server resources

must grow or scale to meet those demands. Well answer some common ques-

tions about server scaling and consider the implications on an enterprise.

We hear the terms scale-up and scale-out servers used re-

quently, but what are they and what are the diferences between them?

Stephen Bigelow: There are two basic ways to scale computing (server)

resources in a data center. The rst is to add more servers or scale-out. Say

a business has a virtualized server running ve business applications and is

using 80% o the servers physical computing capacity. I the business needs

to deploy more workloads, the current server may not have enough resources

available, so the business could purchase and deploy an additional server to

support the new applications.

Scale-out architecture also includes clustered or distributed computing


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approaches where multiple small servers share the computing load o a singleapplication. For example, a mission-critical workload may run on two or more

servers, and the processing can be shared across those servers in an active-

active conguration. I one server ails, the other(s) can take over and preserve

the applications availability. I more redundancy is needed, the cluster can be

scaled-out with additional server nodes.

But advances in computing power have vastly increased server resources

in each new design. Today, it is possible to replace an aging server with a model

touting ar more processing, memory and I/O capability than previous models

yet occupies the same physical ootprint such as a 1U or 2U rack chassis

and oten consumes less energy. This approach is called scale-up because the

physical box can handle more or larger workloads.

Consider the rst example where one virtual server ran short o resources.

It is possible to deploy a new server in the next technology reresh cycle with ar

more computing resources, migrate all o the workloads rom the old server to

the new one, take the old server out o service or allocate it to other tasks and be

let with signicantly more available resources to tackle additional production

workloads without adding signicantly to data center space or energy require-

ments. Its like youre slowly easing the older server into retirement.


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When is it best to use a scale-up server in the data center, and whenshould an organization opt or a scale-out server?

Bigelow: There is no single best answer. Both scale-up and scale-out ap-

proaches are valid means o adding computing resources to a data center envi-

ronment, and they are not mutually exclusive. A scale-out approach could be

the right answer when a large number o smaller nodes are needed, perhaps

or a web server arm or a server cluster where physically redundant hosts are

required. Conversely, a scale-up server approach might be right or a major

virtual server consolidation initiative where more workloads must reside on

the ewer physical servers.

How does virtualization play into the scale-up versus scale-out

discussion?

Bigelow: You saw a bit o this in the previous questions. An organization

that deploys server virtualization can take advantage o server consolidation

by moving a greater number o workloads onto ewer and more capable servers.

This reduces the total number o servers that an organization has to buy and

puts ar more emphasis on the scale-up approach.

The bigger issue is resource allocation. Poor or careless allocation can ad-

versely aect scale-up plans. Virtualization allows you to provision a virtual


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machine or each workload and allocate computing resources to each virtualmachine. I you provide excess resources to a virtual machine 2 GB o mem-

ory when only 1 GB is needed resources are wasted and the server may host

ewer virtual machines than expected. Conversely, i an administrator doesnt

assign enough resources to a virtual machine, that workload may perorm

poorly or even cause the entire server to crash.

A business will get the most value rom consolidating to a scale-up server

i resources are properly allocated to meet each workloads needs.

Dont scale-up servers present more disruptions or a data center?

Bigelow: The potential or scale-up server ailures and work disruptions is

certainly real. When a powerul server runs a single application such as a da-

tabase, there is little potential or extra disruption since an application crash

or server ailure only means that a single workload needs to be recovered. As

long as the server is running or other suitable server hardware is available, it

doesnt take long or skilled IT sta to recover the application thanks to the

servers greater computing power.

However, the matter is a bit dierent i the scale-up server is virtualized and

consolidated with numerous workloads. I a server like that ails there could

be many more workloads to recover and the process could take considerable


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time. Remember that as each workload is restored it will start using networkand other computing resources on that box and eectively slow the recovery

o subsequent workloads.

Still, its important to put such disruptions into the proper perspective.

Mission-critical workloads should be protected with some kind o resiliency

strategy such as physical server clustering or virtual workload redundancy us-

ing tools like EverRun rom Marathon Sotware. When critical workloads are

protected, they will continue to be available and will eventually synchronize

with the original machine when it is successully recovered. Only non-essential

or non-critical workloads would bear the brunt o extended downtime.

How does the reliability o scale-up and scale-out servers compare?

Bigelow: The reliability o scale-up and scale-out servers typically com-

pares quite well. The interesting thing is that many enterprise-grade serv-

ers are incorporating technologies designed to enhance reliability and avoid

downtime. Techniques that were once in the domain o the most powerul and

expensive systems are quickly ltering down to entry-level models.

Even entry-level 1U servers include redundant power supplies so the server

will continue to run even when one supply ails. Similarly, the presence o sev-

eral multi-core processors means that only some workloads may be disrupted


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i a core ails, but the aicted workloads can be restarted on another systemor even on other available processor cores in the same system. Its the same

when several network I/O ports are present. Workload trafc can ailover rom

a aulty port to a working port or the impacted workloads can be migrated to

another server with minimal perormance degradation. A measure o CPU and

network port redundancy can be realized on entry-level enterprise servers.

Memory is one o the last rontiers in server reliability because virtual ma-

chines reside as images in server memory. Entry-level enterprise servers like

the Dell PowerEdge R510 support error correction code (ECC) memory which

can correct common kinds o memory corruption, but ECC generally doesnt

protect against outright memory aults.

More sophisticated servers such as the Hewlett-Packard ProLiant amily

seek to mitigate downtime by including ault-tolerant memory techniques such

as memory mirroring think RAID-1 with disk storage--and spare memory

modules online that can automatically take over or ailed memory modules

similar to hot spare disk storage.


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DESPITE IT WISHES, SCALE OUT ARCHITECTURE GROW-ING IN DATA CENTERS

Lately Ive noticed that IT trends within an organization and IT trends within a

data center are like classical mechanics and quantum mechanics, respectively.

I its been a while since your last physics course, classical mechanics govern

the motion o large objects. However, once the object gets small enoughlike

to the size o an atomall the rules change. Those new rules are quantum

mechanics.

Data centers are like that, too. Outside a data center, IT departments preach

consolidation and centralization. They push data and processing back into the

data center with virtualization and cloud computing. They even push the desk-

tops into the data center with virtual desktop inrastructure.

But within a data center, the trend is the opposite. Applications and systems

that scale up with a Just throw hardware at it approach are yielding to those

that scale out. The result is a swarm o smaller machines working together

versus a monolith, or local disk arrays orchestrated by sotware to provide the

services o a more traditional centralized array.


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DECENTRALIZATION TRUMPS CENTRALIZATIONSo the operative question is, why arent data centers ollowing their own

rules?

Frankly, theres only one reason or this: cost. The cost o a traditional cen-

tralized disk array is enormous compared to the perormance it delivers, espe-

cially when you actor in the complex way servers attach to it, and the way the

storage systems and networks have to be managed and monitored. In contrast,

local storage is easy. Absolutely everybody knows how it works, so its relatively

immune to human error. Its ast enough or most workloads, especially with

many RAID controllers now oering native solid-state drive caching (to inex-

pensive commodity drives, no less). And every server monitoring tool on the

planet can monitor local storage, so its one less thing you have to pay or, be

trained on, implement and manage.

Systems that scale out are trendy or similar reasons. With a monolithic

system, you need to size or peak workloads. The rest o the year, all that capac-

ity is wasted, or at least hard to use. A scale-out system can be sized to exactly

what is needed, with the capacity returned to a pool ater peaks subside. This

also appeals to organizations pushing workloads to the cloud. As hybrid cloud

technologies mature, the idea o cloud bursting, or temporarily pushing


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workloads into a public cloud, gets more realistic every year. It also meansthe ability to take ull advantage o concepts like Amazon Elastic Compute

Clouds Spot Instances to run workloads in EC2 when the price to do so is lower

than your own data centers operational costs.

Will the trend o internal data center decentralization continue, despite the

trend o organizational IT centralization? Most things in IT are cyclical, so it

wouldnt surprise me i, in 10 years, we started centralizing data centers again.

Until then, I think we should get used to IT not practicing what it preachesor

the good o our bottom lines.


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