Storm Clouds Ahead? An Introduction to Cloud Computing

(1)

Storm Clouds Ahead?

An Introduction to Cloud Computing

Andy Bolton

Chief Executive Officer, Capacitas

Strategy Briefing – Cloud Computing: The Capacity, Performance & Cost Implications, 28th_{May 2012}

An Introduction to Cloud Computing

2

Agenda for today

9:00 - 9:30 Registration & Coffee/Tea

9:30 - 10:00 Welcome and Introduction to Cloud Computing Andy Bolton, Chief Executive Officer, Capacitas 10:00 - 10:45 Capacity Management and the Cloud

Danny Quilton, Chief Operating Officer, Capacitas 10:45 - 11:00 Coffee/tea

11:00 - 11:45 The Economics of Cloud Computing

Dr. Manzoor Mohammed, Chief Technology Officer, Capacitas

11:45 - 12:30 Assessing the Capacity and Performance Benefits of a CDN for easyjet.com Danny Quilton, Chief Operating Officer, Capacitas

12:30 - 13:15 Buffet lunch and networking

13:15 - 14:00 Building a Simple Capacity Model of a Cloud Based Service Sameena Hassam, Senior Consultant, Capacitas

14:00 - 14:30 Question & Answer Panel Session Chair: Danny Quilton

(2)

Introduction

• Capacitas is a professional services and training company that specialises in ICT performance and capacity.

• Founded in 2002 we have grown to 21 staff in summer 2012

• We have a strong placement programme running with the objective to recruit these students as graduates

• In 2011-2012 financial year:

• 95% of revenue is from consultancy services and 5% from training

• 48% of consulting revenue was from managed services

• 52% of consulting revenue was from customer projects

• Clients from many sectors including transport, retail, investment banking, insurance and outsourcers

3

Introduction

The IT industry has evolved over the last fifty years, changed paradigms constantly:

• from single, hugely expensive mainframe systems back in the 1960s and 1970s;

• through the rise of the personal computer in the 1980s;

• the associated explosion in distributed computing in the 1990s and server sprawl;

• and through to the new era of consolidation back onto centralised platforms.

(3)

Centralised Computing Paradigm (1955-1985)

Dial-in or Leased Line

Remote user Local users

Distributed Computing Paradigm (1985-1995)

6 Application Server Database Server Web Server File Server Dial-in Remote user Local users

(4)

Distributed Computing Paradigm (1995-2000)

Distributed Computing Paradigm (2000-2005)

(5)

Distributed Computing Paradigm (2005-)

Cloud: the next step in Virtualisation?

We have now virtualised many aspects of computing (i.e. consolidated onto larger platforms):

• Computing power (e.g. VMware servers)

• Networks (e.g. VPNs)

• Storage (e.g. SANs)

• Desktops (e.g. Citrix)

(6)

Cloud: the next step in Virtualisation?

Desktop Operating System

Data (Profile and documents) System Services (Windows services, COM, OLE, printers, etc)

Configurations (Profile and documents) Application A SystemGuard™ Environment Application B Software Virtualisation Layer Virtual Hardware Virtual Machine Virtual Machine Application A Application Application B Guest Operating System Guest Operating System VPN Fibre Channel

Typical Cloud Architecture

© Capacitas 2002-2012 12 Application Servers Database Servers Web Servers Storage Servers ‘Cloud’ Provider Authentication Servers Billing Servers Provisioning Servers IT Management End-User Services Systems Management End-User

(7)

Cloud Service Providers

Some of the leading providers of Cloud services are:

• Amazon • Google • Microsoft • Rackspace • Salesforce © Capacitas 2002-2012 13

Some Cloud Services Available

• Web Servers (e.g. Apache, IIS)

• Application Servers (e.g. Java, Linux, Windows Server, Solaris)

• Queue Services

• Database Servers (e.g. Oracle, SQL Server)

• Storage Services

(8)

Managing Service Capacity

One of many reasons for companies to adopt Cloud computing is the difficulty in forward planning of service capacity to meet demand.

This has many repercussions. These include:

• Inability to reduce or prevent capacity-related service outages;

• Inability to accurately forecast when additional capacity is required;

• Inability to identify when capacity can be reduced;

• Inability to plan capacity purchases in advance preventing cost-effective procurement;

• Inability to forecast costs of the infrastructure and provide accurate budgets;

• Inability to relate customer-driven demand units to capacity required.

Too many organisations therefore undertake easier, reactive capacity management activities.

15

Managing Service Capacity

© Capacitas 2002-2012 16 Managing Service Capacity Managing Demand Managing Supply Yield Management Developing Complementary Services Partitioning Demand Promoting Off-Peak Demand Offering Price Incentives Developing Reservation Systems Sharing Capacity Increasing Customer Participation Creating Adjustable Capacity Scheduling Work-Shifts Cross-Training Employees Using Part-Time Employees

(9)

Managing Service Capacity – Where is Cloud?

© Capacitas 2002-2012 17 Managing Service Capacity Managing Demand Managing Supply Yield Management Developing Complementary Services Partitioning Demand Promoting Off-Peak Demand Offering Price Incentives Developing Reservation Systems Sharing Capacity Increasing Customer Participation Creating Adjustable Capacity Scheduling Work-Shifts Cross-Training Employees Using Part-Time Employees

Cloud Service Costs

The comparative cost advantage of the Cloud business model is contentious at best.

There are many reports that claim Cloud is less expensive than conventional in-house computing. However there are also reports that claim the opposite.

The answer…

…is not in this presentation I’m afraid!

Some contradictory resources:

• Forrester report: The ROI Of Software-As-A-Service, by Liz Herbert and Jon Erickson

• CMG MeasureIT 8.2: Capacity Concerns in a SaaS and Cloud World

(10)

Cloud Service Costs – Pricing Models

Pricing tend to be based on utility models, often comprising a mixture of the following methods:

• a subscription fee (e.g. monthly)

• a resource usage fee (e.g. CPU seconds, GB storage, GB I/O)

• a transaction fee (e.g. # of transactions processed)

This pricing structure is comparable to buying utilities, such as gas and electricity, hence the origin of the term ‘utility computing’.

19

Cloud Service Costs – Example Pricing

An example pricing model is described below:

• Processing: £0.10 per CPU available per hour

• Storage: £0.12 per GB stored per month

• Storage transaction: £0.01 per 5,000 transactions

• Data transfers: £0.05 in / £0.10 out / GB

(11)

The Implication of Utility Pricing

While there are many advantages with adopting a Cloud model, there is a risk of this uncapped pricing scheme resulting in unexpectedly large bills.

IT organisations like budgets! These are designed so that the company knows in advance what the annual ICT expenditure is likely to be.

Cloud introduces a completely variable cost item into the financial model. This doesn’t mean it’s unpredictable, but unless there is some way contractually to cap the volume-based fees this is a risk.

21

The Implication of Utility Pricing on Outsourcers

The variability of the utility pricing model can have a considerable impact on Outsourcers.

Their customers expect a fixed price for their contracts, especially in the public sector. The public sector often plans budgets out as far as 3 or 5 years, so cost variability is

unwelcome. They frequently specify caps for transaction volumes.

An outsourcer who wants to provide or use a Cloud-based infrastructure may have to carefully structure contracts to avoid paying for its customers excess demand.

(12)

Financial Risk to Outsourcers

© Capacitas 2002-2012 23 80 90 100 110 120 130 140 150 Ja n -0 9 F e b -0 9 M a r-0 9 A p r-0 9 M a y -0 9 Ju n -0 9 Ju l-0 9 A u g -0 9 S e p -0 9 O c t-0 9 N o v -0 9 D e c -0 9 Ja n -1 0 F e b -1 0 M a r-1 0 A p r-1 0 M a y -1 0 Ju n -1 0 Ju l-1 0 A u g -1 0 S e p -1 0 O c t-1 0 N o v -1 0 D e c -1 0 Ja n -1 1 F e b -1 1 M a r-1 1 A p r-1 1 M a y -1 1 Ju n -1 1 Ju l-1 1 A u g -1 1 S e p -1 1 O c t-1 1 N o v -1 1 D e c -1 1

Financial Risk to Outsourcers

© Capacitas 2002-2012 24 80 90 100 110 120 130 140 150 Ja n -0 9 F e b -0 9 M a r-0 9 A p r-0 9 M a y -0 9 Ju n -0 9 Ju l-0 9 A u g -0 9 S e p -0 9 O c t-0 9 N o v -0 9 D e c -0 9 Ja n -1 0 F e b -1 0 M a r-1 0 A p r-1 0 M a y -1 0 Ju n -1 0 Ju l-1 0 A u g -1 0 S e p -1 0 O c t-1 0 N o v -1 0 D e c -1 0 Ja n -1 1 F e b -1 1 M a r-1 1 A p r-1 1 M a y -1 1 Ju n -1 1 Ju l-1 1 A u g -1 1 S e p -1 1 O c t-1 1 N o v -1 1 D e c -1 1 Service cap is breached Unexpected leap in demand

(13)

Financial Risk to Outsourcers

© Capacitas 2002-2012 25 80 90 100 110 120 130 140 150 Ja n -0 9 F e b -0 9 M a r-0 9 A p r-0 9 M a y -0 9 Ju n -0 9 Ju l-0 9 A u g -0 9 S e p -0 9 O c t-0 9 N o v -0 9 D e c -0 9 Ja n -1 0 F e b -1 0 M a r-1 0 A p r-1 0 M a y -1 0 Ju n -1 0 Ju l-1 0 A u g -1 0 S e p -1 0 O c t-1 0 N o v -1 0 D e c -1 0 Ja n -1 1 F e b -1 1 M a r-1 1 A p r-1 1 M a y -1 1 Ju n -1 1 Ju l-1 1 A u g -1 1 S e p -1 1 O c t-1 1 N o v -1 1 D e c -1 1 Service cap is breached Unexpected leap in demand Outsourcer liable for this cost

Service Performance

When IT infrastructure is kept in-house monitoring and measuring service performance at each step of a transactional path is achievable, though it is not frequently not undertaken.

However as more companies adopt formal Service Management processes such as ITIL there is the need to establish Service Level Agreements (SLAs).

One key aspect of a Service Level Agreement is the monitoring, measurement and reporting of aspects of service performance such as transactional response times, availability and batch run times and end times.

Moving to a Cloud model can make this more difficult. Some commercial Cloud SLAs are a retrograde step from current commercial outsourcers’ SLAs, simply containing statements like:

“we guarantee […] external connectivity 99.95% of the time”.

(14)

Service Performance – In-house

Response Time Measurable Remote_{Response Times}

Local Office

Service Performance – Cloud

© Capacitas 2002-2012 28 Application Servers Database Servers Web Servers Storage Servers ‘Cloud’ Provider End-User Measurable End-to-End Transaction Response Time Customer Demarcation Supplier Demarcation Measurable Local Response Time Immeasurable But Derivable Supplier Response Times Local Office

(15)

Service Performance – Service Level Agreements

The Service Level Agreement defines the service that the customer expects from a supplier

Key Points:

• Do not rely on Service Credits to guarantee performance; often it is cheaper for the service provider to pay the service credit than resolve the problem

• Ensure the SLA is achievable, watertight and equitable; one-sided SLAs help neither party in the long-term

• Unless the SLA has a Service Bonus for exceeding performance do not expect anything more than achieving any targets; this is the service provider’s margins at stake!

29

30

Summary

• Cloud is a new computing paradigm that is here to stay

• As with any new technology or business model it has its pros and cons • Before adopting Cloud it requires careful consideration of:

• Service Management aspects, such as capacity, performance and resilience • Security and Data Protection compliance