Lecture 15:Distributed Multimedia Systems

Haibin Zhu, PhD.

Assistant Professor

Department of Computer Science

Nipissing University

© 2002

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Contents

15.1 Introduction 15.2 Characteristics of multimedia data 15.3 Quality of service management 15.4 Resource management 15.5 Stream adaptation 15.6 Case study: Tiger video file server 15.7 Summary

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Learning objectives

To understand the nature of multimedia data and the scheduling and resource issues associated with it.

To become familiar with the components and design of distributed multimedia applications.

To understand the nature of quality of service and the system support that it requires.

To explore the design of a state-of-the-art, scalable video file service; illustrating a radically novel design approach for quality of service.

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A distributed multimedia system

Wide area gateway Videoserver

DigitalTV/radioserver

Video cameraand mike

Local network Local network

Figure 15.1

Applications:– non-interactive: net radio and TV, video-on-demand, e-learning, ...

– interactive: voice &video conference, interactive TV, tele-medicine, multi-user games, live music, ...

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Multimedia in a mobile environment

Applications:– Emergency response systems, mobile commerce, phone service,

entertainment, games, ...

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Characteristics of multimedia applications

Large quantities of continuous data Timely and smooth delivery is critical

– deadlines– throughput and response time guarantees

Interactive MM applications require low round-trip delays Need to co-exist with other applications

– must not hog resources

Reconfiguration is a common occurrence– varying resource requirements

Resources required:– Processor cycles in workstations – and servers– Network bandwidth (+ latency)– Dedicated memory– Disk bandwidth (for stored media)

At the right timeand in the right quantities

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Application requirements

Network phone and audio conferencing– relatively low bandwidth (~ 64 Kbits/sec), but delay times must be short ( <

250 ms round-trip)

Video on demand services– High bandwidth (~ 10 Mbits/s), critical deadlines, latency not critical

Simple video conference– Many high-bandwidth streams to each node (~1.5 Mbits/s each), high

bandwidth, low latency ( < 100 ms round-trip), synchronised states.

Music rehearsal and performance facility– high bandwidth (~1.4 Mbits/s), very low latency (< 100 ms round trip), highly

synchronised media (sound and video < 50 ms).

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System support issues and requirements

Scheduling and resource allocation in most current OS’s divides the resources equally amongst all comers (processes)– no limit on load

– can’t guarantee throughput or response time

MM and other time-critical applications require resource allocation and scheduling to meet deadlines– Quality of Service (QoS) management

Admission control: controls demand QoS negotiation: enables applications to negotiate admission

andreconfigurations

Resource management: guarantees availability of resources for admitted applications

– real-time processor and other resource scheduling

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Characteristics of typical multimedia streams

Data rate(approximate)

Sample or frame frequency size

Telephone speech 64 kbps 8 bits 8000/secCD-quality sound 1.4 Mbps 16 bits 44,000/secStandard TV video(uncompressed)

120 Mbps up to 640 x 480pixels x 16 bits

24/sec

Standard TV video (MPEG-1 compressed)

1.5 Mbps variable 24/sec

HDTV video(uncompressed)

1000–3000 Mbps up to 1920 x 1080pixels x 24 bits

24–60/sec

HDTV videoMPEG-2 compressed)

10–30 Mbps variable 24–60/sec

Figure 15.3

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Typical infrastructure components for multimedia applications

Microphones

Camera

Screen

Window system

CodecD

BMixer

PC/workstation PC/workstation

C Videostore

Networkconnections

K

L

M

CodecA G

CodecH

Windowsystem

Video file system

: multimedia stream

White boxes represent media processing components, many of which are implemented in software, including:

codec: coding/decoding filtermixer: sound-mixing component

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Figures 15.4 & 15.5

Component Bandwidth Latency Loss rate Resources required

Camera Out: 10 frames/sec, raw video640x480x16 bits

Zero

A Codec In:Out:

10 frames/sec, raw videoMPEG-1 stream

Interactive Low 10 ms CPU each 100 ms;10 Mbytes RAM

B Mixer In:Out:

2 44 kbps audio1 44 kbps audio

Interactive Very low 1 ms CPU each 100 ms;1 Mbytes RAM

H Windowsystem

In:Out:

various50 frame/sec framebuffer

Interactive Low 5 ms CPU each 100 ms; 5 Mbytes RAM

K Networkconnection

In/Out: MPEG-1 stream, approx.1.5 Mbps

Interactive Low 1.5 Mbps, low-lossstream protocol

L Networkconnection

In/Out: Audio 44 kbps Interactive Very low 44 kbps, very low-lossstream protocol

This application involves multiple concurrent processes in the PCs

Other applications may also be running concurrently on the same computers

They all share processing and network resources

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Quality of service management

Allocate resources to application processes – according to their needs in order to achieve the desired quality of multimedia

delivery

Scheduling and resource allocation in most current OS’s divides the resources equally amongst all processes– no limit on load

– can’t guarantee throughput or response time

Elements of Quality of Service (QoS) management– Admission control: controls demand

– QoS negotiation: enables applications to negotiate admission andreconfigurations

– Resource management: guarantees availability of resources for admitted applications

– real-time processor and other resource scheduling

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The QoS manager’s task

Application components specify their QoS requirements to QoS manager

Yes No

Yes No

Flow spec.

Resource contract

Admission control QoS negotiation

QoS manager evaluates new requirementsagainst the available resources.

Sufficient?

Reserve the requested resources

Allow application to proceed

Application runs with resources as per resource contract

Negotiate reduced resource provision with application.Agreement?

Do not allow application to proceed

Application notifies QoS manager of increased resource requirements

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Figure 15.6

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QoS Parameters

Bandwidth– rate of flow of multimedia data

Latency– time required for the end-to-end transmission of a single data element

Jitter variation in latency :– dL/dt

Loss rate– the proportion of data elements that can be dropped or delivered late

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Managing the flow of multimedia data

Flows are variable– video compression methods such as MPEG (1-4) are based on

similarities between consecutive frames – can produce large variations in data rate

Burstiness– Linear bounded arrival process (LBAP) model:

maximum flow per interval t = Rt + B (R = average rate, B = max. burst)

– buffer requirements are determined by burstiness– Latency and jitter are affected (buffers introduce additional delays)

Traffic shaping– method for scheduling the way a buffer is emptied

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Protocol version

Maximum transmission unit

Token bucket rate

Token bucket size

Maximum transmission rate

Minimum delay noticed

Maximum delay variation

Loss sensitivity

Burst loss sensitivity

Loss interval

Quality of guarantee

Bandwidth:

Delay:

Loss:

Figure 15.8 The RFC 1363 Flow Spec

acceptable jitter

acceptable latency

maximum rate

burstiness

percentage per T

maximum consec-utive loss

T

value

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(a) Leaky bucket

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Figure 15.7

process 1

process 2

Traffic shaping algorithms – leaky bucket algorithm

analogue of leaky bucket:– process 1 places data into a buffer in bursts– process 2 in scheduled to remove data regularly in smaller amounts– size of buffer, B determines:

maximum permissible burst without loss maximum delay

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Token generator

(b) Token bucket

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Figure 15.7

Traffic shaping algorithms – token bucket algorithm

Implements LBAP– process 1 delivers data in bursts– process 2 generates tokens at a fixed rate– process 3 receives tokens and exploits them to deliver output as quickly as it gets

data from process 1

Result: bursts in output can occur when some tokens have accumulated

process 2 process 1

process 3

tokens: permits to place x bytes into output buffer

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Admission control

Admission control delivers a contract to the application guaranteeing:For each computer:

cpu time, available at specific intervals memory

Before admission, it must assess resource requirements and reserve them for the application

– Flow specs provide some information for admission control, but not all - assessment procedures are needed

– there is an optimisation problem: clients don't use all of the resources that they requested flow specs may permit a range of qualities

– Admission controller must negotiate with applications to produce an acceptable result

For each network connection:bandwidthlatency

For disks, etc.:bandwifthlatency

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Resource management

Scheduling of resources to meet the existing guarantees:Fair scheduling allows all processes some portion of the resources based on

fairness: E.g. round-robin scheduling (equal turns), fair queuing (keep queue lengths equal) not appropriate for real-time MM because there are deadlines for the delivery of

data

Real-time scheduling traditionally used in special OS for system control applications - e.g. avionics. RT schedulers must ensure that tasks are completed by a scheduled time.

Real-time MM requires real-time scheduling with very frequent deadlines.

Suitable types of scheduling are:

Earliest deadline first (EDF)

Rate-monotonic

e.g. for each computer:cpu time, available at specific intervalsmemory

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EDF schedulingEach task specifies a deadline T and CPU seconds S to the scheduler for each work item (e.g. video frame). EDF scheduler schedules the task to run at least S seconds before T (and pre-empts it after S if it hasn't yielded).It has been shown that EDF will find a schedule that meets the deadlines, if one exists. (But for MM, S is likely to be a millisecond or so, and there is a danger that the scheduler may have to run so frequently that it hogs the cpu).

Rate-monotonic scheduling assigns priorities to tasks according to their rate of data throughput (or workload). Uses less CPU for scheduling decisions. Has been shown to work well where total workload is < 69% of CPU.

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Stream adaptation: Scaling and filtering

SourceTargets

High bandwidth

Medium bandwidth

Low bandwidth

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Figure 15.9

Scaling reduces flow rate at source– temporal: skip frames or audio samples

– spatial: reduce frame size or audio sample quality

Filtering reduces flow at intermediate points– RSVP is a QoS negotiation protocol that negotiates the rate at each

intermediate node, working from targets to the source.

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QoS and the Internet

Very little QoS in the Internet at present– New protocols to support QoS have been developed, but their implementation

raises some difficult issues about the management of resources in the Internet.

RSVP– Network resource reservation

– Doesn’t ensure enforcement of reservations

RTP– Real time data transmission over IP

need to avoid adding undesirable complexity to the Internet

IPv6 has some hooks for it

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Source address(128 bits)

Destination address(128 bits)

Version (4bits) Priority (4bits) Flow label (24 bits)

Payload length (16 bits) Hop limit (8bits)Next header (8bits)

IPv6 header layout

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Video on demand for a large number of users

Quality of service

Scalable and distributed

Low cost hardware

Fault tolerant

Tiger design goals

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Tiger

Network

Clients

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Tiger architecture

Storage organization– Striping– Mirroring

Distributed schedule

Tolerate failure of any single computer or disk

Network support

Other functions– pause, stop, start

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Tiger video file server hardware configuration

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Each movie is stored in 0.5 MB blocks (~7000) across all disks in the order of the disk numbers, wrapping around after n+1 blocks.

Block i is mirrored in smaller blocks on disks i+1 to i+d where d is the decluster factor

n+10 n+21 n+32 n+43 2n+1n

Controller

Cub 0 Cub 1 Cub 2 Cub 3 Cub n

ATM switching network

video distribution to clientsStart/Stop

requests from clients

low-bandwidth network

high-bandwidth

Figure 15.10

Cubs and controllersare standard PCs

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Tiger schedule

block play time T block service

time t

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Figure 15.11

Cub algorithm: 1. Read the next block into buffer storage at the Cub.2. Packetize the block and deliver it to the Cub’s ATM network controller with the

address of the client computer.3. Update viewer state in the schedule to show the new next block and play sequence

number and pass the updated slot to the next Cub.4. Clients buffer blocks and schedule their display on screen.

slot 0

viewer 4 state

viewer client viewer state: slot 1

free

slot 2

free

slot 3

viewer 0 state

slot 4

viewer 3 state

slot 5

viewer 2 state

slot 6

free

slot 7

viewer 1 state

012

in time t

Stream capacity of a disk = T/t (typically ~ 5)Stream capacity of a cub with n disks = n x T/t

Network address of client

FileID for current movie

Number of next block

Viewer's next play slot

Viewer state:

slot 0

viewer 4 state

viewer client viewer state: slot 1

free

slot 2

free

slot 3

viewer 0 state

slot 4

viewer 3 state

slot 5

viewer 2 state

slot 6

free

slot 7

viewer 1 state

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Tiger performance and scalability

1994 measurements:– 5 x cubs: 133 MHz Pentium Win NT, 3 x 2Gb disks each, ATM

network.– supported streaming movies to 68 clients simultaneously without lost

frames.– with one cub down, frame loss rate 0.02%

1997 measurements:– 14 x cubs: 4 disks each, ATM network– supported streaming 2 Mbps movies to 602 clients simultaneously

with loss rate of < .01%– with one cub failed, loss rate <.04%

The designers suggested that Tiger could be scaled to 1000 cubs supporting 30,000 clients.

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Summary

MM applications and systems require new system mechanisms to handle large volumes of time-dependent data in real time (media streams).

The most important mechanism is QoS management, which includes resource negotiation, admission control, resource reservation and resource management.

Negotiation and admission control ensure that resources are not over-allocated, resource management ensures that admitted tasks receive the resources they were allocated.

Tiger file server: case study in scalable design of a stream-oriented service with QoS.