Beyond Server Selection: Challenges in Multiple-Origin Content Distribution

Mostafa H. AmmarCollege of Computing

Georgia Institute of TechnologyAtlanta, GA

ammar@cc.gatech.edu

Contributors

Ellen ZeguraHyewon JunChristos GkantsidisPradnya KarbhariMatt SandersLi Zou

Multiple-Origin Content Distribution Systems

Content is ReplicatedAuthoritativeGrass-roots (Peer-to-Peer)

Content is Re-constituted

Challenges

Server Selection Benefit of content replication can only be

realized with proper selection

Multipoint-to-point sessions … on their way to becoming a dominant

communication paradigm in a network that was designed for pt-to-pt connections

Talk Outline

Server SelectionApplication-Layer AnycastingSelection vs Binding

Multipoint-to- point sessionsImpact of Parallel DownloadingPer Session Rate Allocation

Please forgive lack of references

Talk Outline

Server SelectionApplication-Layer AnycastingApplication vs Network-Layer

AnycastingMultipoint-to- point sessions

Impact of Parallel DownloadingPer Session Rate Allocation

Server Replication

Server Selection ProblemHow does a client determine which

of the replicated servers to access

Interested in Wide-Area Replication

Anycasting

Network-Layer Anycasting in RFC 1541Anycast IP addressesNetwork-layer metricsPer-packet selection

Application-Layer Anycasting

Group of servers identified by Anycast Name

Clients request service from group identified by name

Automatic connection to a “good” server

An Architecture

Resolver

Orange Server Group

Green Server Group

Green Service?

Go to server y

Server y

Resolver

“Close” to clientMaintains

Anycast group membershipSelection-enabling information

Client may provide filter that tells resolver how to select

DNS-like hierarchy of resolvers

Web Server Selection

An instantiation of architectureCriterion: Best Response Time

[client request, last byte received]includes path and server delays

Problem: Maintaining response time estimate

for each server in anycast group at resolver

Response Time Estimation Alternatives

ProbePushUser-Experience

Developed a Hybrid Push/Probe Technique

Wide-Area Experiments

4

3

5

3

4

51

5

5

3

UCLA

WU

UMD

GT

Servers: UCLA, GTx2, WU,Clients: UMDx4, GTx16,Resolvers: UMD, GT

Anycasting VS Random Selection

What if Anycasting is popular?

Checkpoint

Appropriate guidance of clients to servers is an important infrastructure function

Client-perceived as well as global performance can be improved with the appropriate selection technology

What about a network-layer anycasting infrastructure?

Talk Outline

Server SelectionApplication-Layer AnycastingApplication vs Network-layer Anycasting

Multipoint-to- point sessionsImpact of Parallel DownloadingPer Session Rate Allocation

Selection vs Binding

Selection vs Binding

Selection: A function that returns instantaneous server choice.

Binding: An application-level function which decides on the use a particular server.

Spectrum Of Binding

Spectrum of Binding (2)

Initial Binding (IB) : Select one server and stay with it during the connection life time

Periodic Binding (PB) : Periodically select a server and switch to the new server.

Continuous Binding (CB) : Select the best server per packet to react fast to the server performance change

Design Space

App-Layer Anycasting

Our OwnServer Migration

Protocol

The desirability of a network-layer anycasting infrastructure depends on whether Continuous Binding can be shown to outperform Initial Binding

Migration of a CB Client

Simulation Topolgy

Initial vs. Continuous Binding

Server Rank Change every [1,10] sec Server Rank Change everfy [51,60] sec

Despite the overhead of migration, Continuous Binding is able to improve performance when the connection is long-lived.

Heterogeneous Binding

Increasing use of either scheme over the other by all clients with long-lived connections leads to overall performance degradation!

Checkpoint

Network-layer anycasting allows for efficient continuous binding

Continuous binding outperforms initial binding in some long transfer, highly-dynamic situations

Did not account for overhead of selection function

But we have something more sinister to worry about ….

Talk Outline

Server SelectionApplication-Layer AnycastingApplication vs Network-layer Anycasting

Multipoint-to- point sessionsImpact of Parallel DownloadingFairness

Motivation

Traditional data retrieval- over a point-to-point connection from a single server to a single client

Current trend- retrieval over multiple point-to-point connections from multiple servers to a single clientexamples: CDNs, replicated servers,

caches, parallel file downloads, web-traffic, MD-CDNs

What is a Session?

Definition of multipoint-to-point session:A set of point-to-point connections

started from multiple servers to a single client in order to transfer an application-level object

Typical Sessions in the Internet

Typical Sessions

Talk Outline

Server SelectionApplication-Layer AnycastingApplication vs Network-layer Anycasting

Multipoint-to- point sessionsImpact of Parallel DownloadingPer Session Rate Allocation

Impact of Parallel Downloading

Question 1: How much can a single user gain by parallel downloading?

Question 2: What happens if all users perform parallel downloading?

Question 3: How do parallel downloading users affect single downloading users?

Aggressiveness pays off.

Number of servers

Tim

e (i

n se

c) For a ~7MB file:

•Best rate: ~3Mbps.

•4x faster than single server.

0

50

100

150

3 4 5 6

Single Server

StaticEqual

StaticUnequal

DynamicEqual

Wide deployment of Parallel Downloading

More ConnectionsNumber of competing flows increases.More requests at the server (but, for a

shorter period of time).More Overhead

Fixed overhead is paid multiple times:Cost of a request = {size, rate, etc.}-

Dependent cost + Fixed Cost.

Many aggressive clients are harmful!

Aggressive clients can hurt simple clients

Summary

There is strong local incentive for a client to use parallel downloading.

But if every one does it there is evidence global performance suffers

We need a per session rate allocation.

Talk Outline

Server SelectionApplication-Layer AnycastingApplication vs Network-layer Anycasting

Multipoint-to- point sessionsImpact of Parallel DownloadingPer-Session Rate Allocation

Our Goal

To develop algorithms to achieve rate allocations which are fair to all sessions

Some challenges:Data path of each session forms a treeEvery session has multiple bottlenecksPartial sharing of bottlenecks between

sessions

Inter-session and Intra-session fairness

Focus on Static Sessions

For purposes of rate allocation, connections start and terminate at approximately the same time

Examples: parallel file downloads, multimedia streaming using MD-CDNs

Current Rate Allocation Approach

Max-min fairness, TCP fairnessProblems with allocating rate on a

per-connection basis:sessions with more connections get

higher rate allocation than sessions with fewer connections

this is not a fair rate allocation from a session point of view

Proposed Session Fair Approaches (1)

Normalized rate session fairnessrate allocation is based on weight of

each connectionweights wi,j are assigned to each

connection j in each session i, subject to the constraint:

this constraint ensures that total session rates are fair with respect to each other

1j

i,jw

Proposed Session Fair Approaches (2)

Per-link session fairnessrate allocation at each link on a per-

session basiseach session then allocates this rate

amongst the connections that traverse that link

this ensures fair allocation of session rates

Example- Connection fair

Example - Normalized rate session fair

Example- Per-link session fair

Simulation Model and Fairness Measures

100,600-node topologies using GT-ITM

varying percentages of clients and servers

sessions with 1,4,15 connections with varying percentages

fairness measures: variance, mean, maximum, minimum of session rates and fairness index

Evaluation- fairness index

criterion: fairness index-

fairness index of 1 implies a very fair (equal) distribution

session fair rate allocations achieve a better fairness index than connection-fair rate allocations

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i

in xnn

ixxxxf i

1

212

2

1,...,,

Fairness indices of session rates for different algorithms

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60 70 80

% of 1-connection sessions

fair

ness

inde

x

connection fairnormalized rate SFper-link SFuser fair queueing

Variance of session rates

0E+0

1E+7

2E+7

3E+7

4E+7

5E+7

0 10 20 30 40 50 60 70 80

% of 1-connection sessions

vari

ance

of s

essi

on r

ates connection fair

normalized rate SFper-link SFuser fair queueing

Checkpoint

Multipoint to point sessions are increasingly a predominant mode of communication in the Internet.

Per-Session rate allocation seems a natural response to better control sharing behavior.

To DO: Implement the protocols and architecture for

realizing session-fair rate allocationsExtend this framework to dynamic sessions with

multiple connections starting and ending at different times

Concluding Remarks

Moving content around is the primary function of wide-area networks today

Emerging services and paradigms provide new challengesContent Replication Server SelectionMultipoint-to-point sessions Resource

sharing questionsPeer-to-Peer that’s another story …