school of computing science simon fraser university
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School of Computing Science Simon Fraser University. CMPT 880: Large-scale Multimedia Systems and Cloud Computing Introduction Mohamed Hefeeda. Course Objectives. Understand basics of multimedia systems & cloud computing Know current research issues in these areas Develop research skills - PowerPoint PPT PresentationTRANSCRIPT
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Mohamed Hefeeda 1
School of Computing ScienceSimon Fraser University
CMPT 880: Large-scale Multimedia Systems and Cloud Computing
Introduction
Mohamed Hefeeda
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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.