9/14/2011

Yrjö Raivio

Aalto University, School of Science

Department of Computer Science and Engineering

Data Communications Software

Email: yrjo.raivio(at)aalto.fi

Course email: t-110.5121(at)tkk.fi

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T-110.5121 Mobile Cloud Computing

Basics

14.09.2011

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• Definition

• Key benefits

• Technologies

• Challenges and opportunities

• Conclusions

Outline

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Lecture is based on the Virtual Keynote by Doug Terry: ”Technology in the Cloud – Plus

some Challenges and Opportunities”, Microsoft Research, June 1, 2011, available from

http://techpack.acm.org/cloud/

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Definition

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“Mobile Cloud computing is a model for enabling convenient, on-demandmobile network access to a shared pool of configurable mobilecomputing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.”

Adapted from: P. Mell and T. Grance, “The NIST Definition of Cloud Computing”, 2009

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What is cloud computing

1. The illusion of infinite computing resources available on demand, thereby eliminating the need for Cloud Computing users to plan far ahead for provisioning.

2. The elimination of an up-front commitment by Cloud users, thereby allowing companies to start small and increase hardware resources only when there is an increase in their needs.

3. The ability to pay for use of computing resources on a short-term basis as needed (e.g., processors by the hour and storage by the day) and release them as needed, thereby rewarding conservation by letting machines and storage go when they are no longer useful.

Source: Ambrust et al, ”Above the Clouds: A Berkeley View of Cloud Computing”, 2009

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• ”The horse is here to stay,

but the automobile is only a

novelty - a fad”, President of

the Michigan Savings Bank,

1903

• “..we expected to get orders

for five machines, we came

home with orders for 18.”,

Thomas Watson, Jr., April 28,

1953

• “There is no reason for any

individual to have a personal

computer in their home.”

Ken Olsen, President, Digital

Equipment Corp., 1980

History

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Sources: Joe Sherlock, The View Though The Windshield, available at: http://www.joesherlock.com/nwsltr1.html ;

Rolf Harms and Michael Yamartino: The Economics of the Cloud, Nov. 2010.

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• “The interesting thing about Cloud Computing is that we’ve redefined Cloud Computing to include everything that we already do. . . . I don’t understand what we would do differently in the light of Cloud Computing other than change the wording of some of our ads.”

Larry Ellison, Wall Street Journal, September 26, 2008

• “It’s stupidity. It’s worse than stupidity: it’s a marketing hype campaign. Somebody is saying this is inevitable — and whenever you hear somebody saying that, it’s very likely to be a set of businesses campaigning to make it true.”

Richard Stallman, The Guardian, September 29, 2008

Some skepticism

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Key benefits

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Economies of scale

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Source: Rolf Harms and Michael Yamartino: The Economics of the Cloud, Nov. 2010.

• Cheaper MIPS (5-7 times)

• Better utilization of computing resources (5-10% to 60-80%)

• Multi-tenancy: one instance can serve several customers

• Less admin people per server (from 1:100 up to 1:10 000)

• Worth 1$ IT requires 8$ admin costs

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Elasticity

Source: Ambrust et al, Above the Clouds: A Berkeley View of Cloud Computing, Feb 2009

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Resource planning

• Resources can be optimized to meet service needs

• Service integration time can be shortened, example SMSC setup from 2 weeks to 4 minutes

Time

Turnover

Private

cloud

Public

cloudHybrid

cloud

Early

development

Exponential

growth

Mature

market

Source: Rolf Harms and Michael Yamartino: The Economics

of the Cloud, Nov. 2010.

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Pay-as-you-go

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• Avoid high upfront investment, avoid risk

• Adapt to changing business

• Buy or lease?

• Amortizise value to investment period

• Equated Monthly Installment

• Net Present Value

1)1(

)1(

12

1212

mr

mrr

m EA

Er

S

r

CPNPV

N

N

T

TT

)1()1(0

Private cloud

Public cloud

a

b

F (cost)

Source: TeliaSonera

b a

b

dtyfByfA0

)()(

• Minimize

• www.cloudonomics.com

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Realism – Case SMSC

Source: Y. Raivio, O. Mazhelis, K. Annapureddy and P. Tyrväinen, Hybrid Cloud Architecture for

Short Message ServicesSAC2012, submitted for approval

• Granularity

• Public cloud charging based on full hours

• Startup takes ~ a minute

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Always available

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• Anyone, anytime, anywhere

• High availability?

• Amazon Elastic Compute Cloud (EC2)

• 99.95% = max 4 h 23 min down time per year

• Telecom

• 99.999% = 5 min

• Availability Zone, fully (?) independent computing systems

• Using two EC2 Availability Zones

%9999.99)1(11 22APP FP

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Everything as a Service

SaaS (Software as a Service)

– Ready to deploy application

– Salesforce, Gmail, SMS, voice

PaaS (Platform as a Service)

– No system administration

– Simplified development

– Scaling is provided by the PaaS framework

– Google Apps Engine, Microsoft Azure, Force.com

IaaS (Infrastructure as a Service)

– Computers owned by the cloud provider

– No hardware management issues

– Dynamic scaling of resources through virtualization

– Billing is calculated by usage only

– Amazon EC2

Sim

plicit

y

Evo

luti

on

To

tal

mark

et

40 B

€(2

011)

70

% S

aa

S&

Pa

aS

-3

0%

Ia

aS

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Technologies

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• Drivers

• Better utilization of HW (from 15% to 80%), saves energy and money

• Reduces system admin work

• Easier SW installation

• Hypervisors (VM manager): Xen, KVM (Kernel based VM), VMware

• Full (complete HW simulation), Para (interface between OS and HW) and HW-assisted virtualization

Virtualization

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APP

1

OS

CPU

APP

N

CPU

2

Source: Z. Ou, Virtualization Technology, T-110.7100, Autumn 2010.

..

OS

CPU

APP APP

1

OS

APP

N..

CPU

1

Hypervisor

Virtual Machines

..

Single task Multi task Hyper threading Virtualization

APP

1

OS-1

APP

N.. APP

1

OS-N

APP

N..

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• No SQL, Not only SQL

• A family of databases for implementing (large) storage in the cloud

• Google BigTable, Amazon SimpleDB, LinkedIn Voldemort

• Good for web-scale data and analytics, not so great for transaction processing

• Data model not relational, rather a key-value store

• Scalable by nature

• ACID (Atomicity, Consistency, Isolation, Durability) relaxed in favor of BASE (Basically Available, Soft state, Eventually consistent)

• Challenges: interoperability, lock-in problem, data lifecycle, small data

Storage

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Source: R. Paivarinta and Y. Raivio, ”Performance Evaluation of NoSQL Cloud Database

in a Telecom Environment”, Closer 2011.

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Telecommunication Application

Transaction Processing (TATP)• Originally developed in

2003 to test HLRs based on SQL databases

• Simulates load on HLR database

• Ported for HBase, four tables denormalized into one adding redundancy

• 80% reads, 20% writes

Transaction

name

Type % Tables

Get-Subscriber-

Data

Read 35 Subscriber

Get-New-

Destination

Read 10 Special Facility,

Call Forwarding

Get-Access-

Data

Read 35 Access Info

Update-

Subscriber-Data

Write 2 Subscriber,

Special Facility

Update-Location Write 14 Subscriber

Insert-Call-

Forwarding

Write 2 Call Forwarding

Delete-Call-

Forwarding

Write 2 Call ForwardingHLR

MSC

Client N

MSC

Client 2

MSC

Client 1

…

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Measurement results

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Service Level Agreement (SLA)

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Source: M. Murphy, ”Telco Clouds” [presentation], Cloud Asia 2010

• Research topics:

• Availability

• Latency

• Throughput

• Availability alone not enough

• Telecom users require more specific SLAs

• Sustainability?

• Penalties from violation?

• Monitoring tools important SLA Carrier grade 6 EC2 Large VMs

Availability 99.999 %99.95 % one zone

99.9999 % two zones

Latency < 150 ms < 50 ms (EU zone)

Throughput > 1000 msg/s >1000 msg/s

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Measurement results

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Source: R. Paivarinta and Y. Raivio, ”Performance Evaluation of NoSQL Cloud Database

in a Telecom Environment”, Closer 2011.

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Programming models

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• High data volumes

• Facebook: 1 PB, 2-3 TB added every day

• Web: 100 billion web pages -> 400-500 TB compressed

(duplicated across several clusters)

• eBay: 6.5 PB, 50 TB added every day

• Bottleneck in data transfer speeds (reads/writes from/to disks)

• Unlike disk capacity, bandwidth improves linearly

• Solution: read parallel from multiple disks

• How to change sequential programming to parallel

• Fault tolerance, Automated program partitioning

T = 1012

P = 1015

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MapReduce

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• Google introduced MapReduce 2003/2004

• Hides from a programmer complexity of parallelization, fault-tolerance, data distribution and load-balancing

• Principle

• Iterate over a large number of records

• Extract something of interest from each (MAP)

• Shuffle and sort intermediate results

• Aggregate intermediate results (REDUCE)

• Generate final output

Source: Denis Shestakov, Cloud Computing seminar 24.9.2010

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Traditional IT vs. Iaas, PaaS, SaaS

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Source: Rolf Harms and Michael Yamartino: The Economics of the Cloud, Nov. 2010.

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• Bandwidth needed for VM migration

• Delay, jitter

• Location independent

Communications

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Source: Ambrust et al, ”Above the Clouds: A Berkeley View of Cloud Computing”, 2009

Bandwidth bottleneck: 1 TB drive, 1 Gbit/s I/O = 2 h 13 min

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• Static

• Challenges

• Dynamics

• Machine learning

• Lack of APIs for monitoring and control

• Go-scheduling (computation close to data,

simultaneous computation)

• Analytics tools a hot topic

Provisioning and monitoring

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• Power Usage Efficiency (PUE) defined as:

• Typical PUE 2-3, state of the art 1.7

• 15% of all costs

• 59% IT equipment

• 33% cooling

• 8% power loss

• Power off idle machines

• Raise temperature

• Cold location, cheap energy (for example Finland)

• More fine-grain accounting

• Better algorithms

• E2E model required including public clouds

Power management

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tPowerITEquipmen

ityPowerTotalFacilPUE

Source: A. Greenberg, J. Hamilton, D.A. Maltz abd P. Patel, The Cost of a Cloud: Research

Problems in Data Center Networks, ACM SIGCOMM Computer Comm. Review, Jan 2009.

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Privacy, Security and Trust

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Threat Description Target

Abuse and Nefarious Use of Cloud

Computing

Abusing anonymity for

spamming, malicous code

launch etc.

IaaS,

PaaS

Insecure Interfaces and APIs Anonymous logging, miuse of

resources

All

Malicious Insiders Misuse of internal information All

Shared Technology Issues Misuse of computation

resources

IaaS

Data Loss or Leakage Misuse of data All

Account or Service Hijacking Eavesdrop business All

Unknown Risk Profile Lack of control All

Source: CSA, Top Threats to Cloud Computing V1.0, March 2010

Other topics: regulation, privacy, service reputation management

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Public

cloud

Private

cloud

Telecom Cloud

SaaS

PaaS

IaaS

Support Systems

(MVNO/BSS)

Service Delivery

(SMSC)

Storage (HBase)

Computation (HLR)

Communication

Open Telco

SaaS

PaaS

IaaS

SaaS

PaaS

IaaSHybrid

Cloud

Eucalyptus

OpenStack

OpenNebula

Mobile Cloud Computing framework

Amazon EC2

End users

Adhoc

Cloud Mobile

Offloading

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• Mobile access to cloud

• Using mobile context to enhance cloud based services

• Adhoc mobile cloud

• Mobile offloading: moving computation or/and data to cloud

• Already third of Facebook accesses from mobile

Mobile client

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Mobile offloading

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Source: B.-G. Chun and P. Maniatis, ”Augmented

Smartphone Applications Through Clone Cloud

Execution”, HotOS 2009.

Source: Kumar & Lu, ”Cloud Computing for Mobile Users:

Can Offloading Computation Save Energy ”, 2010

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Conclusions

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Challenges and opportunities

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Source: Ambrust et al, ”Above the Clouds: A Berkeley View of Cloud Computing”, 2009

# Challenge Opportunity

1 Availability of Service Use Multiple Cloud Providers

2 Data Lock-In API standardization

3 Data Confidentiality and Auditability Deploy Encryption, VLANs, and Firewalls

4 Data Transfer Bottlenecks Higher Bandwidth LAN Switches

5 Performance Unpredictability Flash Memory

6 Scalable Storage Invent Scalable Store

7 Bugs in Large-Scale Distributed

Systems

Invent Debugger that relies on Distributed

VMs

8 Scaling Quickly Invent Auto-Scaler

9 Reputation Fate Sharing Offer reputation-guarding services

10 Software Licensing Pay-for-use licenses

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•Remote and shared computing over the Internet

•Consists of components that communicate through APIs

!

•Simple architecture

•Efficient usage of CPU (>50%)

•Scalability

•Load balancing

•Low capex

•High availability

?

•Security & Privacy

•High usage of certain CPUs

•Interoperability

•Vendor lock-in

•High opex

•SLA critical

Pros and Cons

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Questions?

Contacts:

Teacher: yrjo.raivio(at)aalto.fi, A122

Assistants: ramasivakarthik.mallavarapu(at)aalto.fi, PlayRoom

koushik.annapureddy(at)aalto.fi, A118

Course staff: t-110.5121(at)tkk.fi

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1. Virtual Keynote by Doug Terry: ”Technology in the Cloud – Plus some

Challenges and Opportunities”, Microsoft Research, June 1, 2011, available

from http://techpack.acm.org/cloud/

2. Armbrust, Michael, Fox, Armando, Griffith, Rean, Joseph, Anthony D., Above

the Clouds: A Berkeley View of Cloud Computing, Feb. 10, 2009. Available at:

http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-28.pdf

3. Cloud Software program, D1.1.1 Technical Report: Cloud Computing

Technologies, June 27, 2010, available at:

http://www.cloudsoftwareprogram.org/results/i/26999/1569/d1-1-1-technical-

report-cloud-computing-technologies

4. Lee Badger, Tim Grance, Robert Patt-Corner and Jeff Voas: Draft Cloud

Computing Synopsis and Recommendations, Recommendations of the

National Institute of Standards and Technology, May 2011, available at:

http://csrc.nist.gov/publications/drafts/800-146/Draft-NIST-SP800-146.pdf

5. Rolf Harms and Michael Yamartino: The Economics of the Cloud, Nov. 2010,

available at: http://www.microsoft.com/presspass/presskits/cloud/docs/The-

Economics-of-the-Cloud.pdf

Reading material

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