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Towards Content Distribution Networks with Latency Guarantees. Chengdu Huang and Tarek F. Abdelzaher University of Virginia. Outline. Background Challenges Contributions Formulation Architecture Evaluation Conclusion. Overview. - PowerPoint PPT Presentation
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Towards Content Distribution Networks with Latency Guarantees
Chengdu Huang and Tarek F. Abdelzaher
University of Virginia
Outline Background Challenges Contributions
Formulation Architecture Evaluation
Conclusion
Overview Our goal is to guarantee a subsecond upper bound
on response time Latency bound formula specified in contract with content
provider Existing research: optimizing average response time
Client perceived latency consists of Latency from client to a CDN server Latency from a CDN server to some other CDN server
(request forwarding) Processing time within CDN servers
Need and Feasibility -- Observations from the Internet Establishing the Need: (Latency Analysis)
Average latency for web objects are a significant fraction of a second
A large portion of latencies exceed a second Bounded-delay CDN is needed
Establishing the Feasibility: (Cost Analysis) Internet latencies (for a fixed pair) are not time invariant
but only oscillate with a small range Spikes are not very common Can be attributed to underutilization of Internet backbone Replica locations are relatively static – maintenance cost
is low
Contributions Mapping the latency bound guarantee
problem to a well-studied graph theoretic problem
Designed and implemented a real-time CDN system on a WAN platform
Extensive evaluation results drawn from an Internet deployment of the service prototype
Mapping The problem of achieving latency bound is mapped to a graph
domination problem Formulation
Given a set of CDN servers S = {S1,…, Sn}, a content object C, and its latency bound L
Construct a graph G whose vertices are S Edge SiSj is added to G iff server Si can download C from server Sj
within a time less than L To find minimal dominating set D: a subset of S with minimal
cardinality that for all u in S - D, there is a v in D for which uv is in G
Nodes represent servers, edges connect neighbors reachable within latency bound dominating set is reachable within latency bound from any server
Mapping
B
F
C
A
G
D
E
Mapping
Graph Domination Problem
B
F
C
A
G
D
E
Existing Graph Domination Algorithms Centralized greedy heuristic
Repeatedly selects the vertex with highest remaining degree
Best approximation known Distributed algorithms
DDCH (INFOCOM’00) LRG (PODC’01) Kuhn and Wattenhofer (PODC’03)
Limitations Performance in asynchronous environment Need multiple rounds to finish: long termination time
We developed a new distributed, asynchronous algorithm
Architectural Challenges The CDN system runs in a highly dynamic
and asynchronous environment How to handle content objects with different
sizes Absence of global knowledge
Challenge: Asynchronous environment Our distributed algorithm
Goal: Decentralized, asynchronous, fast termination Idea
Inspired by the centralized counterpart Nodes independently nominate the neighbor with the highest degree Receiving nomination makes a node join the dominating set and
send out dominator announcement Receiving dominator announcement makes a node refrain from
sending nomination
Insights: High degree nodes quickly join the dominating set Joining of high degree nodes quickly inhibits further nominations
Reachable
Algorithm -- example
B
F
A
G
D
C
A, B, D, E send NOMINATION to C
F, G send NOMINATION to E (random tie-breaking)
A: 4B: 3C: 5D: 2
A: 4B: 3C: 5
A: 4B: 3C: 5D: 2E: 3
A: 4C: 5D: 3
E: 3F: 3G: 3
E: 3F: 3G: 3
C: 5E: 3F: 3G: 3
A is reachable
A is reachable
A is reachable
Degree=3
Degree=5
Degree=4
Degree=2
Degree=3Degree=3
Degree=3
E
Mapping
B
F
C
A
G
D
E
Degree=3
Degree=5
Degree=4
Degree=2
Degree=3Degree=3
Degree=3
Challenge: probing objects of different sizes Probing is needed to estimated latency Latency depends on file size which can be any size,
making probing challenging Solution
Probe a series objects of certain sizes Assuming latency has a simple linear relation with object
size Use a recursive least square (RLS) estimator to estimate
the parameters and More sophisticated probing techniques can be plugged in
Challenge: objects of different sizes Validation of our latency-size model
Challenge: absence of global knowledge The system should perform well without
global knowledge Introduce a parameter: visibility
Percentage of servers in the system each server knows when it starts
Low visibility incurs more replicas Two heuristics to reduce number of replicas
Reciprocal mode Highest degree node exchange
Implementation Instrument Squid Proxy Cache Deployed on PlanetLab
PlanetLab is a WAN platform with more than 100 sites across 20+ countries
Deployed on 30~80 nodes
Experiment on PlanetLab
Evaluation outline Efficiency Latency bound guarantee Absence of global knowledge
Evaluation Number of replicas
2
3
4
5
6
set1 set2 set3 set4 set5 set6
No
rmal
ized
Ter
min
atio
n T
ime
DDCH
LRG
0
5
10
15
20
25
30nodes200ms
30nodes300ms
30nodes400ms
80nodes200ms
80nodes300ms
80nodes400ms
Nu
mb
er
of
Re
pli
ca
s
Centralized
DDCH
LRG
DG
Latency Bound:
Evaluation Termination Time
2
3
4
5
6
set1 set2 set3 set4 set5 set6
No
rmal
ized
Ter
min
atio
n T
ime
DDCH
LRG
30 Nodes 80 Nodes
Evaluation Latency bound guarantee
Baselines Single Server Random (with the same number of replicas) Average Latency Greedy (Qiu INFOCOM’00)
Evaluation Latency bound guarantee
Latency Bound Guarantee Number of Replicas
Evaluation Absence of global knowledge
Conclusion Designed and implemented a CDN system
that provides latency bound Based on a distributed algorithm that
performs well in asynchronous environment Experiment results show that latency bound
can be achieved with a very high confidence