Self-organizing Wide-area Network Caches
Self-organizing Wide-area Network Caches
Samrat Bhattacharjee Ken Calvert Ellen Zegura Networking and Telecommunications Group College of Computing Georgia Institute of Technology Atlanta, Georgia, USA http://www.cc.gatech.edu/projects/canes
Sponsors: DARPA, NSF
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Self-organizing Wide-area Network Caches
Conclusions
Results
Self-organizing caching schemes
Caching approaches
Problem statement
Outline
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Self-organizing Wide-area Network Caches
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Problem Statement
Improve client access latencies by associating caches with routers within the network Network Cache − caches reply − serves request #1
server
request # 1 request # 0
Develop algorithms to co-ordinate network caches in order to reduce latency
Assumptions { Request-response paradigm { Requests can be served by caches Components { Location of caches { What to cache and where { Rules for forwarding requests { Protocols to communicate between caches { Cache consistency Examples: Harvest, icp, Squid, Netcache
Wide-area Caching
Self-organizing Wide-area Network Caches
Develop algorithms to coordinate caches to reduce latency
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Self-organizing Wide-area Network Caches
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Active Networks and Network Caching Active Networks provide a programmable network platform an active network
active nodes can execute "user" algorithms active routers could execute new caching algorithms
Use active networking to instantiate self-organizing cache management algorithms within the network
Self-organizing Wide-area Network Caches
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Caching Model and Topology Model
Clients, Servers, Popular Items
Transit-Stub Network Topologies
transit domain T
T
T
T T
T transit router stub router stub domain
hosts Transit−Stub Topology Model
Self-organizing Wide-area Network Caches
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Approaches towards Caching
Caching by location { At transit nodes | Backbone routers { At border stub nodes | Access routers server
T
T
T
T T
T transit caches stub connected to transit caches request
Self-organizing Wide-area Network Caches
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Self-organizing Network Caches
Small caches without regard to location
E ective use of widely distributed caches is dicult server
active caches, run self−organizing algorithms
request
Challenges: Where to cache, how to forward requests?
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Modulo Caching
Spread item over client-server path
Cache radius | modulo caching server 1
2
1
3
2
3 1
2 3
modulo caching radius = 3
1 request
Item is cached once on server-client path every radius hops
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Cache Lookaround Observation: Item location size average item size Use some item memory to store location of nearby items Trade cache memory for location information server
lookaround caching 1 level of lookaround request
Request may be de ected (even o the shortest path to server) to caches within lookaround radius
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Simulation Methodology
Total cache size constant across methods
Example : total number of cache slots in network is 12. S
C
T
C
S
S
S
self−organizing schemes nominal cache size == cache size at each interface = 1 average cache size at each node ~ 1.72 S C T transit−only caching cache size at each interface = 6 average cache size at each node = 12
C
S
S
S
S
C
S
S
S
C
T
stub connected to transit caching cache size at each interface = 3 average cache size at each node = 6 S stub node T transit node
C stub node connected to transit node caching node
Base Topology | 1500 nodes, average degree 3.71 { Average transit-only cache 25 times larger than average active cache, average SCT cache 4.25 times larger
20 25 30 35 Nominal Cache Size
40
Stub connected to Transit Transit Only Modulo Caching Modulo w/ 2 hop Lookaround
Server Dist 0.8, Repeat Prob 0.1, Modulo Radius 3
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No Cache RTL=13.23
10
45
10
9.5
9
8.5
8
7.5 5
10
15
40
Stub connected to Transit Transit Only Modulo Caching Modulo w/ 2 hop Lookaround
20 25 30 35 Nominal Cache Size
No Cache RTL=13.24
Server Dist 0.8, Repeat Prob 0.5, Modulo Radius 3
Result: Varying Cache Size
Self-organizing Wide-area Network Caches
11.5 11 10.5 10 9.5 9 8.5 8 5
Round Trip Latency
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SCT caches are not able to reduce latency as well Self-organizing schemes perform better as cache size increases, and as access correlations increase
Figure 1: Low Access Correlation Figure 2: High Access Correlation
Round Trip Latency
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Self-organizing Wide-area Network Caches
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Round Trip Latency (No Cache: 12.75-13.3
Result: Varying Server Distribution
Cache Size 16, Repeat Prob 0.5, Modulo Radius 3 9.5 9 8.5 8 7.5
Stub connected to Transit Transit Only Modulo Caching Modulo w/ 2 hop Lookaround
7 6.5 0
0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Server Distribution (Fraction of Total Nodes)
Self-organizing schemes are robust against server distribution
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Base
Cache Size 08, Server Dist. 0.8, Repeat Prob. 0.3
Higher Degree Less S-Domain Lower Degree More S-Domain Topology
No Cache No AN 15 Stub connected to Transit Transit Only Modulo Caching 14 Modulo w/ Lookaround Modulo w/ 2 hop Lookaround 13 12 11 10 9 8
Figure 3: Di erent Topologies
13.5 13 12.5 12 11.5 11 10.5 10 9.5 9 2
6 8 Number of Preferred Servers
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No Cache No AN Stub conn. to Transit Transit Only Modulo Caching Modulo Caching w/ Lookaround Modulo Caching w/ 2 Hop Lookaround
Cache Size 08, Sdist 0.8, Repeat Prob 0.3, Modulo Radius 3
4
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Figure 4: Spatial Access Pattern
Round Trip Latency (Hops)
Result: Varying Topology and Access Patterns
Self-organizing Wide-area Network Caches
Round Trip Latency
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Self-organizing Wide-area Network Caches
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Analysis
Probabilistic analysis of cache performance Expressions for latencies for various methods 14 Analysis, rtl=12 Analysis, rtl=14 Simulation, rtl=13.1
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Round Trip Latency
12 11 10 9 8 7 6 5 4 0
100
200
300
400 500 600 Cache Size
700
800
900 1000
Comparison of analytic and simulation results | Lookaround Caches: Base Topology, 50 location pointers per item, 2 levels of lookaround
Analysis of Lookaround Bene t
Self-organizing Wide-area Network Caches
400
500
0.1
0.2
0.3
0.4
0.9 0.8 0.7 0.6 0.5 Local Obj. Cache Fract.
Partitioning of Cache between Local Objects and Location
Caches space is partitioned into data and location pointers Question: What is the optimal amount to devote location pointers?
Round Trip Latency
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14.5
13
13.5
12
12.5
11.5
100
200 300 Cache Size
Valley in plot due to bene ts of lookaround caching
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Increase activity of in-network processing: { Utilize per-application, per-user semantics { Increase per-item state in the network Active Network implementation using ants
Self-organizing schemes reduce latency using far smaller individual caches Self-organizing schemes are robust against varying access schemes, topologies, server distributions Self-organizing algorithms are an active network application that is independent of individual users
Conclusions and Future Work
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