CS 414 - Spring 2011 CS 414 – Multimedia Systems Design Lecture 27 – Media Server (Part 3) Klara...

CS 414 - Spring 2011

CS 414 – Multimedia Systems Design Lecture 27 – Media Server (Part 3)

Klara Nahrstedt

Spring 2011

Administrative MP3 – posted today


Some Interesting Facts

DBMS2.com Source (May 2009)Facebook had 400 terabytes of disks managed by

Hadoop/Hive with an approx. 6:1 compression ratioFacebook’s Hadoop/Hive system ingests 15

terabytes of new data per dayFacebook had 610 Hadoop nodes (in May 2009)

running in a single cluster and was heading for 1000 Yahoo had 2000 nodes (in May 2009) and was heading for

4000


Some Interesting Facts Source: www.slideshare.net (March 2011) Current data sets:

NYSE: 8PB; Google > 12PB; Data Volumes:

NYSE: 1.5 TB daily; Facebook: 350 M users; 3.5B shared items/weekFacebook adds > 100K users, 55M ‘status’

updates, 80M photos daily


Outline Disk Scheduling

SCAN-EDF Group Sweeping Mixed Scheduling

Admission Control File System Metadata/Indexing Block Size Issues


Disk Scheduling Policies Goal of Scheduling in Traditional Disk

Management Reduce cost of seek time Achieve high throughput Provide fair disk access

Goal of Scheduling in Multimedia Disk Management Meet deadline of all time-critical tasks Keep necessary buffer requirements low Serve many streams concurrently Find balance between time constraints and efficiency


EDF (Earliest Deadline First) Disk Scheduling Each disk block request is tagged with

deadline Policy:

Schedule disk block request with earliest deadline

Excessive seek time – high overheadPure EDF must be adapted or combined with

file system strategies


EDF Example


Note: Consider that block number Implicitly encapsulates the disk track number

SCAN-EDF Scheduling Algorithm Combination of SCAN and EDF algorithms Each disk block request tagged with

augmented deadline Add to each deadline perturbation

Policy: SCAN-EDF chooses the earliest deadline If requests with same deadline, then choose

request according to scan direction


Implementation of SCAN-EDF Notation:

Di be deadline of disk block request ‘i’

Ni be track (block) position on disk

Nmax be maximum number of disk tracks

Deadline Modification: Di + f(Ni)

f(Ni) converts track number of ‘i’ into a small perturbation of deadline

Perturbation small enough so that Di + f(Ni) ≤ Dj + f(Nj) for Di ≤ Dj

Possible f(Ni) = Ni/Nmax


SCAN EDF Example (Nmax = 100)


Enhanced SCAN-EDF (1) Use more accurate perturbation of deadline Consider

Actual track position of disk head ‘N’ Nmax – max number of disk tracks

Ni – next track to be considered


Head Moves Upwards

Enhanced SCAN-EDF (2) Algorithm:

If head moves upwards (towards Nmax), then

(a)

(b)


maxmax )(,;

N

NNNfNNNN iiii

max

max)(,1;N

NNNfNNN iiii

Enhanced SCAN-EDF (3) If head moves downwards (towards 1), then

(a)

(b)


maxmax )(:;

N

NNfNNNN iiii

max

)(:1;N

NNNfNNN iiii

Group Sweeping Algorithms

Policy: Each Request consists of (Deadline, Block Number

)Disk Block Requests served in cycles In one cycle, requests divided into groups

according to similar deadlinesWithin group use SCANAs we retrieve blocks, we may need smoothing

buffers to ensure continuity


Group Sweeping Example


Mixed Scheduling (uses SSTF – Shortest Seek Time First)


Example of SSTF

Mixed Scheduling


SSTF (Shortest Seek Time First) + Balanced Strategy

Admission Control


Client 1 retrieves K1 blocks in oneround

Client 2 retrievesK2 blocks

Client 3 retrievesK3 blocks

Client 4 retrieves K4 blocks

Server

Admission Control Disk block requests are timed

Media server must determine admit a stream serve (schedule) a stream without having negative effect on

other streams already serviced.

Deterministic Guarantees Admission control considers worst case scenario when admitting

new stream Constrained Disk Placement Example: M - size of blocks, G –

size of gabs, rdt – data transfer of disk


)/(sec

)(sec)(sec

storsr

torsGtorsMT

dtplay

Admission Control


)/)((min)( ipl

iii

i

RK

α – overhead switching from one round (‘j-1’)to another round (j), and then transmitting the first block of the ‘j’ round

β – transmission time of (Ki-1) blocks in ‘j’ round, i=1,..4Ki – number of blocks retrieved by client ‘i’ ηi – Block granularity retrieved for client ‘i’ (e.g., in Bytes)Ri – playback rates of client ‘i’ (e.g., in Bytes per second)

Minimal Intra- Ki blocks delay Cost to switch and move Ki blocks

Admission Control

Statistical GuaranteesDeadlines are guaranteed with certain

probabilityAdmission control considers statistical

behavior of the disk system while admitting new stream (average performance)

Best effort ServiceNo guarantees


Conclusion

The data placement, scheduling, are very important for any media server design and implementation.

Still need to consider multimedia file system and caching – next lecture


CS 414 - Spring 2011 CS 414 – Multimedia Systems Design Lecture 27 – Media Server (Part 3) Klara...

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