Annotated Callgraph

(1)

상지대학교 컴퓨터공학과 고 광 만

Mobile Cloud Offloading

Mobile Cloud Offloading, kkman@sangji.ac.kr 5/1/17

(2)

People Truly Love Their Smartphones

Mobile Cloud Offloading, kkman@sangji.ac.kr 2

5/1/17

(3)

Source of Dissatisfaction

Mobile Cloud Offloading, kkman@sangji.ac.kr 3

5/1/17

(4)

Process Performance Improvement

5/1/17 Mobile Cloud Offloading, kkman@sangji.ac.kr

4

(5)

Battery Lifetime Capacity Improvement

5

(6)

Reasons for SLOW Enhancement in Battery Lifetime

6

Battery capacity is continually increasing Energy Efficiency ISSUES

But, power consumption of a mobile devices remains steady due to demand for new hardware technologies

¤

Higher resolution display

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Higher pixel Camera

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Faster wireless network

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New sensors including NFC, fingerprint scanner, etc.

(7)

7

Forthcoming Applications, SW, technologies demand extremely higher performance

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Multimedia Processing

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Holographic and 3D media processing

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Bigdata processing

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Augmented Reality

(8)

Mobile apps can’t reach their full potential

8

Augmented Reality

Speech Recognition and Synthesis Interactive Games Slow, Limited

or Inaccurate

Too CPU

intensive Limited

Power Intensive

Not on par with

desktop counterparts

(9)

Problems Ahead

9

WE want

¤ to keep power consumption constraint

¤ to drastically boost up performance

(10)

Computation Offloading

10

Let’s shift difficult and dirty works to someone else, CLUOD.

Computation‐intensive Offloading

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Computation Offloading Architecture

11

(12)

Offloading

solution to augment these mobile systems’ capabilities by migrating computation to more resourceful computers (i.e., servers)

this is different from the traditional client‐server architecture.

A significant amount of research has been performed on computation offloading:

making it feasible

making offloading decisions

developing offloading infra‐ structures

(13)

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Issue 1,

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Offloading may save energy and improve performance on mobile systems.

■

However, this usually depends on many parameters

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the network bandwidths

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the amounts of data exchanged through the networks.

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Many algorithms have been proposed to make offloading decisions to improve performance or save energy

13

(14)

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Issues 2,

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Offloading requires access to resourceful computers for short durations through networks, wired or wireless.

■

These servers may use virtualization to provide offloading services so that different programs and their data can be isolated and

protected.

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Issues 3,

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Offloading may be performed at the levels of

■

methods,

■

tasks

■

applications

■

virtual machines

(15)

■

The common approaches used to make offloading decisions:

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why to offload (improve performance or save energy)

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when to decide offloading (static vs dynamic)

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what mobile systems use offloading (laptops, PDAs, robots, sensors)

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types of applications (multimedia, gaming, calculators, text editors, predictors)

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infrastructures for offloading (grid and cloud computing).

15

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Case Study: Face‐detection

17

(18)

Case Study: Real‐time Subtitle Generator

18

(19)

More Offloading Extension

19

● Mobile Cloud Computing (MCC)

●

Computation Offloading + Capability Extension

●

Computation‐intensive Offloading

● Improve performance and energy efficiency

● Communication‐intensive Offloading

●

Capability Extension

● Enable impossible things by using cloud resource other than

processor

(20)

20

(21)

Types of Computation Offloading

21

(22)

Method‐level Offloading

22

(23)

MAUI: Mobile Assistance Using Infrastructure

23

MAUI Contributions:

Combine extensive profiling with an ILP solver

¤

Makes dynamic offload decisions

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Optimize for energy reduction

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Profile: device, network, application

Leverage modern language runtime (.NET CLR)

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To simplify program partitioning

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Reflection, serialization, strong typing

(24)

MAUI Architecture

24

Maui server Smartphone

Application

Client Proxy

Profiler Solver Maui Runtime

Server Proxy

Profiler Solver Maui Runtime

Application

RPC

Maui Controller

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How Does a Programmer Use MAUI?

25

Goal: make it dead‐simple to MAUI‐ify apps

¤

Build app as a standalone phone app

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Add .NET attributes to indicate “remoteable”

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Follow a simple set of rules

(26)

Language Run‐Time Support For Partitioning

26

Portability:

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Mobile (ARM) vs Server (x86)

¤

.NET Framework Common Intermediate Language

Type‐Safety and Serialization:

¤

Automate state extraction

Reflection:

¤

Identifies methods with [Remoteable] tag

¤

Automates generation of RPC stubs

(27)

MAUI Profiler

27

Profiler

Callgraph

Execution Time

State size

y Network Latency

h Network Bandwidth Device Profile

CPU Cycles

Network Power Cost Network Delay Computational Delay Computational Power Cost

Computational Delay Callgraph Annotated

(28)

Is Global Program Analysis Needed?

28

Yes! – This simple example from Face Recognition app shows why local analysis fails.

FindMatch 900 mJ

InitializeFace Recognizer

5000 mJ 1000mJ

DetectAndExtract Faces

15000 mJ User

Interface

Cheaper to do local

(29)

29

FindMatch

InitializeFace Recognizer

1000mJ

DetectAndExtract Faces

User

Interface 25900mJ

Cheaper to offload

(30)

Can MAUI Adapt to Changing Conditions?

30

Adapt to:

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Network Bandwidth/Latency Changes

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Variability on method’s computational requirements

Experiment:

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Modified off the shelf arcade game application

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Physics Modeling (homing missiles)

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Evaluated under different latency settings

(31)

How much can MAUI improve performance?

31

Face Recognizer

(32)

offload

32

Up to 40% energy savings on Wi‐Fi Solver would decide not

to offload

Arcade Game

(33)

Can MAUI Run Resource‐Intensive Applications?

33

CPU Intensive even on a Core 2 Duo PC

Can be run on the phone with MAUI

Translator

(34)

Can MAUI Run Resource‐Intensive Applications?

34

CPU Intensive even on a Core 2 Duo PC

Can be run on the phone with MAUI

Translator

(35)

Cuckoo: A Computation Offloading Framework

35

(36)

36

(37)

Is Offloading Really Effective ??

37

(38)

Is Offloading Really Effective ??

38

(39)

Is Offloading Really Effective ??

39

(40)

Is Offloading Really Effective ??

40

(41)

Mobile Edge Computing

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● Mobile Edge Computing enables mobile subscribers to access IT and cloud computing services at the close proximity within the range of Radio Access Network (RAN).

● The main goal of Mobile Edge Computing to reduce latency by

bringing the computation and storage capacity from the core WAN to the edge network.

● Mobile Edge Computing can be defined as

“Mobile Edge Computing is a model for enabling business oriented, cloud computing platform within the radio access network at the close proximity of mobile subscribers to serve delay sensitive,

context aware applications.”

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Enpiler: Energy‐aware offloading framework through comPiler code analysis techniques

43

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IoT Applications in Mobile Edge Cloud Computing Environments

44

Mobile Edge Server - 2

Mobile Edge Server - n

(AR/VR Gaming Processing) Mobile Device

Mobile Edge Server Side

(Sensing Bigdata Processing) Mobile Device

(Multimedia Image Processing) Mobile Device Concurrent

Modules

Distributed Offloading

Concurrent Modules

Key Management Server

(45)

Selected Publications

45

■ 고광만(교신) 외 4인, "Enabling Far-Edge Analytics: Performance Profiling of Frequent Pattern Mining Algorithms," Sensors(IF: 2.033, Q2), April 2017.

■ 고광만(교신) 외 4인, "SIMDOM: A Framework for SIMD Instruction Translation and Offloading in Heterogeneous MCC Architectures,"

Transactions on Emerging Telecommunications Technologies (IF: 1.130,

Q1), February 2017.

■ 고광만(교신) 외 3인, Optimization Techniques to Enable Execution

Offloading for 3D Video Games", Multimedia Tools and Applications(IF:

1.345, Q2), July 2016.

■ 고광만(공동) 5인, "A Case and Framework for Code Analysis Based

Mobile Application Energy

Estimation," International Journal of Communication Systems (IF 1.02, Q3), December 2016.

■ 고광만(공동) 외 6인, "Towards native code offloading based MCC

frameworks for multimedia applications: A survey," Journal of Network and Computer Applications(IF: 2.413, Q1), November 2016.

■ 고광만(교신), "Reduction of Media Servers Overload with Energy-saving

Adaptive Streaming", Mobile Information Systems(IF: 1.463, Q2), Article

ID 7462821, Jan., 2016.

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Q&A

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