beyond server selection: challenges in multiple-origin content distribution
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Beyond Server Selection: Challenges in Multiple-Origin Content Distribution. Mostafa H. Ammar College of Computing Georgia Institute of Technology Atlanta, GA [email protected]. Contributors. Ellen Zegura Hyewon Jun Christos Gkantsidis Pradnya Karbhari Matt Sanders Li Zou. - PowerPoint PPT PresentationTRANSCRIPT
Beyond Server Selection: Challenges in Multiple-Origin Content Distribution
Mostafa H. AmmarCollege of Computing
Georgia Institute of TechnologyAtlanta, GA
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
n
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 …