icacon 2015 -0.7em9cm0.3pt mobile application offloading

ICACON 2015

Mobile Application Offloading: An Opportunitytowards Mobile Cloud Computing

A. Ellouze, M. Gagnaire

May 22, 2015

Outline

ResearchMotivationOffloading decision modelDecomposition of energy consumptionState of charge of the batteryMobile Application Offloading Algorithm (MAO)Numerical SimulationFuture Work

A. Ellouze, M. Gagnaire ICACON 2015 – Institut Mines-Télécom 2 / 16

Research Motivation

MotivationI An offloading strategy for mobile applications;I Infrastructure deployment – cross optimization tool between Radio

resources disponibility and VMs allocation;


Research Offloading decision model

Mobile Processor Job Modeling

Figure: Processor Sharing Model


Research Offloading decision model

Offloading Decision Model

Concept of critical delay:

I Expected excution delay – Dexpected(j) given N(j),ta(j), CM

I Ideal execution delay – D(j)

I Preferred waiting delay (in relation with QoE) – d(j)

I Critical delay – D∗(j) = D(j) + d(j)

A new application is eventually offloaded if:

Dexpected (j) > D∗(j) (1)


Research Decomposition of energy consumption

Decomposition of energy consumption

I If job j is processed on the mobile terminal, the minimum energyconsumption EM

min(j) is given by:

EMmin(j) = Pactive

M × DMmin(j) (2)

I If this same job is processed on the server, the energy ES(j) consumedby the mobile terminal for this offload is given by:

ES(j) = Etr (j) + EMidle,p(j) + EM

idle,w (j) (3)

Etr (j) = P × V (j)B

(4)

where,P(Ptr );Ptr = min(Pmax ,P0 + 10 × log10(M) + PL) (5)

EMidle,p(j) =

N(j)CS

× P idleM (6)

EMidle,w (j) =

ρ× N(j)CS

2 × (1 − ρ)× P idle

M (7)


Research State of charge of the battery

State of charge of the battery

I 20% as a threshold to offload applications meeting the requirements.

I SoC = 1 − αA

αf , where, αA is the accumulated capacity during the timeperiod [ts, te] at the discharge current rate I and αf is the full capacity.

Figure: Battery model

11J. C. Zhang, S. Ci and H. Sharif, "An Enhanced Circuit-Based Model for Single-CellBattery," IEEE Applied Power Electronic

Conference and Exposition (APEC), February 21-25, 2010, Palm Springs, pp. 672-675.


Research Mobile Application Offloading Algorithm (MAO)

Algorithm Flowchart

Main contributions:I QoE Satisfaction;I Network conditions considered;I User Position within the Cell

considered;I CPU considered;I State of Charge of the battery

considered;

Figure: MAO Flowchart.


Research Mobile Application Offloading Algorithm (MAO)

Applications - Benchmark

Range of applications:I Chess game: processing inherent to the execution of one move on the

chess-board;I Speech recognition: conversion of an analog speech to a text and vice

versa;I Virus scanning: a program able to detect the presence of virus, and if

possible, to kill it;

Table: Applications parameters.

Application CPU Interactivity d(j) inseconds

D(j)∗inseconds

Chess game High High 0.2 7.329Speech recognition Low High 1 1.75Virus Scanning High Low 10 25.671


Research Numerical Simulation

Simulation parametersTable: Simulation parameters.

Parameter ValueCellular layout 1 Single hexagonal cellCell radius 500 mPath loss model PL = A+30× log10(

dd0 )+Xf +Xh+σ

Channel fading Typical urbanCarrier frequency 2GHzSystem Bandwidth 5MHz (25RBs)Thermal Noise per RB −121.45 dBmBit rate of the mobile 600Kbps < B < 16MbpsTraffic Control messages, data trafficNumber of UE 1Number of application activation 100Inter-arrival in seconds λ−1 2, 15, 30Simulation duration 3 hours

22we assume that an application server is located at the antenna’s site and the three applications softwares are installed on the remote

server.


Research Numerical Simulation

Equally mixed applications

Variable loads Variable CPU Speed

15 20 25 30 35 40 45 50 55 600

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0.8

0.9

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inter−arrival of applications (in seconds)

Ene

rgyG

ain

& R

ejec

tionR

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Energy Savings and Rejection Ratio under variable loads (Scenario Mix)

Energy GainRejection Ratio

0 5 10 15 20 25 300

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0.3

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F (CPU Server Speed per CPU Mobile Speed)

Ene

rgyG

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& R

ejec

tionR

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Performance Evaluation With Different Computational Resources

Energy GainRejection Ratio

X axis: Inter-arrival in seconds X axis: F (Computational ratio)Y axis: Energy Gain and Rejection Ratio Y axis: Energy Gain and Rejection Ratio

Conclusion:I When inter-arrivals gets greater, offloading applications is less beneficial.I Having more computational resouces provides avenues for more gains

to be achieved in terms of energy savings.


Research Summary

Summary

I Developed Offloading decision model;

I Designed offloading algorithm tweaking on energy efficiency andQoE(MAO);

I Used realistic applications to simulate;

I Evaluated model through simulated results;


Research Future Work

Perspectives

I Recovery effect of the battery to take into account;

I Overhead virtualization and architectures’s compatiblity to include;

I Cross optimzation tool between radio resources and VM’s placement;


Bibliography

Bibliography

Dusza B, Ide C, Cheng L, Wietfeld C. " An accurate measurement-based power consumptionmodel for LTE uplink transmissions ", In Proc. IEEE INFOCOM (Poster), Turin, Italy, 2013.

X. Ma, Y. Cui, L. Wang and I. Stojmenovic "Energy Optimization for Mobile Terminals viaComputation Offloading", 2012 2nd IEEE International Conference on Parallel Distributed andGrid Computing (PDGC), 2012, pp. 236-241.

J. C. Zhang, S. Ci and H. Sharif, "An Enhanced Circuit-Based Model for Single-CellBattery,"IEEE Applied Power Electronic Conference and Exposition (APEC), February 21-25, 2010,Palm Springs, pp. 672-675.

Dusza, B., Ide, C., Cheng, L. and Wietfeld, C. (2013), CoPoMo: a context-aware powerconsumption model for LTE user equipment. Trans Emerging Tel Tech, 24: 615-632.

UE radio transmission and reception, January 2012. 3GPP TS 36.101, V 9.10.0.

E. Lagerspetz and S. Tarkoma, "Mobile search and the cloud: The benefits of offloading," inNinth Annual IEEE International Conference on Pervasive Computing and Communications,PerCom 2011, 21-25 March 2011, Seattle, WA, USA, Workshop Proceedings. IEEE, 2011,pp. 117-122.

John L. Henning, SPEC CPU2006 benchmark descriptions, SIGARCH Comput. Archit.News34,September 2006. Architecture for LTE Mobile Terminals", IEEE Conference, 2012.


ICACON 2015

Mobile Application Offloading: An Opportunitytowards Mobile Cloud Computing

A. Ellouze, M. Gagnaire

May 22, 2015

The End Related Work

Related Work

I E. Cuervo, A. Balasubramanian, D.-k. Cho, A. Wolman, S. Saroiu, R. Chandra, and P. Bahl,"Maui: making smartphones last longer with code offload," in Proceedings of the 8thinternational conference on Mobile systems, applications, and services"

I B.-G. Cn, S. Ihm, P. Maniatis, M. Naik, and A. Patti, "Clonecloud: elastic execution betweenmobile device and cloud." in EuroSys, C. M. Kirsch and G. Heiser, Eds. ACM, 2011, pp.301-314.

I Shiraz, M. Gani, A. Khokhar, R.H. Buyya, R., "A Review on Distributed Application ProcessingFrameworks in Smart Mobile Devices for Mobile Cloud Computing," Communications Surveysand Tutorials, IEEE , vol.15, no.3, pp.1294,1313, Third Quarter 2013


icacon 2015 -0.7em9cm0.3pt mobile application offloading

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