Kaleidoscope – Adding Colors to

Kademlia

Kaleidoscope – Adding Colors to

KademliaGil Einziger, Roy Friedman, Eyal

Kibbar

Computer Science, Technion

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Kademlia Overview

Kademlia is nowadays implemented in many popular file sharing applications like Bit-torrent, Gnutella, and eMule.

Applications over Kademlia have 100’s of millions users worldwide.

Invented in 2002 by Petar Maymounkov and David Mazieres.

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Kademlia is goodKademlia has a number of desirable features not simultaneously offered by any previous DHT.

– It minimizes the number of configuration messages nodes must send to learn about each other.

– Configuration information spreads automatically as a side-effect of key lookup.

– Nodes have enough knowledge and flexibility to route queries through low-latency paths.

– Kademlia uses parallel, asynchronous queries to avoid timeout delays from failed nodes.

Easy to maintain

Easy to maintain

Fast Log(N) lookups

Fault tolerant

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The “Problem” – one of the key advantages of Kademlia.

Many ways to reach the same value…

K possible peers to make the first step.

The first peer returns k other peers that are closer to the value. Each one of these peers returns other closer peers

And so on…

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Until finally we reach the k-closest nodes.

These nodes store the actual value!

Many possible routing paths…

All roads lead to Rome…

But all of them lead to the same k closest peers.

Popular contentMany users that love Fry… I can’t help you

all, I am just a laptop!

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Caching to the rescue!

Local Cache – After searching an item, cache it locally. (Guangmin, 2009). KadCache – After searching an item, send it to the last peer along the path.A suggestion of Kademlia authors that was not evaluated until now.

KC

LC

Motivation: If a value is popular, we should be able to hit a cached copy before reaching the k-closest nodes.

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The three rules of Kaleidoscope-

“Everything has a color” – We assign each key an additional secondary key called color. This key is generated by hashing the Kademlia key. It has a small domain (e.g. 17).

“Peers only perform lookups for keys of their own color” – if a node wants to find a (key, value) pair of a different color it has to first forward the request to a correctly colored node.

“Only the peer that performed the lookup cache the lookup” – More efficient use of distributed cache content.

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Kaleidoscope

Step 1: forward request to a correctly colored peer.

Step 2: Iterative lookup that favors contacting correctly colored peers.

Step 3: Cache the result, and forward it back to the initiator.

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Forward the request -Looking for

a valueForward the request to one of the peers in your appropriate k-bucket. If there is a peer, favor contacting that peer. If the receiving node is not it will continue forwarding.

The forward phase ends when we reach a peer.

(or if we cannot advance in the XOR metric)

(along the Kademlia lookup path)

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Break symmetry using the color:

K possibilities, but we favor peers.

Looking for a value

We keep picking peers as the next iterative step.

If there is no we can still pick any of the peers.

We continue the lookup until we find the value.

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The value can be found either at the k-closest peers or in

Peers.

Forward the result backwards.Looking for

a value

The value is cached at the node that performed the iterative lookup.

Also, cache content do not violate users privacy.

Thanks !!!!

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Items are only cached on nodes of matching color. Making each node an ‘expert’ for its own color.

More Colors = better cache hit rate:

1. Higher cache capacity– items are only stored in correctly colored peers.

2. Higher cache hit rate (Mathematically analyzed in the paper).

However: 3. It takes longer to reach a correctly

colored peer. 4. We encounter less correctly

colored peers during the lookup. 12

Numerical exampleLets assume that our value was requested before by 10% of the nodes, and that our caches are infinite. How likely are we to hit a cached value ?In local cache, all nodes are symmetric.

10%

10%

10%

10%

10%

10%

10%

10%

In Kaleidoscope, we can only hit when contacting peers. However, when we do contact them the likelihood is increased.

57%

0% 0% 0% 0%0% 0% 0%57%

Comparative results • Emulation – We run the actual implementation,

sending and receiving actual UDP packets. (Only the user is simulated)

• Scale - Different network sizes up to 2,500 Kademlia peers.

• Experimental settings: Each peer does: • 200 requests warm-up.• 500 requests measurement interval.

(Up to 300K find value requests in warm-up and 1.25 Million requests in measurement)

• Experiment generation: Each peer receives a file with 700 requests from the appropriate workload. All users continuously play the requests.

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Wikipedia trace (Baaren & Pierre 2009)

“10% of all user requests issued to Wikipedia during the period from

September 19th 2007 to October 31th. “

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YouTube trace (Cheng et al, QOS 2008)

Weekly measurement of ~160k newly created videos during a period of 21

weeks.

• We directly created a synthetic distribution for each week.

Comparative results

Load is distributed more evenly than with local and KadCache.

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Comparative results

Average lookup cost is reduced. 100 items Kaleidoscope can do better

than 800 items KadCache or Local cache!!!

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ConclusionsOur algorithm contributes in the following ways:

Less messages per lookup – up to 60% reduction from cache-less Kademlia.

Better load distribution – Using an overload protection algorithm that is not surveyed in this talk.

Reproducibility– Kaleidoscope, KadCache and Local are released as part of the open source project OpenKad: https://code.google.com/p/openkad/.

Feel free to use them!

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In the paper…Kaleidoscopes performance is

mathematically analyzed for infinite

caches.

An overload protection mechanism that

helps with efficient load distribution.

Further evaluate Kaleidoscope

according to more metrics, such as

latency success rate and privacy.

In the near future

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The end:

Any questions ?

Thanks for listening!

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Kaleidoscope

Kaleidoscope caches enjoy higher hit rate than local cache… why ?

If we denote by C the number of colors. By N the number of nodes in the system.

For each color on average there are nodes. These nodes perform requests for all nodes in the system and therefore each one performs on average requests for C nodes (including itself).

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Kaleidoscope

Some calculations…

Since any node perform requests of a certain color for C other nodes on average, the probability for a cache miss is the same as the probability that C different nodes did not request the value in the past.

(*) we assume unbounded cache in this calculation.

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