Power-Aware Operator placement and broadcasting of continuous query results

POWER-AWARE OPERATOR PLACEMENT AND BROADCASTING OF CONTINUOUS QUERY RESULTS

Panickos Neophytou, Mohamed Sharaf,Panos Chrysanthis, Alexandros Labrinidis

MobiDE 2010 – June 6, 2010

ΕΝΕΡΓΕΙΑΚΑ-ΕΠΙΚΕΡΔΗΣ ΤΟΠΟΘΕΤΗΣΗ ΤΕΛΕΣΤΩΝ ΚΑΙ ΕΚΠΟΜΠΗ ΑΠΟΤΕΛΕΣΜΑΤΩΝ ΕΡΩΤΗΜΑΤΩΝ ΔΙΑΡΚΕΙΑΣ

Motivation

Energy Constraints

Streams: Collection, Processing, Delivery

Social Media Events

Environment Readings

Stock Market

News Events

DSMSBroadcast

Q1Q2Q3Q4

Continuous Queries

(CQs)

Q1Q2Q3

Q1Q2Q3

Problem Definition

Q1

Q2

Q3

Tuning EnergyProcessing Energy






Goal:Design operator placement algorithms that balance the

tradeoff between the overall Tuning and Processing energy

at the clients.

Roadmap

Motivation/Introduction System Model

Stream Processing Model Broadcast Access Model

Operator Placement Algorithms Experiments Conclusion

Stream Processing Model

Client Tuning Energy:

Client Processing Energy:

SelectivityProjectivity

Cost in cycles

Tuning PowerProcessing PowerProcessor Speed

Streams Broadcast Model

Q1Q2Q3Q4

A broadcast is broken into cycles

Cycle:

Q1

Q3

Q4

Broadcast Organization


Q1Q2Q3Q4

A broadcast is broken into cycles

Cycle:

Q1

Q4

Broadcast Organization

Q1 Q4 Q3

Q2


Q1(2)

Q2(3)

Q5(1)

Q3(4)

Q4(5)

Q3

SortedBy size

Tuning Energy

Roadmap




Algorithm - MinDataCutQuery Plan:

MinDataCut gives us the minimal Broadcast Size

Minimal Edge


Clients’ Overall Energy Consumption:

Algorithm - MinPowerCutQuery Plan:


Minimal EdgeClients’ Overall Energy Consumption:

Drawbacks of MinDataCut and MinPowerCut

Q1(1)

Q4(5)

Q3(6)



• Oblivious to Processing costs• High processing energy

Q1(3)

Q4(5)

Q3(6)

14

• Processing-energy aware• High impact on tuning energy

MinDataCut MinPowerCut

MinPowerCut is oblivious to Broadcast Organization

BOSe: Broadcast Aware Operator Selection



Calculate the impact on:1.processing

energy2. global tuning

Query Plan (MinDataCut):

Query Plan (1 step further):

1. Start from the MinDataCut point.2. For each query, calculate the amount of energy

reduction provided by each segment of operators if it were brought back to the server.

3. Bring back the one segment with the maximum reduction.

4. Repeat until no more energy reduction is attainable.

BOSe: Cost-Benefit

Q1A(2)

Q2(3)

Q5(1)

Q3(4)

Q4(5)

Segment from Q1 (at Client N1)

Q1A(2)

Q1B(4.5)

Broadcast Organization (Sorted by size):

tr0 tr1 tr2


N1N2N3N4

N1 N1N4


CostBenefit

Roadmap




Experimental SetupParameter ValuesNumber of queries 20-300 (default 50)Levels per query 2-20 (default 10)Sources tuple rate 500-1000 tuples/secSources tuple size 2000-4000 bytesSelectivity 0.2-1.8, uniformProjectivity 0.5-1.5, uniformOperator costs 100*106-200*106 cycles,

Zipf

Query Workload:

Broadcast:Bandwidth 125000 bytes/sec

Mobile Clients:CPU Speed 1*109 cycles/secProcessing to Tuning power ratio

0.16

Processing to Tuning Power Ratio

BOSe always performs best

22% improvement

Scalability: Number of Queries

Scalability: Number of Operators per Query

Indexed Broadcast Model

Q1(2)

Q2(3)

Q5(1)

Q3(4)

Q4(5)

Q3

Indexed

Tuning Energy

Ix(0.5)

Processing vs. Tuning Power

53% improvement

Conclusions

3 power-aware operator placement algorithms for broadcasting CQ results

BOSe algorithm improves by 53% over centralized processing

Future: Support sharing of operators Support sharing of queries Study the tradeoff between Energy and

Response Time

Thank you – Questions?

Advanced Data Management Technologies Laboratory http://db.cs.pitt.edu

Part of AQSIOS project: NSF GRANT IIS-0534531 NSF career award IIS-0746696

http://db.cs.pitt.edu/

Power-Aware Operator placement and broadcasting of continuous query results

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