PlanetenWachHundNetz

Instrumentation Infrastructure for PlanetLab

Vitus Lorenz-Meyer

Peer-to-Peer

• Distributed on open internet• All participants both receive & provide

services to/from others• Not centrally administered• Membership changes over time (churn)• Example: file sharing (napster, gnutella,…)

Any node can publish a named file Any node can obtain file from another node who

has it. Range of strategies to find nodes containing

desired contentVitus Lorenz-Meyer: Thesis defense University of Texas @ El Paso2

Problem

Rel. Work

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The Problem

• P2P systems hard to tune, requires understanding of complex behavior Requires instrumentation & analysis

• Many P2P systems constructed without scalable instrumentation infrastructure Frequently done in ad-hoc manner

Data transmitted to single collection & analysis node Inadequate for understanding behavior of large systems

of many (hundreds to MILLIONS) of nodes• My work: development of a flexible tool to

enable scalable instrumentation algorithms, data structures

P2P

Related work (1 of 2: cousins)

• Distributed Database Mngmt. Systems Select data at sources Optimize joins (run near to sources…) Commercially used in non-p2p configurations P2P (research): PIER, Sophia

• Sensor Networks Unmanaged radio-connected nodes

provide “network” of surveilance SQL; Compiled into a 3-step process Software communicates through same mechanism IrisNet, TAG

Vitus Lorenz-Meyer: Thesis defense University of Texas @ El Paso4

Related work (2of2: Siblings)

• Aggregation Overlays Information collection subsystem Nodes provide information tuples

Internal aggregation language Computed using parallel prefix of pre-defined

assoc/comm ops Astrolabe, SDIMS, SOMO

• Google’s MapReduce Data selection & aggregation in distributed

system User provides “map” and “reduce” program

Not fully p2p (resource mgmt. overlay)

High-level Approach

• User specifiable programs like MapReduce • Split data collection into 3 ‘phases’

1. Generate values on all nodes2. Pairwise aggregation throughout system3. Evaluate results

• emit measured vals (val,num=1)• Aggregate: (val1+val2,num1+num2)• Evaluate (avg) (val / num)

Easy to use: user provides 3 programs (scripts)

Impl.Rel. Work

Illustration of Binary Aggregation Impl.Rel. Work

Why is this hard in P2P?

• Problem: membership churn• Nodes continuously enter & leave

system• Nobody in charge (p2p)

• Nobody knows membership list!

Exposes following challenges1. Finding all participating nodes2. Constructing an (appx) balanced tree

Impl.Rel. Work

Building Structure Upon Anarchy: Key Based

Routing

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GoalAppr.

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“Chord” Routing

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GoalAppr.

Chord lookup

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GoalAppr.

Building a tree upon KBR

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GoalAppr.

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Building a tree: FTT & KBT

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GoalAppr.

FTT: finger-based tree• Operation associated with a “target” node• Systems send data to finger closest to target• Ambiguous

Depends on all nodes’ fingertables• Tree useful only for aggregation

• KBT: Maps tree on key-space Operation associated w/ target node System/tree-node mapping:

Node assigned to node w/ nearest key

Non-ambiguous Tree useful for both dissemination &

aggregation Single, global tree

Our Structure

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GoalAppr.

• KMR: Subset of KBT, rooted at specific node• One tree / root

Better load-balancing• Tree fully determined by set of active nodes and root

Implementation details

• PWHN-Server layered on FreePastry• PWHN-Client connects to PWHN-Server and

makes query• Callee builds tree making itself root

Vitus Lorenz-Meyer: Thesis defense University of Texas @ El Paso15

Goal

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Appr.

Our Goal

• Develop toolkit for data collection/aggregation in P2P networks Useful for PlanetLab-community

• Extend MR’s model to P2P K.I.S.S.

Users provide programs for gen/agg/eval• Use techniques from P2P

Construct aggregation tree upon key-based-routing

Vitus Lorenz-Meyer: Thesis defense University of Texas @ El Paso16

ExampleImpl. details

Example (1)

• First implementation: Script version, flat, to test approach

• Example 1: Overall average system load Gen emits (1,<1load>,<5load>,<15load>) for each server Agg adds all numbers Eval divides last 3 numbers by first to get average

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EvaluationGoal

Example (2)

• PWHN client (Java) Can start and stop

server Used for specifying

all programs and parameters (Servers, username for flat, method)

Front-end for connecting to servers and making query

Allows saving and graphically representing result

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EvaluationGoal

Example (3)

• Graphing of queried results

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EvaluationGoal

Bar Chart Color bubbles on world map

Example (4)

Graphing of tree

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EvaluationGoal

Graphing of paths of the query

Evaluation

• Minimize disruption Minimize incoming

bytes to client

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SynopsisExamples

•More efficientLower average fan-in of aggregation tree

Evaluation: Fern

• Global Update latency histogram

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SynopsisExamples

10 clients 701 clients

Summary

• PWHN - Instrumentation toolkit Extends MR’s model to P2P Uses P2P techniques (DHTs) Combines FTT and KBT to be more efficient Conclusion: Useful tool that is more efficient

than to build infrastructure into software What did I do?

Survey of systems that provide aggregation in dynamic networks

Classification and naming of aggregation trees upon DHTs

Design and implementation of my own tool (KMR/PWHN)

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Examples

Questions

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Synopsis