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School of Computing ScienceSimon Fraser University

CMPT 880: Large-scale Multimedia Systems and Cloud Computing

Introduction

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Course Objectives

Understand basics of multimedia systems & cloud computing

Know current research issues in these areas

Develop research skills - Reading papers, presentation skills, research

discussion, finding project ideas, code development, and writing

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Course Info

Course web page http://nsl.cs.sfu.ca/teaching/13/880/

References- Mostly research papers and book chapters

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Course Info: Grading

Class participation and Assignments: 50%- Present one topic, from chapter(s)/paper(s)- Read all Mandatory Reading and participate in

discussion- Few assignments and quizzes

Final Project: 50%- New Research Idea (publishable A+)- Implementation and evaluation of an already-

published algorithm/technique/system (Good demo A+)

- Quantitative comparisons between two already-published algorithms/techniques/systems

- A survey of a topic- …

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Course Info: Topics

Introduction- Overview of clouds and multimedia systems- Video coding basics

Cloud computing - Datacenter design - Virtualization- Storage systems- Programming models

Cloud support for multimedia systems Mobile multimedia clouds

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Multimedia Systems

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Definitions and Motivations

“Multimedia” is an overused term- Means different things to different people- Because it touches many disciplines/industries

• Computer Science/Engineering• Telecommunications Industry• TV and Radio Broadcasting Industry• Consumer Electronics Industry• ….

For users - Multimedia = multiple forms/representations of

information (text, audio, video, …)

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Definitions and Motivations

Why should we study/research multimedia topics?

Huge interest and opportunities- High speed networks - Powerful (cheap) computers (desktops … cell

phones)- Abundance of multimedia capturing devices

(cameras, speakers, …)- Tremendous demand from users (mm content makes

life easier, more productive, and more fun)

- Here are some statistics …

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Some video statistics

Growth of various video traffic [Cisco 2008]- Video traffic accounted for 32% of Internet traffic in 2008 and

is estimated to account for 50% in 2012

- Y-axis in Petabytes (1000 Terabytes) per month.9

2006 2007 2008 2009 2010 2011 20120

2000

4000

6000

8000

10000

12000

14000

Internet Video to PCInternet Video to TVNon-Internet Consumer Video

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Multimedia:The Big Picture [SN04]

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QoS in Networked Multimedia Systems

Quality of Service = “well-defined and controllable behavior of a system according to quantitatively measurable parameters”

There are multiple entities in networked multimedia system- User- Network- Local system (memory, processor, file system, …)

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QoS in Networked Multimedia Systems

Different parameters belong to different entities QoS Layers

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QoS Layers

User

Application

System

Local Devices Network

Perceptual(window size, security)

Media Quality(frame rate, adaptation rules)

Traffic(bit rate, loss, delay, jitter)

Processing(CPU scheduling, memory, hard drive)

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QoS Layers

QoS Specification Languages- Mostly application specific- XML based- See: Jin & Nahrstedt, QoS Specification Languages for

Distributed Multimedia Applications: A Survey and Taxonomy, IEEE MultiMedia, 11(3), July 2004

QoS mapping between layers- Map user requirements to Network and Device requirements- Some (but not all) aspects can be automated- For others, use profiles and rule-of-thumb experience- Several frameworks have been proposed in the literature- See: Nahrstedt et al., Distributed QoS Compilation and Runtime

Instantiation, IWQoS 2000

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QoS Layers

QoS enforcement methods- The most important/challenging aspect- How do we make the network and local devices implement the

QoS requirements of MM applications?

We need to - enforce QoS in Network (models/protocols)- enforce QoS in Processor (CPU scheduling for MM)- When we combine them, we get end-to-end QoS

Notice:- If not enough resources, we have to adapt (or scale) the MM

content (e.g., use smaller resolution, frame rate, drop a layer, etc)

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Cloud Computing

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Cloud Computing

• “Cloud Computing” … fuzzy term

– Some argue it is just rebranding of old stuff

– Others see it as revolutionary technology that will transform everything in computing

– Truth … somewhere in between

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Cloud Computing: Vision

• Goal … achieve the old dream for computing

Make computing a utility

• Similar to electricity & water– we (customers) do not worry about design, operation,

maintenance of power plants, nor do we think about power transmission systems

– Home users simple requirements, e.g., lighting – Industries complex requirements, e.g., high voltage – … and we pay as we consume

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Cloud Computing: NIST Definition

“Cloud computing is a model for enabling ubiquitous,

convenient, on-demand network access to a shared pool of

configurable computing resources (e.g., networks, servers,

storage, applications, and services) that can be rapidly

provisioned and released with minimal management

effort or service provider interaction.”

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Cloud Computing: Service Models

• IaaS (Infrastructure as a Service)– Basic computing resources (CPU, storage, network, …)– Amazon EC2

• PaaS (Platform as a Service) – Platform to develop apps using programming languages, libraries,

services, and tools supported by the cloud provider– Windows Azure, Amazon EMR (Elastic MapReduce)

• SaaS (Software as a Service)– Software apps provided by the cloud provider– SalesForce.com (e.g., payroll, customer relation management, …)

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Cloud Computing: Why Now?

• Better Internet & Mega Datacenters– Internet: faster, prevalent, and more reliable – Mega Datacenters:• economy of scale (5—7x cheaper hardware than

medium size companies)• Already deployed (Amazon AWS, Google, …)

Additional revenue stream• Already developed software for in-house use (e.g.,

Google File System, MapReduce)

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Cloud Computing: Why Now? (2)

• New technology trends and business models– Shifting from high-touch, -margin, -commitment to

low-touch, -margin, -commitment service• E.g., content distribution using Akamai vs. using

Amazon CloudFront• New application opportunities – Mobile interactive apps, large batch processing,

business analytics, …

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Cloud Computing: 3 New Aspects

• Illusion of infinite computing resources – Users do not need to plan ahead for provisioning

• Elimination of up-front commitments by users – Start small and increase on demand

• Pay on short term basis, e.g., hourly – Cost saving by getting machines only when needed– Elasticity: can scale up or down (quickly)

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Migrating Apps to Clouds

• Candidate apps for migration have following C/C’s – Demand for resources vary with time

• provisioning private data centers for peak wastes resources– Demand is not known in advance

• Cannot provision private data centers; either too much waste (overprovisioning) or lost opportunities (underprovisioning)

– Can leverage “cost associativity”• Using one machine for 100 hrs costs same as using 100 for 1 hr

• Cloud migration transfers risk of miscalculating demand from user to cloud provider– Risk is mitigated by statistical multiplexing across multiple users

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Cloud Economics

• Resources wasted in overprovisioning (left) and • Requests/services are rejected in under provisioning

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Cloud Economics

• Cost-benefit analysis should consider – Variability of the demand– Cost of transferring data in/out of cloud– Utilization of private resources; typical server utilization 5-20%

• Cannot have ~100% utilization as delay explodes – Cost of hardware drops during depreciation period (~3 years)

• Cloud providers can reduce cost for customers– Human cost to manage private resources– Time to provision resources

• Few minutes on clouds vs. weeks for private resources– Risk of early disposal of hardware

• Termination of project, market change for product, …• extra cost

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Cloud Computing Model

System Design

Programming Models & Resource Management

Cloud Services

Cloud Applications

Data center, storage system

Virtualization, allocation, programming

Domain-specific services

Large-scale applications

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Data Centers

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Data Centers—Clusters

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Data Centers – Storage Hierarchy

• Notice the differences in latency and bandwidth

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Data Centers—Software Infrastructure

• Quite complex system to program– Many components– Different bandwidth and latency– Many failures

• Several tools and models to help– MapReduce– BigTable– Google File System– DryadLINQ– …

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Data Centers – Power Distribution

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Data Centers – Cooling

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PUE: Power Usage Effectiveness

• PUE = Total building power / power in IT equipment – reflects quality of the datacenter building– Ideal to be 1.0

• Old data centers had PUE from 2.0 to 3.0

• Newer ones have PUE < 2.0

• Google reported PUE <1.10 in some recent data centers

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Power Overhead in Data Centers

• Rough division of power overheads in data centers

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Data Centers – IT Power Consumption

• No single component dominates power consumption

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Data Centers—Tiers

• Tier I: – single path for power /cooling distribution, no redundant

components• Tier II

– adds redundant components (N + 1), improving availability.• Tier III:

– Multiple power /cooling distribution paths but one active path– Provide redundancy even during maintenance, usually N + 2

• Tier IV:– two active power/cooling distribution paths, redundant

components• Most commercial DCs are III and IV– Availability for II, III, IV: 99.75, 99.98%, 99.995%

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Summary

• Demand of multimedia content is growing

• QoS layers for end-to-end quality

• Cloud computing … make computing utility

• Candidate cloud apps have variable/unknown demand

• Migrating to cloud, if feasible, may – reduce cost, – accelerate development/deployment, and – mitigate risk of estimating success/failure of new service/product

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References

• Armbrust et al., Above the Clouds: A Berkeley View of Cloud. Computing, UCB/EECS-2009-28, Tech Report, February 2009

• Barroso and Holzle, The Datacenter as a Computer An Introduction to the Design of Warehouse-Scale Machines, 2009.