Cache Updates in a Peer-to-Peer Network of Mobile Agents Ilias Leontiadis Master’s Thesis...

Cache Updates in a Peer-to-Peer Network of Mobile AgentsIlias Leontiadis

Master’s Thesis

University of Ioannina,Greece

Supervisors:

Vassilios V. Dimakopoulos

Evaggelia Pitoura

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System model Multi-Agent system (MAS)

Network of software agents Computational resources are distributed across this network The agents cooperate to fulfill a specified task

To do so, they need resources provided by other agents

Open MAS No global knowledge of the agents in the system Thus, agents are not aware of which agent provides a particular

resource

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Resource discovering in open MAS

Approaches: Central directory

There is a single agent which holds a directory, matching resources to agents

Middle agents Some agents (middle agents) keep a fraction of the directory

Distributed caches: each agent keeps part of the directory Performance Fault tolerance

Issue

How to locate an agent that provides a particular resource

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Distributed cache model

Each agent maintains cache entries about k different resources of other agents

Cache entries: keep the contact information of one agent that provides the resource

The system is modeled as a directed graph G(V;E) called the cache network

If agent u v, then v is a neighbor of u

a2

a4

a1

a6

a3

a5

R4: A2 R8: A6

R1

R2

R3

R4

R7

R1

R8

R5

R2

R1

R9

R6

Cache of a1

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Searching for a resource We consider the following flood-based search methods

Plain flood Forward the query to all its neighbors for a maximum number of steps (TTL: Time-To-

Live)

Teeming Propagates the search message only to a random subset of its neighbors Φ the fixed probability of selecting a particular neighbor

Teeming with decay like teeming, but the subset gets smaller as we go deeper into the tree Φ = (1-d)level d < 1. d is called decay parameter

K-Random paths (K-walkers) The agent that initiates the search selects K random neighbors All the other agents forward the message to only one random neighbor

Plain flood Teeming 2-Random paths

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The Problem:Cache network and mobility

Mobility Agents may move (e.g. change IP) Resources may move from one agent to

another

In fact, no one knows the new location of agent A: when it moved, it didn’t inform anyone

a2

a4

a1

a6

a3

a5

a3

Old location

New location

Problem: when an agent A moves, other agents that had cached A’s resources now have invalid cache entries

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Cache update policies

Since the cache entries form our overlay network, what we update is the network topology itself.

We propose a number of update policies that combine two basic techniques: Pull-based

Initiated by the agent that wants to update its cache Push-based

Initiated by the agent that moves.

We consider the problem of cache updates in a peer-to-peer network of mobile agents

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Pull-based methodInitiated by the agent that wants to refresh a cache entry Periodically On-demand

Any flood-based search algorithm can be used Plain flood Teeming (with decay) K-Random paths

Pull methodsearch the network for an agent that knows the new location

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Pull search example

Old location

New location

Agents knows the new location

Agent that pulls

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Pull search example

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Pull search example

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Pull search example

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Pull search example

Replies Directly

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Pull search example

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Push-based method

Pull cannot work alone When an agent moves, it must inform at least one

more agent about its new location

Push methodWhen an agent moves, it “pushes” a message to the network to inform other agents about its new location

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Push example

New location

Needs the update

Needs the update

Needs the update

Old location

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Push example

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Push example

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Push example

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Push example

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Our Update Policies

Plain Push/Pull combination with appropriate variations of flooding

Push with snooping directories combined with periodic pulls a novel variation of push, where agents that receive information about other moving agents maintain it for a short period of time

Inverted Cache with Leasingan informed push approach combined with leasing

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Plain push/pull method

There is no information about which agents need the update

Plain pushThe moving agent blindly floods the network with messages that contain its new location in order to inform others.

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Plain push/pull method A wide push is needed to update a sufficient number of agents

Large TTL and small decay

An agent may not receive the update because: Offline during push Push messages may not reach it

Larger TTL and decay values needed Disconnected network

Such agents should perform on-demand pull.

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Periodic Pull

Another option is to use periodic pull

How frequently do we pull ?

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Periodic Pull

Adaptive Time To Pull period (TTP)

When TTP expires:

If resource was still there Increase TTP

If resource had moved D times Decrease TTP:

Use weight to calculate new TTP

CTTPTTPestimate

bD

TTPTTPestimate

TTPwTTPwPTT estimate )1(

Bhide, M., Deolasee, P., Katkar, A., Panchbudhe, A., Ramamritham, K., and Shenoy, P. 2002. Adaptive Push-Pull: Disseminating Dynamic Web Data. IEEE Trans. Comput. 51, 6 (Jun. 2002), 652-668.

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Push with snooping directories and periodic pulling

Push with snooping directoriesEvery agent monitors the network and maintains a directory of recently moved agents.

This directory is termed snooping directory.

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Push with snooping directories example

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Snooping dir-a1 moved to …


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a1






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Push with snooping directories and periodic pulling

All agents perform periodic local pulls

Periodically, they search the network locally for agents that have recently moved and they update their cache, if necessary

Why periodic pulling? To take advantage of the snooping directories:

if we pull after a long time, information about old moves might have already been deleted

Time between two subsequent pulls < expiration time of entries in the snooping directories

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Push with snooping directories and periodic pulling: Discussion

Before: send the update to as many agents as possible Now: send it to some agents and others will be updated by periodic local pulling.

For example: If agents periodically pull from their two-hop neighborhood:

it is sufficient to push-inform just one agent in every two-hop neighborhood

We prefer to use a k-walkers algorithm for pushing We spawn K-walkers and we would like all agents to be at most two-hops away

from the walk-paths.

Snooping directories with periodic pulling allows us to reduce push-flooding

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Push with snooping directories

Changed location

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Inverted cache push/pull

When the agent moves, it informs the agents found in its inverted cache to update their plain cache in its plain cache to update their inverted directory

By knowing where to send the updates Avoid flooding Low message overhead

Drawback: When an agent adds/replaces/deletes a resource from its cache, a message has to be sent to the resource owner.

A B C A

B

C

Every agent keeps a list of the agents to which it is known, called inverted cache

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Inverted cache push/pull

IssueStoring the entire inverted cache directory may not always be preferable,

as there may exist popular agents/resources

Solution Only a limited directory may be maintained The inverted cache strategy can be combined with Leasing

The agents that are not informed could use on-demand/periodic pull.

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Inverted cache push/pull: Leasing

Every entry in the cache gets a lease time issued by the resource owner

After the expiration the resource owner may delete the entry from its inverted cache, without ever informing the leaser

We could control the size of inverted cache directory through lease times Shorter lease times smaller inverted cache directories Maximum inverted cache directory zero lease times

When lease expires or cannot get/renew a lease Periodic/on-demand pulling

Lease timeTime interval, during which the resource owner guarantees that it will notify the leaser in case the former moves.

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Experimental results Evaluation using a simulator

Create a random graph There are 1000 agents owning 3000 resources. Each agent shares some resources Some resources are more popular than others Initially, all agents have valid cache entries

The simulation runs for a number of turns: In each turn, an agent can

Move Search for a resource (on demand pull if necessary) Make a cache replacement etc.

We keep statistics

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Plain push/pull Extent of flooding and percentage of valid cache entries

0

20

40

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120

0 20 40 60 80 100 120 140 160 180 200 220 240

turn

% o

f va

lid c

ach

e

narrowmediumwidefull

decay TTL

narrow 0.4 4

medium 0.3 5

wide 0.2 5

full 0 5

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Plain push/pullExtent of flooding and message overhead

0

1000

2000

3000

4000

5000

6000

7000

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9000

full wide medium narrow

mes

sag

es

pull

push

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Push with snooping directories and periodic pull Extent of flooding and percentage of valid cache entries

0

20

40

60

80

100

120

0 20 40 60 80 100 120 140 160 180 200 220 240

turn

% o

f va

lid c

ach

e


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Push with snooping directoriesand periodic pull Extent of flooding and message overhead

0

1000

2000

3000

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5000

6000

7000

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9000

10000


mes

sag

es

pull

push

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Plain push/pull vs. snooping

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messag

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pull

push

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turn

% o

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messag

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Inverted cache push/pullwith leasesLease time duration and percentage of valid cache entries

0

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0 20 40 60 80 100 120 140 160 180 200 220 240

turn

% o

f v

alid

ca

che

smallmediumlargevery large

Lease time

small 5

medium 25

large 50

very large 100

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Inverted cache push/pullwith leasesLease time duration and inverted cache directory size

0

5

10

15

20

25

30

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very large large medium small

Lease times

inv

ert

ed

ca

ch

e d

ir s

ize

Max

Average

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Inverted cache push/pullReplacement frequency and message overhead

0

500

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high medium low

Cache replacement frequency

me

ss

ag

es

replacementpullpush

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Comparison Plain push/pull

Easily implemented Can be used in an unreliable environment (on-demand pull) Achieves satisfactory results when using wide flooding High message overhead: It should be used only when we have low mobility Should be used only when we do not want to use any additional memory for

cache update methods

Push with snooping directories and periodic pull Can achieve the same cache quality with plain push/pull but with significantly

less message overhead. Uses additional memory

Inverted cache with leasing Negligible push message overhead One-hop update propagation Replacing a cache entry requires contacting the resource owner

Unsuitable for systems with high replacement ratio. Directory size can become quite large for popular agents Not appropriate for unreliable open MAS

Agents rely on each other to be updated Agents must be online to maintain a valid inverted cache

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Conclusions We considered the problem of cache updates in a peer-to-peer network of mobile

agents Each agent maintains in its cache information about other agents When agents move, cached entries about them become obsolete

We propose a number of update policies that combine pull-based techniques initiated by the agent that wants to update its cache push-based methods initiated by the agent that moves.

Push/pull variations Plain push/pull combination Push with snooping directories and periodic pulls inverted cache with leasing

Our experimental results designate Snooping directory same cache consistency with plain push/pull but with less message

overhead Inverted cache method is cost effective when cache replacements are infrequent

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Future work

Mobile agents resemble mobile (wireless) nodes in ad-hoc networks:

apply our policies to route maintenance in these networks

File replicas in p2p systems apply push/pull techniques to create optimum number of

replicas keep replicas consistent

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Questions ?

Cache Updates in a Peer-to-Peer Network of Mobile Agents Ilias Leontiadis Master’s Thesis...

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