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Some IT Trends. The Big Switch in Early 21 st Century. Core Technologies for Cloud Computing and Future Internet

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March 6, 2012, Kai Hwang, USC

Core Technologies for Cloud

Core Technologies for Cloud

Computing and Future Internet

Computing and Future Internet

Prof. Kai Hwang

University of Southern California

This talk attempts to address a few critical issues on cloud computing technology, environment, and emerging

new applications in business, science, and society.



What are the desired future Internet architecture and protocolsto achieve much greater bandwidth, efficient packet routing, and enhanced security than today’s Internet ? How to use clouds to boost the global economy ?



What roles clouds can play with a healthy cloud ecosystemto realize

the Internet of Things (IoT), to secure pervasive datacenters, and to make the social networkstrustworthy ?

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March 6, 2012, Kai Hwang, USC

The Big Switch

in Early 21

st

Century

Industries race to build

massive datacenters

(capacity)

High bandwidth network

and pervasive connectivity

Terabit network in sight

Wideband wireless mobility

Virtualization help realize

the economics of scale

2

Some IT Trends

The Data Delugeis clear trend from Commercial (Amazon, e-commerce) , Community (Facebook, Search) to Scientific applications

Light weight clients from smartphones, tablets to sensors Multicore reawakeningparallel computing

Exascale initiativeswill continue drive to high end with a simulation orientation

Clouds with cheaper, greener, easier to use IT

for (some) applications

New jobs associated with new curricula

Clouds as a distributed system(classic CS courses)

Data Analytics(Important theme at SC11)

Network/Web Science

What are the Cloud Computing

Services ?

(Software, Platform, Infrastructure, Data, Storage, Compute, …) as a Service

(SaaS, PaaS, IaaS, DaaD, SaaS, CaaS, ….)

Clients Other Cloud Services Govt. Cloud Services Private Cloud Cloud Manager Public Cloud

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March 6, 2012, Kai Hwang, USC

Cloud Applications

Science and Technical

Applications

Business Applications

Consumer/Social Applications

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March 6, 2012, Kai Hwang, USC

Today’s Cloud Services Stack

Network

Cloud Services

Co-Location

Cloud Services

Compute & Storage

Cloud Services

Platform

Cloud Services

Application

Cloud Services

Cloud Business Potential:

a trillion $ business/year by 2020?

120? 2016 15% 600? 2020? 30% 2 Trillion

Distributed

and Cloud

Computing

Kai Hwang,

Geoffrey Fox,

Jack Dongarra,

published by

Morgan Kaufmann,

Oct. 2011

(648 pages)

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March 6, 2012, Kai Hwang, USC

Cloud Computing

dealing with too

many issues yet to be resolved by ACCTA

Virtual ization QoS Serv ice Le vel Agre emen ts Reso urce Meterin g Billing Pricing Provisioning

on Demand Utility & Risk Management Scalability Reliability Energ y Efficiency Securit y Privacy Trust Legal & Regulatory Software Eng. Complexity Programming Env. & Application Dev.

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March 6, 2012, Kai Hwang, USC

The Google Gmail Example

http://www.google.com/green/pdfs/

google-green-computing.pdf

Clouds win by efficient resource use over datacenters

10 Business Type Number of users # servers IT Power per user PUE (Power Usage effectivenes s) Total Power per user Annual Energy per user Small 50 2 8W 2.5 20W 175 kWh Medium 500 2 1.8W 1.8 3.2W 28.4 kWh Large 10000 12 0.54W 1.6 0.9W 7.6 kWh Gmail (Cloud)

< 0.22W 1.16 < 0.25W < 2.2 kWh

Challenges/Issues

in

Cloud Computing

What Applications

Work better in The Cloud ?

Workflow and Services

Pleasingly parallel applications

of all sorts analyzing roughly independent data or spawning independent simulations including

Long tail of science

Integration of distributed sensor data

Science Gateways and portals

Commercial and Science Data analytics

that can use MapReduce (some of such apps) or its iterative variants (most analytic apps)

Data Analysis requirements

not well articulated in many fields – See http://www.delsall.org for life sciences

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March 6, 2012, Kai Hwang, USC

Example 1:

Game Cloud

built at USC

GamePipe Lab,

No.1 Game program among the to 10

in the USA

(Courtesy of Zhao, Hwang, and Villeta, Feb. 2012 [7])

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March 6, 2012, Kai Hwang, USC

Reduction of Gaming Latency

and

Improvement of the QoS and QoE (Frame Rate)

on The Game Cloud at USC GamePipe Lab. 2012

Major Technological Challenges

in Developing the Future Internet



Programmable Networking Architectures



Fusion of The Internet, Mobile and TV Networks



Named Data Networking beyond the TCP/IP



Personalized communication protocols



Intelligent Routing and Content Distribution



New Ideas for Security and Privacy Protection



Service migration and disaster recovery



Massive data protection beyond encryption

Three Approaches To Building The

Future Internet Architecture



OpenFlow for Programmable Virtual Networking

(Stanford, Princeton, etc., 2008)



Content-Centric Networking (CCN)

: Named Data Networking

(HP Lab, UCLA, etc. 2009)



Service-Oriented Future Internet Architecture

(SOFIA) : Chinese Academy of Sciences,

Institute of Computing Technology (2011)

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March 6, 2012, Kai Hwang, USC

OpenFlow Architecture and Protocol

enable virtual networking, advanced

Forwarding and Programmability

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March 6, 2012, March 19, 2012Kai Hwang, USC Prof. Kai Hwang, USC

OpenFlow for Developing

The Future Internet

Conventional TCP/IP Internet Protocols

The CCN Approach in Named Data Networking

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March 6, 2012, Kai Hwang, USC

(Source: G. Xie, et al, “Service-Oriented Future Internet Architecture (SOFIA)” IEEE Infocom 2011 , Shanghai, China , April 2011 [2])

SOFIA :

Service-Oriented Future

Internet Architecture

under

Development at CAS/ICT

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March 6, 2012, Kai Hwang, USC

Cloud Roles in The Future Internet

Cloud Ecosystem Requirements:



At the system level, the cloud ecosystem

include the cloud platform and infrastructure,

resource management policies, etc.



At the service level, the SLAs, globalized

standards, reputation system, billing and

accounting system, cloud business models, etc.



At the user (client) level, Application

programming interfaces (APIs), cloud

programming environment, Quality of Service

control, etc.

Cloud is built on Massive Datacenters

Range in size from “edge”

facilities to megascale (100K

to 1M servers)

Economies of scale

Approximate costs for a small size center (1K servers) and a larger, 400K server center.

This data center is

This data center is

11.5 times

11.5 times

the size of a football field

the size of a football field

Technology Cost in small-sized Data Center Cost in Large Data Center Ratio Network $95 per Mbps/ Month $13 per Mbps/ month 7.1 Storage $2.20 per GB/ Month $0.40 per GB/ month 5.7 Administra-tion ~140 servers/ Administrator >1000 Servers/ Administrator 7.1

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March 6, 2012, Kai Hwang, USC

Ecosystem for Market-Oriented Clouds

(Source: R. Buyya, et al, “Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivery IT Services as Computing Utilities”, Proc. of HPCC, Sept. 25-27, 2008)

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March 6, 2012, Kai Hwang, USC

Develop methodologies and tools to automate the

process of cloud management in 4 objectives

Autonomic Cloud Management

Capacity Management Resource Management Power Management Autonomic Cloud Management Reliability Management Admission Control Load Balancing 26 1. Manage resources to provisioning of service quality assurance and adaptation 2. Automate the configuration process of VMs and virtual clusters 3. Manage energy consumption under SLA constraints 4. Develop fault prediction models for proactive failure management

MapReduce “File/Data Repository” Parallelism

Instruments

Map = (data parallel) computation reading and writing data Reduce = Collective/Consolidation phase e.g.

forming multiple global sums as in histogram

Disks Map1 Map2 Map3

Reduce Communication

Portals /Users MPI or Iterative MapReduce

Map Reduce Map Reduce Map

Example 2

: MapReduce Skyline

Composition of Web Services

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March 6, 2012, Kai Hwang, USC

Reduction of Web Service Composition Time

from 929 ms to 220 ms using fewer Skyline representatives

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March 6, 2012, Kai Hwang, USC

Amazon’s Lesson

Down for 3 days

since

4/22/2011

1000x of businesses

went offline. e.g.

Pfizer, Netflix, Quora,

Foursquare,Reddit,….

SLA contract

99.95% availability

(< 4.5 hour down

10% penalty,

otherwise

Architecture of The Internet of Things

Merchandise Tracking Environment Protection Intelligent Search Tele-medicine Intelligent Traffic Cloud Computing Platform Smart Home Mobile Telecom Network The Internet Information Network RFID RFID Label Sensor Network Sensor Nodes GPS Road Mapper Sensing Layer Network Layer Application Layer

Cloud Support in Internet of Things

Cloud Support in Internet of Things

and Social Network Applications

and Social Network Applications

1. Smart and pervasive cloud applications for individuals, homes, communities, companies, and governments, etc.

2. Coordinated calendar, itinerary, job management, events, and consumer record management (CRM) services

3. Coordinated word processing, on-line presentations, web-based desktops, sharing on-line documents, datasets, photos, video, and databases, content distribution, etc.

4. Deploy conventional cluster, grid, P2P, social networking applications in the cloud environments, more cost-effectively. 5. Earthbound applications that demand elasticity and parallelism to

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March 6, 2012, Kai Hwang, USC

Example 3:

Internet of Things: Sensor Grids

A pleasingly parallel example on Clouds

A sensor(“Thing”) is any source or sink of time series In the thin client era, smart phones, Kindles, tablets, Kinects, web-cams are sensors

Robots, distributed instruments such as environmental measures are sensors

Web pages, Googledocs, Office 365, WebEx are sensors Ubiquitous Cities/Homes are full of sensors

They have IP address on Internet

Sensors – being intrinsically distributed are Grids

However natural implementation uses clouds to consolidate and control and collaborate with sensors

Sensors are typically “small” and have pleasingly parallel cloud implementations

33

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March 6, 2012, Kai Hwang, USC

Sensors as a Service

(SaaS)

Sensors as a Service Sensor Processing as a Service (MapReduce)

A larger sensor ………

Output

Sensor

Research on Cloud Security

Trusted Zones for VM Insulation

Tenant #2 APP OS APP OS Virtual Infrastructure Physical Infrastructure Cloud Provider APP OS APP OS Virtual Infrastructure Tenant #1 Insulate information from cloud providers’ employees Insulate information from other tenants Insulate infrastructure from Malware, Trojans and cybercriminals Segregate and control user access Control and isolate VM in the virtual infrastructu re Federate identities with public clouds Identity federation Virtual network security Access Mgmt Cybercrime intelligence Strong authentication Data loss prevention Encryption & key mgmt Tokenization

Enable end to end view of security events and compliance across infrastructures Security Info. &

Event Mgmt GRC

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37

March 6, 2012, Kai Hwang, USC

Clouds and Grids/HPC

Synchronization/communication Performance

Grids > Clouds > HPC Systems

Clouds appear

to execute effectively Grid workloads but

are not easily used for closely coupled HPC applications

Service Oriented Architectures

and

workflow

appear to

work similarly in both grids and clouds

Assume for immediate future, science supported by a

mixture of

Clouds – data analysis

(and pleasingly parallel)

Grids/High Throughput Systems

(moving to clouds as

convenient)

Supercomputers

(“MPI Engines”) going to exascale

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March 6, 2012, Kai Hwang, USC

Service-Oriented Architecture (SoA)

Database S S S S S S SS S S S S Sensor or Data Interchange Service Another Grid

Raw Data  Data  Information  Knowledge  Wisdom  Decisions

S S S S Another Service S S Another Grid S S Another Grid SS SS SS SS SS SS SS Storage Cloud Compute Cloud S S SS SS S S Filter Cloud Filter Cloud Filter Cloud Discovery Cloud Discovery Cloud Filter Service fs fs fs fs fs fs Filter Service fs fs fs fs fs fs Filter Service fs fs fs fs fs fs Filter Cloud Filter Cloud Filter Cloud Filter Service fs fs fs fs fs fs Traditional Grid with exposed services

Geoffrey Fox: Grid of various clouds -- from Raw Data to Wisdom.

SS = Sensor service, fs= filter services

Conclusions :



At present, cloud technology is still far from being mature and user-friendly. Cloud industralization demands first to upgrade of our computer science/engineering education significantly.



Interesting IoT applications must leverage the clouds to store and process massive amounts of data, that are changing rapidly on a daily basis. With storming and distrustful clouds, the IoT will be a just dream which will never become true.



Clouds, IoT and social networks are rapidly changing our world, reshaping all human relationships, affecting our daily life, and impacting the global economy and political system. We have to be ready to face the new challenges, like it or not !

To Probe Further :

1. K. Hwang, G. Fox, and J. Dongarra, Distributed and Cloud Computing,

Morgan Kauffmann Publishers, Oct. 2011

2. N. Mckeown, et al “OpenFlow: Enabling Innovation in Campus Networks”, SIGCOMM CCR, Vol. 38, No.2 pp.69-74, 2008

3. G. Xie, et al. “Service-Oriented Future Internet Architecture (SOFIA)” IEEE Infocom, Shanghai, China, April 2011

4. R. Buyya, C. S. Yeo, and S. Venugopal, “Market-Oriented Cloud Computing: vision, Hype, and Reality for Delivery IT Services as Computing Utilities”,

Proc. of IEEE Int’l Conf. on HPCC, Dalian, China, Sept. 25-27, 2008

5. K. Hwang and D. Li, “Trusted Cloud Computing with Secure Resources and Data Coloring”,IEEE Internet Computing, Sept. 2010.

6. L. Chen, K. Hwang, and J. Wu, “MapReduce Skyline Composition of Web Services in Inter-Cloud Applications”, IEEE Trans. Parallel and Distributed Systems,

(TPDS) submitted under review, 2012.

7. Z. Zhao, K. Hwang and J. Villeta, "GamePipe: A Virtualized Cloud Platform Design and Performance Evaluation",ACM Third Workshop on Scientific Cloud Computing (ScienceCloud2012), submitted and under reviewing, 2012.

References

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