2009 kalman.graffi emanics_aspects_ofautonomiccomputing_20090617

2008_Kalman.Graffi_QuaP2P_Kolloquium_Efficiency.Management.ppt

KOM - Multimedia Communications LabProf. Dr.-Ing. Ralf Steinmetz (director)

Dept. of Electrical Engineering and Information TechnologyDept. of Computer Science (adjunct professor)

TUD – Technische Universität Darmstadt Merckstr. 25, D-64283 Darmstadt, Germany

Tel.+49 6151 164959, Fax. +49 6151 166152 www.KOM.tu-darmstadt.de

© author(s) of these slides 2008 including research results of the research network KOM and TU Darmstadt otherwise as specified at the respective slide13. April 2023

Dipl.-Math. Dipl.-Inform. Kalman Graffi

[email protected]

Aspects of Autonomic Computing in P2P Systems

EMANICS Workshop on Management in P2P, 27.April 2009

KOM – Multimedia Communications Lab 2

The Peer-to-Peer Paradigm

Peer-to-Peer Systems: Users of a system provide the infrastructure Service is provided from users/peers to users/peers Peer-to-Peer overlays:

virtual networks, providing new functionality E.g. Distributed Hash Tables, Keyword-based Search

Evolution of applications File sharing:

No QoS requirements Voice over IP

Real-time requirements Video-on-demand

Real-time and bandwidth requirements Online community platforms

Potential for high user interaction

Costs Security

Quality of P2P Systems

Retrievability

Coherence

Consistency

Correctness

PerformanceScalability

Flexibility

Stability

Dependability

Service Provisioning

Overlay Operations

Individual Node

Complete System

IP Infrastructure

Availability

Reliability

Robustness/ Fault tolerance

Integrity

Confidentiality

Authentication

Non-repudation

TrustValidityEfficiencyAdaptability

Costs Security


Retrievability

Coherence

Consistency

Correctness


Flexibility

Stability

Dependability


Overlay Operations

Individual Node

Complete System

IP Infrastructure

Availability

Reliability


Integrity

Confidentiality

Authentication

Non-repudation



Quality of Service is Key Success Factor

Quality aspects gain importance Reliability: expected professionalism

Client/Server vs. P2P Same functionality

(video streaming, file distribution)

P2P: No maintenance and administration costs

Client / Server:Guaranteed Quality of Service

Goal of P2P-System-Management:Reach and keep predefined quality levels

Costs Security


Retrievability

Coherence

Consistency

Correctness


Flexibility

Stability

Dependability


Overlay Operations

Individual Node

Complete System

IP Infrastructure

Availability

Reliability


Integrity

Confidentiality

Authentication

Non- repudiation


Costs Security


Retrievability

Coherence

Consistency

Correctness


Flexibility

Stability

Dependability


Overlay Operations

Individual Node

IP Infrastructure

Availability

Reliability


Integrity

Non-



Preset Quality Intervals

Goal of Management: reach and keep preset quality intervals

See: K. Graffi, D.Stingl, J.Rückert, A.Kovacevic and R.Steinmetz “Monitoring and Management of Structured Peer-to-Peer Systems”, 9th International Conference on Peer-to-Peer Computing (IEEE P2P '09)


Management & Leadership Strategies

Management by Techniques

Management by ObjectivesManagement by ExceptionManagement by Delegation

Management by Decision RulesManagement by Direction & Control

Management by ResultsManagement by Motivation

Management by Participation

Integrated Management Models

St. Galler Management ModelZürcher Approach

MAM ModelHoshin Management

Other Management Concepts

Total Quality ManagementLean Management

Change ManagementKaizen

7-S-Model


Management by Techniques in the Management Process

Formulating of Objectives

Management by Objectives

Planning

Management by Alternatives

Decision Making

Management by Decision Rules

Management by Exception

Implement

Management by Delegation

Communicating

Management by Participation

Management by Motivation

Controlling

Management by Results

Management by Systems


Autonomic Computing Cycle


Autonomic Computing Cycle in P2P Systems


Applied for P2P Systems

Monitoring

AnalyzePlan

ExecuteHop Count = 20

Hop Count to highIncrease routing table size

Set routing table size to 80


Monitoring in Structured P2P Systems

Goals: Statistical representation of system state

Overlay indepedency user Key Based Routing Interface Lightweight, robust, efficient, fresh, precise


SkyEye.KOM

SkyEye.KOM Is a monitoring mechanism for structured P2P systems Enables gathering of statistics on P2P systems Enables capacity-based peer search

Properties Scalable and self-organizing due to use of underlying DHT Overlay independent

Operation on new ID space Reusing DHT functionality

Peers form a tree topology for aggregating system statistics / capacities


SkyEye.KOM – Design

Utilizes unified ID-Space within the Interval [0;1]

Chord ID space [0, 2^128] Kademlia ID space [0, 2^160]

Applicable on any DHT

Assumes a certain functionality of the DHT

void route(key, msg, nextHop) boolean resp(key)

0 1

11050

2030

40

45

15

0,09 0,2 0,3 0,4 0,51 0,6 0,75 0,9

SkyEye on a Chord Overlay


The Monitoring Tree


SkyEye.KOM – Information Flow

Construction of a tree topology for gathering and aggregating data

Assumes a certain functionality of the DHT void route(key, msg, nextHop) boolean resp(key)

0

0,25 0,75

0,125 0,375 0,625 0,875

0,18750,0625 0,3125 0,4375 0,5625 0,6875 0,8125 0,9375

0,5 1


Simulation Setup (Monitoring)

Evaluated in PeerfactSim.KOM

Already existing: Chord Global Network Positioning delay model Churn model based on KAD measurements of Steiner

Simulation Setup IdealDHT: Dispatches messages to responsible peer 5000 Nodes

Application

Transport

Overlay

User

Sim

ulation Eng

ine

Network

PeerfactSim.KOM

Service


Number of peers during the simulation (KAD churn)

Structure of the monitoring tree during the simulation

Node Count and Churn

All Peers join during the first 300sInitiation of churn after 4000s

Tree adapts to node count Logarithmic height information age

Online and offline peers


Smoothing: Eliminate Outliners

First initial monitoring reveals several outliners

We use smoothing techniques: Median based Exponential Smoothing based


Relative Error

Median based smoothing Loss of information freshness

Exponential smoothing Weighted average over H values, weights for value i: a * (1-a)^i Good precision, few outliners


Branching Factor and Average Freshness

Information freshness Higher node degree (BF) lower tree heigth fresher information at root Small update interval (UI) more frequent updates fresher information

Costs – out bandwidth consumption Solely depends on update interval


Smooth Monitoring View


Autonomic Computing: Next Steps


Rule based Planing

Analysis Compare preset quality intervals with monitored status No deviance detected: nothing to do Deviance detected (hop count):

Wait until current changes take effect

Plan Metric: Hop Count Parameter: Routing table size Adapt number of fingers

+100% if too small -10% if too large

Execute Inform all peers Adapt changes


Stepwise Adaptation

Introduce execution time Give time for changes to take effect Analyze slope of value history, act only if small


Execution: Spreading of new Configuration

Reminder: SkyEye.KOM aggregates system statistics up the tree Every update message is replied an ACK:

Global view from above Policy of new actions to implement

Root has global view

and can reach all leafs

[µ,σ,σ²,Σ, min, max]




Analysis and Planing

Our Approach: Root analyzes monitored data, detects missed quality intervals Root decides which correction task to initiate Spreading the information to all leafs using SkyEye.KOM Peer adopt locally the new rules


+ Decision


Simulation Setup

Simulator PeerfactSim.KOM

Key question: Does the self-configuration work? Are preset quality intervals reached and hold?

Application

Transport

Overlay

User

Sim

ulation Eng

ine

Network

PeerfactSim.KOM

Service


Starting with Low Hop Count

Quick convergence towards preset quality zone

Analysis: To small hop count is detected Routing table size – 10% Quick adaptation


Starting with High Hop Count

Quick convergence towards preset quality zone

Analysis: To large hop count is detected Routing table size + 100% Quick adaptation


Simulation with 10,000 nodes


Conclusion

Management of p2p systems: Reach and hold preset quality intervals Through Autonomic Computing cycle

Monitoring: SkyEye.KOM provides Global view on statistics of running system:

avg./std./min./max on all metrics

Analysis / Plan / Execute in Chord Hop count Routing table size

Evaluation shows Precise and very cost effective monitoring Preset quality intervals are reached and hold

Part of the Skynet Project: Building a self-optimizing, self-aware

autonomous P2P system Have a look at

www.skynet-project.com

H(„my data“)= 3107

2207

7.31.10.25

peer-to-peer.info

12.5.7.31

95.7.6.10

86.8.10.18

planet-lab.orgberkeley.edu

29063485

201116221008709

611

89.11.20.15

?

?Can I keep

the quality of my p2p system?

System quality ?

Information Management Over-Overlay

SkyEye.KOM


Questions?KOM

Have a look at:www.lifesocial.org

www.skynet-project.com

www.kom.tu-darmstadt.de

2009 kalman.graffi emanics_aspects_ofautonomiccomputing_20090617

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