T.Sharon-A.Frank 1 Multimedia on the Internet

2 T.Sharon-A.Frank Is the Internet Real-Time (MM)?

3 T.Sharon-A.Frank Internet/Multimedia Assumptions Internet –Point-to-Point (unicast) –Best-Effort Delivery –Elastic Applications –FIFO Packet Scheduling –Provides average Packet Delay –End-to-End Reliability –Statistical Multiplexing Gain Multimedia –Multipoint –Soft RT Constraints –Inelastic Applications –Need Control over Delay and Jitter –Various Traffic Classes –Need QoS Guarantees

4 T.Sharon-A.Frank Application Taxonomy (1) ElasticInelastic Applications Elastic Applications: Can tolerate relatively large delay variance – essentially the traditional data application. Inelastic Applications: Comparatively intolerant to delay, delay variance, throughput variance and errors.

5 T.Sharon-A.Frank Examples of Elastic Applications –asynchronous –message is not real-time –delivery in several minutes is acceptable File transfer: –interactive service –require “quick” transfer –“slow” transfer acceptable Network file service: –interactive service –similar to file transfer –fast response required –(usually over LAN) WWW: –interactive –file access mechanism –fast response required –QoS sensitive content on WWW pages

6 T.Sharon-A.Frank Examples of Inelastic Applications Streaming voice: –not interactive –end-to-end delay not important –end-to-end jitter not important –data rate and loss very important Real-time voice: –person-to-person –interactive –important to control: end - to-end data rate end-to-end delay end-to-end jitter end-to-end loss

7 T.Sharon-A.Frank Application Taxonomy (2) ElasticInelastic Applications Tolerant Loose Delay Bounds Firm Delay Bounds IntolerantInteractive Burst Best Effort Level 1 Interactive Bulk Best Effort Level 2 Asynchronous Bulk Best Effort Level 3 Telnet X NFS Web FTP MM-Mail Fax Streamin g VOD Medical Imaging CAD Schemes

8 T.Sharon-A.Frank QoS Types of Service  Best-effort Service  no/partial guarantees/bounds  Predictive Service  estimation based on past network behavior  Guaranteed Service  deterministic  statistical Current service in most protocols

9 T.Sharon-A.Frank Soft RT QoS Guarantees Deterministic  Provide Bounds on Performance of all Packets in a Session. Statistical  No more than a Specified Fraction of Packets will see Performance Below a Certain Specified Value.

10 T.Sharon-A.Frank Deterministic RT QoS Guarantee Delay: no packets delayed more than D time units on E2E basis (T<=D). Loss: no packet loss occurs. Transit Window: bound transit window (Tmax-Tmin<=E). Queuing: the delay of every packet from session i is less than x at queue j.

11 T.Sharon-A.Frank Statistical RT QoS Guarantee Delay: no more than x% of packets have a delay larger than D (PR[T>D]<epsilon) Loss: no more than x% of packets in a session are lost PR[Packet-loss]<epsilon Queuing: the probability that a packet from session i has a delay greater than x is guaranteed to be less than y at queue j.

12 T.Sharon-A.Frank Application Taxonomy (3) Elastic Inelastic Applications Tolerant Loose Delay Bounds Firm Delay Bounds IntolerantInteractive Burst Best Effort Level 1 Interactive Bulk Best Effort Level 2 Asynchronous Bulk Best Effort Level 3 Telnet X NFS Web FTP MM-Mail Fax Streamin g VOD Medical Imaging CAD Schemes Best-effort ServicePredictiveGuaranteed Grab Bandwidth No Certain Arrival Time Uses Data Immediately No Admission Control The Opposite Care About Average Packet DelayQuantitative Maximum Delay

13 T.Sharon-A.Frank Example: Playback Applications Audio/Video Services Soft Real-Time Tolerant Constraints senderreceiver buffer Network Varying delay transmit Buffer, Decompress, PlaybackAcquire signal, Digitize, Compress If arrives late – useless/loss. Playback point: Signal generation time + Fixed offset delay. Compute offset based on max delay: Offset delay can be adjusted provided by network based on observed delays

14 T.Sharon-A.Frank Internet QoS Models Adaptation Model –Adapt applications hide Internet service from the users – scaling –Adapt Internet Differentiated Services (DiffServ) – simple priority Extension Model Integrated Services (IntServ) – resource reservation

15 T.Sharon-A.Frank Adaptation Model Use network Feedback/Scaling Adapt applications (Scaling) Minimal changes to Internet (DiffServ) No need for Resource Reservation: –“Bandwidth will be infinite” When? Everywhere? Overload? –“Applications can be adaptive” Too slow? Can users adapt? –“Simple priority is sufficient” All high priority? Overload?

16 T.Sharon-A.Frank Scaling  Transparent Scaling - usually by dropping some portion of the data stream.  Non-transparent Scaling - usually by adjusting parameters in the coding algorithm. Means to sub-sample a data stream and only present a fraction of its original content. Scaling types:

17 T.Sharon-A.Frank Scaling in Audio and Video  Audio –Transparent scaling is difficult because human ear is sensitive –usually done by changing sampling rate  Video –Temporal scaling (drop frames) –Spatial scaling (reduce resolution) –Frequency scaling (reduce number of DCT coefficients) –Amplitude scaling (reduce color depth) –Color space scaling (reduce number of color entries or even switch to gray scale)

18 T.Sharon-A.Frank Audio Scaling

19 T.Sharon-A.Frank Scaling Example: Videoconferencing

20 T.Sharon-A.Frank Scaling Example:Videoconferencing (2)

21 T.Sharon-A.Frank Stream Management Managing streams is all about managing bandwidth, buffers, processing capacity and scheduling priorities – which are all needed in order to realize QoS guarantees. This is not as simple as it sounds, and there’s no general agreement as to “how” it should be done. For instance: ATM’s QoS (which is very “rich”) has proven to be unworkable (difficult to implement). Another technique is the Internet’s RSVP.

22 T.Sharon-A.Frank Improving QoS in IP Networks IETF groups are working on proposals to provide better QoS control in IP networks, i.e., going beyond best effort to provide some assurance for QoS. Work in Progress includes Differentiated Services (DiffServ), RSVP and Integrated Services (IntServ).

23 T.Sharon-A.Frank Differentiated Services (DiffServ) Relatively simple, coarse-grained QoS mechanism. Deployed in networks without needing to change the operation of the end system application. Based around marking packets with a small- fixed bit-pattern, which maps to certain handling and forwarding criteria at each hop.

24 T.Sharon-A.Frank Extension Model Single Service Model –Best-effort services –Soft real-time services Keep Internet Philosophy –Downward compatible –Common infrastructure –Unified protocol stack –Open/public access –User usage-based pricing Need New Integrated Services (IntServ) Model?

25 T.Sharon-A.Frank Resource Reservation Pre-allocation of needed resources to guarantee deterministic QoS. Allocated resources are dedicated; if not used – remain idle. Example: Internet RSVP – Resource reSerVation Protocol. If resources cannot be reserved, scaling can be used.

26 T.Sharon-A.Frank Internet RSVP QoS The basic organization of RSVP for resource reservation in a distributed system – transport-level control protocol for enabling resource reservations in routers. Interesting characteristic: receiver initiated.

27 T.Sharon-A.Frank Specifying QoS with Flow Specifications A flow specification – one way of specifying QoS – a little complex, but it does work (but not via a user controlled interface). Characteristics of the InputService Required maximum data unit size (bytes) Token bucket rate (bytes/sec) Toke bucket size (bytes) Maximum transmission rate (bytes/sec) Loss sensitivity (bytes) Loss interval (  sec) Burst loss sensitivity (data units) Minimum delay noticed (  sec) Maximum delay variation (  sec) Quality of guarantee

28 T.Sharon-A.Frank An Approach to Implementing QoS The principle of a token bucket algorithm – a “classic” technique for controlling the flow of data (and implementing QoS characteristics).

29 T.Sharon-A.Frank Integrated Services (IntServ) An architecture for providing QOS guarantees in IP networks for individual application sessions. Relies on resource reservation. Routers need to maintain state info, maintaining records of allocated resources and responding to new Call setup requests on that basis.