m-top: multi-target operator placement of query graphs for data streams nazario cipriani, oliver...

M-TOP: Multi-Target Operator Placementof Query Graphs for Data Streams

Nazario Cipriani, Oliver Schiller, Bernhard Mitschang

IDEAS 2011 – Lisbon, 21. September 2011

Universität StuttgartSFB 627

Sample Scenario – Distributed Visualization Pipeline

Interpolator:Interpolates the

Velocity Field

Window Tracker:Tracks the

Window States

Fluid Solver:Calcutales theVector Field

Rendering:Project Primitives,

Rasterize Primitives

Mobile Client:

Display Images

Sources Operators Sinks

Update Orientation

Update User LocationPosition Tracker:

Tracks the Positionsof the Mobile Objects

Update Mobile Objects

Update Window States

CalculateStream Lines:Stream Lines

Calculation

CalculateStream Ribbons:Stream Ribbons

Calculation

Environment:Provides Data for

Environment

CalculateMagnitude:

Calculates theVelocity Magnitude

Distribute:Seeds the Stream

Lines StartingPositions

Desktop Computer:

Display Images



Update Orientation

2


Sample Scenario – Distributed Visualization Pipeline

Interpolator:Interpolates the

Velocity Field

Window Tracker:Tracks the

Window States

Fluid Solver:Calcutales theVector Field



Mobile Client:

Display Images

Sources Operators Sinks

Update Orientation

Update User LocationPosition Tracker:

Tracks the Positionsof the Mobile Objects

Update Mobile Objects

Update Window States

CalculateStream Lines:Stream Lines

Calculation

CalculateStream Ribbons:Stream Ribbons

Calculation

Environment:Provides Data for

Environment

CalculateMagnitude:

Calculates theVelocity Magnitude

Distribute:Seeds the Stream

Lines StartingPositions

Desktop Computer:

Display Images



Update Orientation

3


There are More Sample Scenarios…

Navigation application considering current position of mobile objects

Online trajectory compression on mobile devices

Tourist information guide

…

4


Operator Placement Problem

Static Data 2

Sensor1 Sensor2

Static Data 1

5



Static Data 2

Sensor1 Sensor2

Static Data 1

Query Document



Static Data 2

Sensor1 Sensor2

Static Data 1

Pinned operators



Static Data 2

Sensor1 Sensor2

Static Data 1



Static Data 2

Sensor1 Sensor2

Static Data 1

But: Placement is NP-Complete!

Approximation!


Agenda

Multi-Target Operator Placement Problem

Classification of Placement Strategies

M-TOP Approach

Evaluation

Conclusion and Future Work


Customer (c) Bandwidth

> 500 kbit

Cost No costs

Customer (b) Bandwidth

> 1 Mbit

Latency < 2 s

Cost < 1 Unit

Multi-Target Operator Placement Problem – Domain-specific Requirements

Customer (a) Bandwidth

> 10 Mbit

Latency <= 500 ms

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Customer (c) Bandwidth

> 500 kbit

Cost No costs

Customer (b) Bandwidth

> 1 Mbit

Latency < 2 s

Cost < 1 Unit

Multi-Target Operator Placement Problem – Domain-specific Requirements

Customer (a) Bandwidth

> 10 Mbit

Latency <= 500 ms

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Multi-Target Operator Placement Problem!

Different requirements for placement decision!


Multi-Target Operator Placement Problem – But What to Do?

14

O4O2

Q1

Q2O1

O3 S1

Bandwidth > 10 Mbit

Latency <= 500 ms


Multi-Target Operator Placement Problem – But What to Do?

15

Q2

O1

O2

S1

O4

O3Q1

Q2 O1

O2

S1

O4

O3Q1

?

Bandwidth > 10 Mbit

Latency <= 500 ms

O4O2

Q1

Q2O1

O3 S1

Optimize Bandwidth?

Optimize Latency?


A Classification of Existing Placement Strategies

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Operator Placement

TargetsSystem Load Aurora, MedusaLatency SBON, Rizou et al.

single target multi target

fixed variable

Decision Making Decision Making

Cost + Delay Mariposa

a priori a posteriori

Latency + Bandwidth Bonfils et al.Latency + Network Load Rizou et al.

a priori

Metaheuristic Search E.g.: Tabu Search,

Sinnulated Annealing

QoS-aware Placement M-TOP

Bandwidth SAND/BorealisOperator Importance ACES

Latency + System Load Zhou et al.Processing + Transmission + Storage Ying et al.

a posteriori


A Classification of Existing Placement Strategies

17

Operator Placement

TargetsSystem Load Aurora, MedusaLatency SBON, Rizou et al.

single target multi target

fixed variable

Decision Making Decision Making

Cost + Delay Mariposa

a priori a posteriori

Latency + Bandwidth Bonfils et al.Latency + Network Load Rizou et al.

a priori

Metaheuristic Search E.g.: Tabu Search,

Sinnulated Annealing

QoS-aware Placement M-TOP

Bandwidth SAND/BorealisOperator Importance ACES

Latency + System Load Zhou et al.Processing + Transmission + Storage Ying et al.

a posteriori

Multi-target operator placement with variable

targets and a priori decision making


The M-TOP Approach – Target Definition

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Query graph developers provide target specifications

Example QoS-Targets specification

bottleneck condition

relative importance

rank scheme

Target – Bandwidth

(500kBit - 50% - Max)


The M-TOP Approach – Runtime Statistics

QoS-Statistics include Bandwidth, Latency, Reliability, …

Collected during execution of operators

Node–Node QoS-statistics Describe link performance between nodes

Node–Operator QoS-statistics Describe node performance executing a certain operator

Characteristic diagram of QoS-statistics as fixed length time series Describe the expected probabilities for a certain value w.r.t. QoS-statistics Lookup returns min, avg, and max value as well as the characteristic diagram

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Operators

QoS

Nodes

Nodes


The M-TOP Approach

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Conflation

Early Prune

Graph Assembly

Ranking

Mapping

Execution

Find EdgesBetween Nodes

Rank Nodes And Edges

Map Operatorsto Nodes

Execution ofQuery Graph

MergeOperators

NodePreselection

Statistics


The M-TOP Approach – Conflation

21

Conflation

Early Prune

Graph Assembly

Ranking

Mapping

Execution





MergeOperators

NodePreselection

Statistics

Conflation


The M-TOP Approach – Conflation

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Conflation

Early Prune

Graph Assembly

Ranking

Mapping

Execution





MergeOperators

NodePreselection

Statistics

ConflationReduce

number of operators to

place!


The M-TOP Approach

23

Conflation

Early Prune

Graph Assembly

Ranking

Mapping

Execution





MergeOperators

NodePreselection

Statistics


The M-TOP Approach – Early Prune and Graph Assembly

24

Conflation

Early Prune

Graph Assembly

Ranking

Mapping

Execution





MergeOperators

NodePreselection

Statistics

Eliminated by Early Prune




25

Conflation

Early Prune

Graph Assembly

Ranking

Mapping

Execution




MergeOperators

NodePreselection

Statistics

Eliminated by Graph Assembly



bottleneck conditionrelative importance

rank scheme

Target – Bandwidth

(500kBit - 50% - Max)



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Conflation

Early Prune

Graph Assembly

Ranking

Mapping

Execution





MergeOperators

NodePreselection

Statistics

Eliminated by Graph Assembly


Reduce number of

nodes!


The M-TOP Approach

27

Conflation

Early Prune

Graph Assembly

Ranking

Mapping

Execution





MergeOperators

NodePreselection

Statistics


The M-TOP Approach – Ranking

28

Conflation

Early Prune

Graph Assembly

Ranking

Mapping

Execution





MergeOperators

NodePreselection

Statistics


relative importance

rank scheme

Target – Bandwidth(500kBit - 50% - Max)

Rank value 0.75

Rank value 0.6


The M-TOP Approach – Ranking

29

Conflation

Early Prune

Graph Assembly

Ranking

Mapping

Execution





MergeOperators

NodePreselection

Statistics


relative importance

rank scheme

Target – Bandwidth(500kBit - 50% - Max)

Rank value 0.75

Rank value 0.6

Bring to front

candidates by scoring

value!


The M-TOP Approach

30

Conflation

Early Prune

Graph Assembly

Ranking

Mapping

Execution





MergeOperators

NodePreselection

Statistics


The M-TOP Approach – Mapping

By modeling the Mapping Problem as CSP instance NP-formalisms are applicable

Use a meta heuristic to solve the complex problem Genetic Algorithm (GA) but also other methods (Backtracking, …) are possible

Reason: Computationally too expensive to calculate all possibilities and pick best solution

A Genetic Algorithm (GA) is inspired by the Darwinian law that the fittest individuals (or solutions) have higher probability to survive than others

E.g., fitter ones can better adapt to changing conditions

31


The M-TOP Approach – Mapping as Genetic Algorithm

Loop until termination condition is true Maximum number m of iterations reached or best solution did not change

over last k steps

32




over last k steps

Step1: Assign fitness values to individuals Corresponds to the objective function for the mapping step with additional

constraints

33




over last k steps


constraints

34




over last k steps


constraints

Step2: Select individuals for recombination and mutation Step3: Recombine and mutate individuals

35



over last k steps


constraints

Step2: Select individuals for recombination and mutation Step3: Recombine and mutate individuals


36

potentialrecombination points




over last k steps


constraints

Step2: Select individuals for recombination and mutation Step3: Recombine and mutate individuals Step4: Store fittest individuals, go to Step1 if term. condition is false

Return the fittest (the best) individual

37




over last k steps


constraints

Step2: Select individuals for recombination and mutation Step3: Recombine and mutate individuals Step4: Store fittest individuals, go to Step1 if term. condition is false

Return the fittest (the best) individual

38

Mapping


Evaluation – Runtime Measurements

39


Evaluation – Runtime Measurements – Mapping

40

Runtime:much faster than simply probing



41


Objective value:approximation works well (maybe better for bigger

candidate node cardinalities)



42




Additive QoS:approximation and best

latency possible not far-off



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Additive QoS:approximation and best

latency possible not far-off

M-TOP achieves approx. 90%-95%

of optimal solution.


Conclusion and Future Work

Multi-target operator placement with variable targets and a priori decision making

Specify placement requirements in terms of QoS-targets Bottleneck condition, relative importance, rank scheme

To find possible solutions M-TOP adopts a meta-heuristic in form of a GA to sample the search space

Future work include Consider already running query graphs, adapt to changing conditions (e.g.,

operator migration)

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Finish… And Flowers for You!

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m-top: multi-target operator placement of query graphs for data streams nazario cipriani, oliver...

Documents

stream lines calculation

project primitives

primitives rendering

primitives mobile client

sensor1 sensor2 static

primitives update orientation

window states window

velocity field window