Cloud Computing Virtualized Computing Infrastructures

Cloud Computing –

Virtualized Computing Infrastructures

Erik Elmroth

Cloud computing in plain English

www.youtube.com/watch?v=QJncFirhjPg

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Brief outline

• A Game changing trend in IT use • Revitalization of the datacenters

– Infrastructure providers for service providers

• Compute CloudsCompute Clouds

– virtualization

– datacenter infrastructure providing virtual resources as utility

• Umeå research in Cloud computing

– Research topics – Major projects

”Game Changing Trend”

Growth on Service Consumer Side

– Individuals – professionally and privately – Companies: External services or hosting of

complete IT environment

– Explosive growth in availability of services of internet

Revitalization of data centers!

Revitalization of the datacenters:

Service & infrastructure provider cooperation

Company or individual. Sees service, not hardware Provider for service user. Customer for infra provider Provides infra to service provider. (Datacenter) SLA SLA SLA SLA

Critical performance requirements

- to be cost-efficiently met

Extremely rapid growth (from global scale) – YouTube (16 months) 100 mil/movies per day, 20

mil. unique users per month

– AppStore (19 months): Over 100000 Iphone programs, ovre 3 billion downloads

Regular/planned peaksg /p p

– Banks, tax filing – Market campaign effects Unexpected peaks

– New related video streaming – Stock trading peaks at financial crises Regional aspects in usage patters

– Regional concerns (new, events, etc) – Time-dependent usage-patterns

Critical performance requirements

- to be cost-efficiently met

Extremely rapid growth (from global scale)

– YouTube (16 months) 100 mil/movies per day, 20 mil. unique users per month

– AppStore (19 months): Over 100000 Iphone programs, over 3 billion downloads

Regular/planned peaksg /p p

– Banks, tax filing – Market campaign effects

Unexpected peaks

– New related video streaming – Stock trading peaks at financial crises

Regional aspects in usage patters

– Regional concerns (new, events, etc) – Time-dependent usage-patterns

New Requirements on Datacenters

(Infrastructure Providers)

• Today, load peaks typically managed by extensive over-provisioning

– COSTLY!!!

• Need for a new datacenter infrastructure, that

– provide elasticity:

• scale quickly in response to demand increase (in • scale quickly in response to demand increase (in

minutes, not days)

• shrink dynamically to save resources (energy, other use)

– Improve network parameters by locality-awareness – manage SLAs corresponding to business

agreements

– support a variety of payment schemes (pay-per-use, pre-paid, flat-rate, etc)

• Today’s clouds provide partial solutions

Compute Clouds

• Virtual “cloud” of IT resources (within a datacenter)

• Services run on virtual resources, unaware of the physical resources

• Infrastructure – compute, storage, and network

network

• Utility model – provision on demand, charge back on use

– Notably, as power and running costs become a larger fraction of the total IT cost, the character of IT capacity become more utility-like

Before talking more Clouds…

• Virtualization 10

”Traditional” virtualization

Applic Applic Applic Applic.

Hardware (CPU, RAM, Disk, LAN) Operating System Virtual Machine OS Applic. Virtual Machine OS Applic. … Applic. Virtual Machine OS Applic.

Hypervisor virtualization

Virtual hi Virtualhi Virtualhi

Hardware (CPU, RAM, Disk, LAN) Hypervisor Hypervisor OS Applic. machine Appl OS Appl machine … OS Applic. machine

Virtualization features

With a virtual machine you can:

• Define machine size as part of physical machine • Halt and resume execution

• Migrate between physical machines

These features can be used for many purposes!

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Server Sprawl

• New application = new server

File/Print File/Print Application Application Application Application File/Print Database Database Application Application Application Application Application Database Application Application Application Database Database Application

Problems Server Sprawl

• Hardware

– Increased hardware acquisition costs – Increased infrastructure requirements – Increased hardware maintenance costs – Increased hardware replacement costs

• Administration

– Patch management – Backup and recovery

– Server management and troubleshooting

Server Consolidation

• Increase hardware

utilization

• Reduced costs

– Fewer systems – Less power – Less cooling – Less administration

• Reduced

Infrastructure

– Fewer racks – Fewer switches

Multiple OS & Applications

• Run multiple OS

– Shared hardware

• Incompatible application

• Applications with different

OS and library

requirements

• Isolation between

applications

Other levels of virtualization (with inconsequent naming) • Operating system virtualization

– Virtualization inside OS

– Full separation between applications, but all running in the same OS

• Application virtualizationpp

– Encapsulation of application in executable – no need for traditional installation of application in OS

– Runs as if installed on hardware but all access to OS is virtualized.

• Desktop virtualization

– As appl. virtualization but encapsulation of the whole desktop

What is Cloud Computing?

An emerging computing paradigm where data and services reside in massively scalable data centers

and can be ubiquitously accessed from any connected devices over the internet.

Not only one type of clouds…

NIST definition of cloud computing

5 characteristics •On-demand self-service •Broad network access •Resource pooling •Rapid elasticity •Measured service

National Institute of Standards and Technology

3 service models •Software-as-a-Service •Plattform-as-a-Service •Infrastructure-as-a-Service 4 deployment models •Private •Public •Community •Hybrid

The Amazon example …

(Why is an internet bookstore entering this market?)

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The Amazon example …

• EC2 is a web service that provides resizable compute capacity in the cloud

• S3 provides a web services interface to store and retrieve any amount of data, at any time, from anywhere on the web

• SimpleDB is a web service for running queries on structured data in real time

on structured data in real time

• CloudFront is a web service for content delivery (software distributions, web content, media files)

• SQS offers a reliable, highly scalable, hosted queue for storing messages as they travel between computers

• Mechanical Turk is a web service for programmatically access to marketplace for

work that requires human intelligence 23

Standard EC2 Instances (2010)

• Small: 1.7 GB, 1 EC2 Compute Unit (1 virtual

core), 160 GB storage, 32-bit platform • Large: 7.5 GB, 4 EC2 Compute Units (2 virtual

cores), 850 GB storage, 64-bit platform • Extra Large: 15 GB, 8 EC2 Compute Units (4

virtual cores), 1690 GB storage, 64-bit platform), g , p

One EC2 Compute Unit equivalent to a 1.0-1.2 GHz 2007 Opteron or 2007 Xeon processor

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Standard

Instances Linux/UNIX Windows

Small (Default) $0.10 per hour $0.125 per hour Large $0.40 per hour $0.50 per hour Extra Large $0.80 per hour $1.00 per hour

EC2 High CPU Instances (2010)

• Medium: 1.7 GB, 5 EC2 Compute Units (2

virtual cores), 350 GB storage, 32-bit platform • Extra Large: 7 GB of memory, 20 EC2

Compute Units (8 virtual cores), 1690 GB storage, 64-bit platform

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High CPU

Instances Linux/UNIX Windows

Medium $0.20 per hour $0.30 per hour Extra Large $0.80 per hour $1.20 per hour

Pay only for what you use

On-demand capacity allocation Own capacity

Resource need

S3 pricing (2010)

Storage

$0.150 per GB – first 50 TB / month of storage used $0.140 per GB – next 50 TB / month of storage used $0.130 per GB – next 400 TB /month of storage used $0.120 per GB – storage used / month over 500 TB

Data Transfer

27 Data Transfer

$0.100 per GB – all data transfer in

$0.170 per GB – first 10 TB / month data transfer out $0.130 per GB – next 40 TB / month data transfer out $0.110 per GB – next 100 TB / month data transfer out $0.100 per GB – data transfer out / month over 150 TB

Requests

$0.01 per 1,000 PUT, COPY, POST, or LIST requests $0.01 per 10,000 GET and all other requests* * No charge for delete requests

Common AWS features

• Provide application platforms, including

resources

• Accessed over the web • Simple to use

• Easy to get started (just need a credit card) P

• Pay-per-use

• No contracts (committing to future use)

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Cloud attractions

 Cost – especially for peaks

 Flexibility; rapid scalability and de-scalability

 Data replication

 Easier cross-institution collaboration

 Any {time, place, device} access via web

b browser

 Alternative if departmental or central IT

non-responsive

 Priorities: no need to focus on commodity IT

 Future of computing

Cloud concerns

 Loss of control

 Integration: enterprise & federated

authorization

 Interoperability: with key enterprise apps

 Accessibility and user interface limitations of

web apps web apps

 Reliability, performance, security

 Offline access

 Features; changes; vendor lock-in

 Policy/compliance concerns (privacy)

 Business “surprises”; Support; More Logins

How? Virtual machines

-Abstracts hardware

Grid technology

-Distributed virtual resource

Business Service Management

- Dynamic SLA management

Autonomic systems

Cloud Computing 2015 –

Virtual infrastructure for future service delivery

What?

•Large-scale IT capacity

•Compute + storage + network

•Automaticly increase, decrease & migrate •Large-scale management Federated  clouds Service Provider Infrastructure id Internal infra‐ structure Service Provider Bursted internal clouds

B

ASIC DEPLOYMENT SCENARIOS Infrastructure Infrastructure Infrastructure Provider Service Provider Multi‐clouds Provider Provider Infrastructure Provider Infrastructure Provider Infrastructure Provider Infrastructure Provider Broker

Cloud Resource Management

What? For whom?

•Service providers •Infrastructure providers

What?

•Compute + storage + network •Low and high level management

How?

•Single abstraction – multiple use (scenarios) •General tools for key functionality •Flexibility in deployment and configuration

Example (low level management):

Elasticity- & access control

Elasticity control

•Control system handling peaks &lows •Inceasing ability to meel SLAs •Reduces resource consumption consumption Access control •Overbooking of elastic services

•Access control quality directly determines income and SLA violation rate

Holistic Cloud management

Business Level Objectives

Management constraints

Algorithms

Policies AlgorithmsPolicies AlgorithmsPolicies AlgorithmsPolicies AlgorithmsPolicies AlgorithmsPolicies

UmU cloud research

– additional examples

Create cloud infrastructure

• Architectures and software for cloud and grid systems

• Methods for improving resource utilization • Monitoring & accounting

• Self-management, self-optimization • Algorithms for scheduling and elasticity • Algorithms for scheduling and elasticity • Algorithms for efficient VM migration Use cloud infrastructure

• Basic unbderstanding of how to develop software to be run on elastic infrastructure

• Tools to create and run cloud services • Test and development jointly with end users

Migrating large virtual machines Why migration?

Server consollidation, cloud optimization, resource management, elasticity control, etc Basic algorithm for live migration:

1. Transfer all memory pages

Er ik El m ro th el m ro th@ cs .um u. se Er ik El m ro th el m ro th@ cs .um u. se 2. Repeat:

2.1 Transfer all pages being “dirtied” during migration process

3. Suspend VM

4. Transfer remaining pages 5. Researt VM on destination host

Time from sustpend to restart is downtime. VM unavailable during this time

Challenge

If memory pages are dirtied rapidly relative to transfer time

• VM suspended during extended time • Network connection timeouts • Services on the VM crashed

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• Services on the VM crashed

Likely to happen for VMs with “busy” memory access patterns

Solution: page caching and delta compression

• Transfer only difference between current and perviously trnsferred version

• Optimize page order for transfers

Demo (effect of delta compression)

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International collaborations

EU FP7 IP. Introduced federated clouds. EUs first major cloud project.

EU FP7 IP. Optimized cloud services over complete lifecycle. Non-functional aspects. EU FP7 IP. Pioneering federated storage clouds. Raised level of abstraction Media- and telecom applications

UMIT

Research Lab

level of abstraction. Media and telecom applications. Governments strategic efforts. Methods and software for eScience applications.

Umeå initative for innovation and industry benefits within simulation, visualisation, computation and infrastructur. Key partners: IBM Haifa Research Labs, SAP Research, ATOS Orgin, Universidad Complutense de Madrid, Leeds University, Barcelona Supercomputer Center, Telefonica I+D, and British Telecom

Next generation infrastructure for service delivery – Federation of clouds

– Leverage migration – enable migration

– Service definition, automati QoS, monitoring, aaccounting/billing – Open specifications

RESERVOIR (EU FP7)

”Resources and Services Virtualization without Barriers”

•UMU and 12 partners •IBM, Israel (coord)

•Telefonica, Spain

•SAP, Ireland + Israel

•SUN Microsystems, Germany

Th l F

p p

– For diverse underlying technology (e.g., virtualization technology)

Infrastructure provider Infrastructure provider Infrastructure provider Service provider •Thales, France •Elsag-Datamat, Italy

•Global Grid Forum

•5 academic (Spain, Italy, UK, Belgium, Schweiz)

•Budget: 17 (10) M Euro

•Duration: 2008-02 - 2011-03

The Reservoir Architecture

Service Manager Service Provider

SLA SLA

SD+ SLA

• Monitors service and enforces SLA compliance by managing capacity of Service Components (VEEs) or/and size of Service Tiers • Deals with mapping of service

metrics (response time) to infrastructure metrics (VEE size)

(SM)

Infrastructure Provider = Site/Domain/Cloud VEE Management System

VEE Management Enablement Layer

Virtualized Physical Resource (e.g., Hypervisor) infrastructure metrics (VEE size)

• Monitors VEEs and finds best VEE placement

• Deals federation of domains

VEE = Virtual Execution Environment (VEEM)

The Reservoir Architecture

Service Manager Service Provider SLA SLA SD+ SLA (SM) Clear separation of concern &

delegation of responsibility, e.g., • SM unaware of placement

(local & remote)

• Primary VEEM takes the role of

Infrastructure Provider = Site/Domain/Cloud VEE Management System

VEE Management Enablement Layer

Virtualized Physical Resource (e.g., Hypervisor)

VEE = Virtual Execution Environment (VEEM)

y

an SM towards remote site • Remote VEEM sees no

difference between local SM and remote VEEM

Service Applications on Reservoir

One multi-VEE

application on:

– One VEE host – Multiple VEE hosts – Multple sites

SM may specify placement constraints, e.g.,

– When physical nearness is needed

– For redundancy – Various types user

requests

The Evolution of the Power Grid

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The Pearl Street Station

•Make your own infrastructure •Not the company’s main

business but a considerable competitive advantage

•The utility industry •Metering •Limited reach

•Efficient distribution •Federation of providers •The diversity factor •Economies of scale

http://www.anl.gov/Media_Center/logos22-1/electThe US National Power Grid

The Evolution of the

Compute

Grid

R E S E R V O I R

“…will move towards a mix of microproduction and

large utilities, with increasing numbers of small-scale

producers co-existing with large-scale regional

•Make your own infrastructure •Not the company’s main

business but a considerable competitive advantage

•Efficient distribution •Federation of providers •The diversity factor •Economies of scale

http://www.by-star.net/techspeak/datacenter/

http://www.smcplus.com/applications.asp?id=32

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producers, and load being distributed among them dynamically…”

There’s Grid and then thar Clouds - Ian Foster

•The utility industry •Metering •Limited reach

Create an eco‐system for cloud infrastructure

OPTIMIS (EU FP7)

Scenario 2013+:Most companies use private and public clouds in combination

Internal infra‐ structure Service provider Infrastructure provider Infrastructure provider Infrastructure provider Infrastructure provider Broker Service provider

Create an eco‐system for cloud infrastructure

– Self‐management – Self‐optimization – Risk assessment

Internal 

OPTIMIS (EU FP7)

Scenario 2013+:Most companies use private and public clouds in combination

•UMU (scientific coordinator) and 11 partners:

•ATOS Origin, Spanien (coord)

•British Telekom, UK

Cloud providers’ eco‐system

– Programming  model – Service  composition Construction Risk assessment – Energy efficiency –Data management operation Also:   –Multi‐clouds –Federated clouds – License management –Energieffektivitet – User locality External  operation – Risk – Trust – Eco‐aspects – Cost (Economy) Deployment  optimization British Telekom, UK •SAP, Irland •Fraunhofer, Germany •Flexiscale, UK •451Group, UK

•5 akademiska (Spain, Italy, UK, Belgium, Schweiz)

•Budget: 10 (7) M Euro

OPTIMIS

 

MAJOR OUTCOMES AND BENEFICIARIES

Validation scenarios:

•Programming model validation 

through lifecycle management of  

on‐demand ERP/CRM services

•Extended elasticity via transparent 

cloud bursting

•Cloud brokerage and federation 

involving many cloud providers

Major outcomes: Key beneficiaries: •Service Providers  •Infrastructure Providers Additional stakeholders: •Brokers

•Independent software vendors

•Service consumers (end‐users)

Major outcomes:

•OPTIMIS Toolkit

•Tools for construction, deployment,  operation

•General base toolkit for trust, risk, cost  and eco aspects

•Reference architectures and guidelines for  (bursted) internal clouds, multi‐clouds and  federated clouds

•Showcase results through business driven  validation scenarios

•Market predictions, business models and 

legal guidelines  Infrastructure Provider Service Provider

Construction phase Deployment phase Operation phase

Deployment  Optimizer OPTIMIS Base  Toolkit OPTIMIS Base  Toolkit Deployment  Optimizer OPTIMIS Base  Toolkit OPTIMIS Base  Toolkit Admission  Control OPTIMIS Base  Toolkit OPTIMIS Base  Toolkit Admission  Control OPTIMIS Base  Toolkit OPTIMIS Base  Toolkit Service Optimizer OPTIMIS Base  Toolkit OPTIMIS Base  Toolkit Service Optimizer OPTIMIS Base  Toolkit OPTIMIS Base  Toolkit Cloud Optimizer OPTIMIS Base  Toolkit OPTIMIS Base  Toolkit Cloud Optimizer OPTIMIS Base  Toolkit OPTIMIS Base  Toolkit Construction  Optimizer Configuration  Manager Configuration  Manager Programming  Model and IDE Programming  Model and IDE DC_OSLO DC_WARSAW DC_HAIFA DC_ATHENS

VISION (EU FP7)

Infrastructure for reliable and effective delivery of data-intensive storage services, facilitating the convergence of ICT, media and telecommunications

DC_MADRID DC_BERLIN

DC_PARIS

DC_LONDON DC_ROME

•UMU and 14 partners: •IBM, Israel (coord)

•Deutche Welle, Germany

•RAI, Italy

•Telenor, Norway

•Siemens, Germany

•France Telecom, France

Technology Innovations

–Raise Abstraction Level of Storage: objects with user-defined and system-user-defined metadata

–Data Mobility and Federation: enable comprehensive data migration and interoperability across remote locations

–Computational Storage: technology for specifying and executing computations close to storage

–Content-Centric Storage: facilitate access to data by content and its relationships

–Advanced Capabilities for Cloud-based Storage: support delivery of data-intensive services securely, at the desired QoS, at competitive costs

Validation Scenarios –Media –Telco –Healthcare –Enterprise •SAP, Germany •Telefonica, Spain

•Engineering SPA, Italy

•ITricity, Netherlands

•Storage Networking Industry Assoc, Europe

•3 academic (Italy, Sweden, Greece)

•Budget: 16 (9) M Euro

•Duration: 2010-10 - 2013-09

VISION Cloud VISION

photoSharing

socialnetworking

Computational storage enables an application’s performance sensitive computations to be performed close to the storage

VCusr1 VCusrn VCusr1 VCusrn Document Sharing VC_User VC_User VC_User

Rich metadata enables sharing of objects across applications Content centric access enables

each application to have its own view of the storage

Mobility and interoperability allow users to change providers and to have storage at multiple providers

Advanced capabilities for cloud-based storage ensure secure access and quality of service

eSSENCE – method development for eScience • Governments strategic

efforts (VR)

• Research on methods development for eScience • Our focus

– Methods and frameworks for d d l d f

•UMU and 2 partners: •Uppsala University (coord)

•Lund University Er ik El m ro th el m ro th@ cs .um u. se

grid and cloud infrastructure – Methods and tools for

applications

•Lund University

•Budget: 102 M SEK

•Duration: 2010 - 2014

UMIT Research Lab

Methods and tools

The parallel revolution

Dynamic scalable IT infrastructure

Foundation of excellent basic research

Engineering research

Interdisciplinary challenges

Industrial applications and inno tion

•Interdisciplinärt forskningslab vid UmU

•Datavetenskap

•Fysik

M t tik

Problem Model Simulation Results

Optimization Computation IT Infrastructure Hardware & software Visualization & interaction innovation •Matematik

•Tillämpad fysik o elektronik

•Budget: 40 M SEK (flertal finansiärer)

•Duration: 2009 - 2015 (to be extended)

•Physical location: MIT building 2nd_floor

Senior researchers Project coordinators

Erik Elmroth, Professor Francisco Hernandez, Assistant professor Johan Tordsson, Assistant professor Lei Xu,

Post Doc Lennart Edblom, PhLic Christina Igasto PhD PhD Students Ahmed

Ali-Eldin Daniel Henriksson Ewnetu Bayuh Lakew

Lars

Larsson Wubin Li Mina Sedaghat Petter Svärd P-O Östberg

Others Tomas Ögren, Systems expert Sebastian Gröhn, Research assistant Marcus Karlsson Research assistant Mikael Öhman, Research assistant www.cloudresearch.se