1 Placement of Continuous Media in Wireless Peer-to-Peer Networks Shahram Ghadeharizadeh, Bhaskar...

1

Placement ofContinuous Media in WirelessPeer-to-Peer Networks

Shahram Ghadeharizadeh, Bhaskar Krishnamachari, Shanshan Song, IEEE Transactions on Multimedia, vol. 6, no. 2, April 2004

Presentation by Tony Sung, MC Lab, IE CUHK10th November 2003

2

Outline

The “H2O” Framework

A Novel Data Placement and Replication Strategy

Modeling (Topology) and Performance Analysis

Two Distributed Implementations

Conclusion, Discussion and Future Work

3


H2O: Home-to-home online

A number of devices connected through wireless channel.

Complements existing wired infrastructure such as Internet and provide data services to individual households.

Implementing VoD over H2O

A household may store its personal video library on an H2O cloud.

Each device might act in3 possible roles:

producer of data;

an active client thatis displaying data;

a router that deliversdata from a producerto a consumer.

4


Media Retrieval

The 1st block of a clip must make multiple hops to arrive.

A portion of the video must be prefetched before playback starts to compensate for network bandwidth fluctuations.

How to Minimize theStartup Latency?

5


By Caching

One Extreme: Full replication

Startup latency is minimized.

Even if bandwidth is not a limiting factor, storage requirement is tremendous.

A Novel Approach: Stripe the video clip into blocks, and replicate blocks that has a later playback time less often in the system.

Startup latency is still minimized.

6

A Novel Data Placement & Replication Strategy

Parameters

Video clip X is divided into z equal sized blocks bi of size Sb

Playback duration of a block : D = Sb / BDisplay

Playback time of bi = (i - 1)D

Let : time to transmit a block across one hop = h

bi can be placed at most Hi hops away under the constraint:

Hi = ((i - 1)D) / h

Assumptions

CBR media, h is a fixed constant, BLink > BDisplay

b1 should be placed on all nodes in the network.

For bi with 1 ≤ i ≤ z, it can be placed less often to save storage.

7

A Novel Data Placement & Replication Strategy

Core Replication and Placement Strategy

1. Divide the clip into z equal sized blocks bi, each Sb in size.

2. Place b1 on all nodes in the network.

3. For each bi with 1 ≤ i ≤ z, compute its delay tolerance Hi .

4. Based on Hi , compute ri which is the total number of replicas required for block bi . Notes that ri and its computation are topology dependent, and it decreases monotonically with i until it reaches 1.

5. Construct ri replicas of block bi and place each copy of the block in the network while ensuring that for all nodes there exists at least one copy of the block bi that is no more than Hi hops away.

8

Modeling and Performance Analysis

Analysis of ri for 3 different topologies:

Worst-Case Linear Topology

Grid Topology

Average Case Graph Topology

Performance is measured in percentage savings over full replication.

9



N devices organized in a linear fashion.

In the worst case scenario, bi must be replicated ri = N-Hi times and takes N-ri-1 hops to reach the destination.

If ri is non-positive, it is reset to 1.This is equivalent to stop replicating those blocks whose index exceeds Ur = ((N – 1)h)/D + 1.

Giving total storage as:

contains replica of bi

10



N = 1000

h = 0.5 s

BDisplay = 4 Mbps

Sb = 1 MB

D = 2 s

Worst Case

Storage Requirement

11


Grid Topology

N devices organized in a square grid of fixed area, each neighbors only the 4 nodes in each cardinal direction.

No. of replicas ri :

Expected total storage:

12


Grid Topology

2-min Clip 2-h Clip

Effect of Block Size

Decrease StorageIncrease Complexity

Decrease StorageIncrease Complexity

Depends on h onlyIndep. of N and SC

13


Average Case Graph Topology

N devices scattered randomly in a fixed area A with radio range R for each node.

Of any given node, the expected number of nodes within Hi hops is between

where γ is a density dependent correction factor between 0 and 1 (when densed and nodes are distributed evenly).

Using the upper boundary, the number of replicas and expected total storage are :

and

14


Comparison

250 devices, 97% savings

more than 80%

15

Distributed Implementations

TIMER and ZONE

Both control placement of ri copies of each bi with the follow objective:

Each node in the network in within Hi hops of at least one copy of bi

General Framework

The publishing node H2Op computes block size Sb, no. of blocks z, and required hop-bound Hi, using the previous expressions.

H2Op floods the network with a message containing this information, and each recipient H2Oj computes a binary array Aj that signifies which blocks to host. The recipients will also retrieve from H2Op a copy of the blocks.

TIMER and ZONE differs in how Aj are computed.

16


TIMER

A distributed timer-suppress algorithm

When H2Oj receives the flooded query message, it performs z rounds of “elections”, one for each block

H2Oj determines whether to maintain a copy of block bi

Each node picks a random timer value from 1 to M and starts count down.

When timer reaches 0, and the node is not already suppressed

elects itself to store a copy of block b

sends a suppress message to all nodes within Hi hops

At the end of a round, every node will either be elected or suppressed, and every node is guaranteed to be within Hi hops of an elected node

17


ZONE

Assumes existence of nodes with geopositioning info

Consider all nodes fit within an area of S x S

For each bi , divide the area into si x si squares such that the square fits within a circle of radius HiR where R is the radio range

It can be shown that si = γHiR√2 where γ≦ 1 is a correction factor that depends on node density.

A copy of bi is placed near the center of each square

18


ZONE

z rounds of elections, one for each block

All nodes determine which zone they belongs to,based on Hi

For each zone

Elect the node that is closest to the zone center by a distributed leader election protocol (such as FloodMax)

19


Comparison

Both distribution algorithms require a few more replicas per block than predicted analytically.

<= Border Effect

The percentage savings, however, is still several orders of magnitude superior to full replication.

20


Comparison

Blocks distribution across H2O devices is uniform with TIMER.

ZONE favors placement of blocks with a large Hi towards the center of a zone. But results are not provided.

21

Conclusion

Explored a novel architecture collaborating H2O devices to provide VoD with minimal startup latency.

A replication technique that replicates the first few blocks of a clip more frequently because they are needed more urgently.

Quantified impact of different H2O topologies on the storage space requirement.

Proposed two distributed implementation of the placement and replication technique.

22

Discussion and Future Work

Assumptions: fixed h

In wireless ad hoc networks, h is a function of the amount of transmitting devices

Admission control and transmission scheduling shall be added to address the variability of h

Can be extended to adjust data placement when a user requests a clip

Enable H2O cloud to respond to user actions such as removal of device

Consider bandwidth constraints

1 Placement of Continuous Media in Wireless Peer-to-Peer Networks Shahram Ghadeharizadeh, Bhaskar...

Documents

Transcript of 1 Placement of Continuous Media in Wireless Peer-to-Peer Networks Shahram Ghadeharizadeh, Bhaskar...