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Beehive : Achieving O(1) Lookup Performance in P2P Overlays for Zipf-like Query Distributions. Venugopalan Ramasubramanian (Rama) and Emin G ü n Sirer. Cornell University. introduction. caching is widely-used to improve latency and to decrease overhead passive caching

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beehive achieving o 1 lookup performance in p2p overlays for zipf like query distributions

Beehive: Achieving O(1) Lookup Performance in P2P Overlays for Zipf-like Query Distributions

Venugopalan Ramasubramanian (Rama)

and

Emin Gün Sirer

Cornell University

introduction
introduction
  • caching is widely-used to improve latency and to decrease overhead
  • passive caching
    • caches distributed throughout the network
    • store objects that are encountered
  • not well-suited for a large-class applications
problems with passive caching
problems with passive caching
  • no performance guarantees
  • heavy-tail effect
    • large percentage of queries to unpopular objects
    • ad-hoc heuristics for cache management
  • introduces coherency problems
    • difficult to locate all copies
    • weak consistency model
overview of beehive
overview of beehive
  • general replication framework for structured DHTs
    • decentralization, self-organization, resilience
  • properties
    • high performance: O(1) average lookup time
    • scalable: minimize number of replicas and reduce storage, bandwidth, and network load
    • adaptive: promptly respond to changes in popularity – flash crowds
prefix matching dhts

0021

0112

0122

prefix-matching DHTs

object 0121

  • logbN hops
    • several RTTs on the Internet

2012

key intuition
key intuition
  • tunable latency
    • adjust number of objects replicated at each level
  • fundamental space-time tradeoff

0021

0112

0122

2012

analytical model
analytical model
  • optimization problem

minimize: total number of replicas, s.t.,

average lookup performance C

  • configurable target lookup performance
    • continuous range, sub one-hop
  • minimizing number of replicas decreases storage and bandwidth overhead
analytical model1
analytical model
  • zipf-like query distributions with parameter 
    • number of queries to rth popular object  1/r
    • fraction of queries for m most popular objects 

(m1- - 1) / (M1- - 1)

  • level of replication
    • nodes share i prefix-digits with the object
    • i hop lookup latency
    • replicated on N/bi nodes
optimization problem
optimization problem

minimize (storage/bandwidth)

x0 + x1/b + x2/b2 + … + xK-1/bK-1

such that (average lookup time is C hops)

K – (x01- + x11- + x21- + … + xK-11-)  C

and

x0  x1  x2  …  xK-1  1

b: base K: logb(N)

xi: fraction of objects replicated at level i

optimal closed form solution

1

[

]

1 - 

dj (K’ – C)

1 + d + … + dK’-1

optimal closed-form solution

, 0  i  K’ – 1

x*i =

, K’  i  K

1

where, d = b(1- ) /

K’ is determined by setting (typically 2 or 3)

x*K’-1 1  dK’-1 (K’ – C) / (1 + d + … + dK’-1)  1

beehive system overview
beehive: system overview
  • estimation
    • popularity of objects, zipf parameter
    • local measurement, limited aggregation
  • replication
    • apply analytical model independently at each node
    • push new replicas to nodes at most one hop away
beehive replication protocol

L 2

0 1 *

B

0 1 *

E

0 1 *

I

0 *

L 1

0 *

0 *

0 *

0 *

0 *

0 *

0 *

0 *

A

B

C

D

E

F

G

H

I

beehive replication protocol

home node

object 0121

L 3

E

0 1 2 *

mutable objects
mutable objects
  • leverage the underlying structure of DHT
    • replication level indicates the locations of all the replicas
  • proactive propagation to all nodes from the home node
    • home node sends to one-hop neighbors with i matching prefix-digits
    • level i nodes send to level i+1 nodes
implementation and evaluation
implementation and evaluation
  • implemented using Pastry as the underlying DHT
  • evaluation using a real DNS workload
    • MIT DNS trace (zipf parameter 0.91)
    • 1024 nodes, 40960 objects
    • compared with passive caching on pastry
  • main properties evaluated
    • lookup performance
    • storage and bandwidth overhead
    • adaptation to changes in query distribution
evaluation lookup performance
evaluation: lookup performance

passive caching is not very effective because of heavy tail query distribution and mutable objects.

beehive converges to the target of 1 hop

evaluation overhead
evaluation: overhead

Storage

Bandwidth

evaluation flash crowds
evaluation: flash crowds

lookup performance

cooperative domain name system codons
Cooperative Domain Name System (CoDoNS)
  • replacement for legacy DNS
    • secure authentication through DNSSEC
  • incremental deployment path
    • completely transparent to clients
    • uses legacy DNS to populate resource records on demand
  • deployed on planet-lab
advantages of codons
advantages of CoDoNS
  • higher performance than legacy DNS
    • median latency of 7 ms for codons (planet-lab), 39 ms for legacy DNS
  • resilience against denial of service attacks
    • self configuration after host and network failures
  • fast update propagation
conclusions
conclusions
  • model-driven proactive caching
    • O(1) lookup performance with optimal replicas
  • beehive: a general replication framework
    • structured overlays with uniform fan-out
    • high performance, resilience, improved availability
  • well-suited for latency sensitive applications

www.cs.cornell.edu/people/egs/beehive

typical values of zipf parameter
typical values of zipf parameter
  • MIT DNS trace:  = 0.91
  • Web traces:
security issues in beehive
security issues in beehive
  • underlying DHT
    • corruption in routing tables
    • [Castro, Druschel, Ganesh, Rowstrom, Wallach]
  • beehive
    • misrepresentation of popularity
    • remove outliers
  • application
    • corruption of data
    • certificates (ex. DNS-SEC)
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