Mobile Cloud Computing: A Comparison of PowerPoint Presentation

Mobile Cloud Computing: A Comparison ofApplication Models

Group #6

Chandra

Shekhar

Jammi

(95167373)

Venkata

Sri

Krishnakanth

Pulla

(95911880)

Prashant

Tiwari

(22721608)

Introduction

Cloud computing offers benefits like increased storage, increased processing capacity, flexibility and mobility, reduced cost etc.

In Mobile arena, numerous constraints hinder actual realization of above mentioned

benfits

.

This gives rise to many research opportunities.

We discuss some progress in this domain so far.

What is Mobile Cloud Computing

A combination of cloud computing, wireless infrastructure, portable devices, location based services have given rise to it.

Mobile cloud computing is a model for transparent

elastic

augmentation

of mobile

device’s

capability

.

Using

ubiquitous

wireless

access to cloud storage and computing

resources.

context-aware

dynamic adjusting of offloading in

respect to

change in operating conditions

,

Preserve available sensing

and interactivity capabilities of mobile devices

Challenges and Issues

Existing cloud computing tools consider process parallelizing on massive data volume, large data storage and flexible VM management.

Mobile Cloud Computing requires computation and storage offloaded to the crowd respecting user interactivity in every possible way.

Addressing mobile constraints is the only way.

Challenges in this domain:

How to abstract complex underlying heterogeneous technology.

How to model parameters like low energy capacity and intermittent

dis

-connectivity that influence performance and interactivity of the application.

Integrate processing and storage on cloud considering privacy and security.

Finally,

Constraints for Mobile Devices: Low Processing Capabilities,

Less available energy, Low Memory, Data rates and plans, Intermittent

Dis

-connectivity, Smaller Display etc.

Types of Mobile Application

Offline Applications: Fat Clients with presentation and business logic processed locally.

Data downloaded from backend.

Advantages: Well Integrated, Optimized Performance, Availability :even without network connectivity.

Disadvantages: No Portability, Complex Code.

Online Applications: Only presentation layer at the client. All processing done online.

Assume constant connectivity with backend.

Platform issues; Web Technology to rescue.

Advantages: Multiplatform, Direct and Instantaneous Accessibility to better services.

Disadvantages: Excessive latency for real time responsiveness, no access to device features, sometimes difficult to maintain sessions for a long time.

** Urgent Need to Address Dynamic Environment of Mobiles!

Novel Application Models for Cloud Computing

Augmented execution: Tackle limitation of computation , memory and battery.Systems present ;based on Cloudlets; employ Dynamic VM Synthesis.Cloudlets->Trusted, Resource Rich cluster of computers, well connected to Internet and available to nearby mobiles.Offers low latency, one-hop high bandwidth wireless access.

Fig. 1.

CloneCloud

categories for augmented

execution.

Dynamic Virtual Machine Synthesis

Fig 2: Dynamic VM synthesis.

Elastic Partitioned/ Modularized Application

Due to heterogeneous changing environment, dynamic partitioning and remote execution of applications preferred.

Calling the Cloud: Middleware to dynamically distribute layers of application between device and server, optimizing latency, data transfer and cost.

Uses

AlfredO

framework, which is based on R-OSGI that allows decomposition of Java Application in software modules.

MAUI: Code offload to improve battery life. Profiling Information of methods used to predict future invocations.

Fig 3: Reference Architecture for Elastic Applications

Weblets

-> platform

independent and can be

executed transparently

on different computing infrastructures

including mobile

devices or

IaaS

(Infrastructure as a Service)

cloud providers

such as Amazon EC2 and S3

.

Application Mobility(Move Application Within Hosts During Execution)

Internet Suspend/Resume:VM encapsulates distinct execution& user customization state.DFS transfers state.Migrate complete VM; More BW and Time

JADE:

Adaptive Application Mobility Solution.

Based on Java

Interoperability amongst Mobiles based on different H/W.

Ad-hoc Mobile Cloud

Helpful when:

No or weak Internet Connection or Access to Big Cloud Providers.Helps create computing communitiesReduces Data Transfer towards/from network.Eg: The Hyrax Project which uses Hadoop Framework on Mobile Devices for sharing data and computation.

Fugure

4: A Mobile Cloud

Topics for Exploration

Programming Abstraction: Hide Complexity of Cloud, or have middleware in place.

Cost Model: Consider parameters like module execution time, resource consumption, battery level, monetary cost, security, bandwidth etc.

Adaptation: Two extreme approaches; Laissez-Fair Adaptation and Application Transparent Adaptation.

Topics for Exploration Contd…

Trust, Security and Privacy: Low control over data, potential data loss.

Unlimited Computational Resources to non- trusted elements.

Cloud Integration: Data Persistence

vs

Data Availability.

Shifting modules to cloud &coordination not easy.

Clone-Cloud

Motivation

With the increasing use of mobile devices, mobile applications with richer functionalities are becoming ubiquitous

But mobile devices are limited by their resources for computing and power consumption

Cloud – the place for abundant resources

Clouds provide opportunity to do huge computations quickly and accurately

So why not use cloud for mobile computations??

CloneCloud

CloneCloud

envisions at an architecture that uses cloud to do computations that consume resources badly on mobiles.

It uses the “connectivity” abilities of the mobile devices as a substitute for computational abilities.

It believes in the intuition that “as long as execution on the cloud is

significantly faster

than execution on the mobile

device,

paying the cost for sending

the relevant

data and code from the device to the cloud and

back may

be worth it

.”

It aims at finding the right spots in an application automatically where the execution can be partitioned and migrated to the cloud.

CloneCloud

This model is primarily applicable to application layer VMs (virtual machines) since the instruction sets in it are

bytecodes

and hence provide hardware and OS independence

It exhibits opportunistic but conservative consistency

The partitioning component for finding migration points uses –

static analysis

to find the constraints and the

dynamic profiling

for building the cost model for execution and migration

It finally uses an optimizer that uses the above constraints and cost models to derive the partitions

Partitioning

The partitioning mechanism yields the partitions in the application that are optimal at execution time and energy consumption

It is run multiple time under different conditions and objective functions – stores all partitions in a database

At run time, the execution picks a partition among these and modifies the executable before invocation

It has three components – static analyzer, dynamic profiler and optimization solver

Partitioning

Fig 5: Partitioning Analysis Framework

Static Analyzer

The static analyzer identifies the legal partitions of the application executable according to a set of

constraints

Migration is restricted to the method entry and exit points

Two more restrictions for simplicity

Migration is allowed only at the boundaries of application methods but not core system library methods

Migration is allowed at the VM-layer method boundaries but not native method boundaries

Example…

Static Analyzer - example

Fig 6: Flow Diagram in a Static Analyzer

Static Analyzer - constraints

Three properties of any legal partition

Methods that access specific features of a

machine must

be pinned to the

machine

Methods that share native state must be

collocated at

the same

machine.

Prevent nested migration.

Dynamic Profiler

Profiler collects data that will be used to construct the cost model

Currently using randomly chosen set of inputs

Future work is to explore symbolic-execution-based techniques since randomly chosen inputs may not explore all execution paths

Each execution is run once on mobile device and once on the clone in the cloud

Profiler outputs set of executions S and a “profile tree”, for both mobile device and the clone

Example…

Dynamic Profiler- example

Fig 7: Example of Dynamic Profiler

Profile tree

One node for each method invocation

Every non-leaf

node also has a leaf child called its residual

node

Residual node holds residual cost which represents

the cost of running

the body

of code excluding the costs of the methods called by

it

Computation cost Cc(

i

,

l

); l=0 on mobile device and filled from T, l=1 on the clone and filled from T’

Migration cost Cs(

i

); sum of a suspend/resume cost and the transfer cost

Dynamic Profiler

For energy consumption model, we do the energy measurements with off-board equipment.

CPU activity (processing/idle), display

state (on/off

), and network state (transmitting or receiving/idle

), and

translate them to a

power value using

func

P

Cc(

i

,

0

)= P(

CPUOn

,

ScrOn

,

NetIdle

)*

T[

i

]

Cc(i,1)= P(

CPUIdle

,

ScrOn

,

NetIdle

)

Cs(

i

) = <Cs(

i

) value from time model> * P(

CPUOn

,

ScrOn

,

NetOn

)

Optimization Solver

It aims at picking up application methods

to

migrate

to the clone from the mobile device, so

as to

minimize the expected cost of the partitioned

application.

Decision variable R(m) m= method in the application.

R(m)=1 ->

partitioner

places a migration point at the entry point of the method.

But

n

ot

all partitioning choices for R

(.)

are legal

Optimization problem

Using the decision variables R(.), the auxiliary decision variables L(.), the method sets VM and VNatC for all classes C defined during static analysis, and the relations I, DC and TC

Cost of a partition

The cost of a legal partition R(.) of execution E is given byOptimization objective is to choose R() so as to minimize C(E)

Distributed Execution

Two unique features of

CloneCloud

Thread granularity migration

:

migration operates at the granularity of a thread

Native-Everywhere:

enables migrated threads to use native

non

-virtualized

hardware(GPUs, Cryptographic accelerators etc.)

Migration Overview

Suspend and capture

Thread

migrator

suspends migrant thread

Captures its state, passes it to node manager

Node manager transfers the capture to clone

Resume and Merge

Clone's thread

migrator

captures and packages the thread state

Node manager transfers the capture back to the mobile device

M

igrator

in the original process is given the capture for resumption

Object Mapping

Evaluation

Evaluation

Related Work

MAUI: Making

Smartphones

Last Longer with Code

Offload

-does not support virtualized methods calling native

methods

-requires programmers to annotate methods as

REMOTABLE

-requires a solver to be running at the server at runtime (as opposed asynchronous solving in

CloneCloud

)

Future Work

CloneCloud

focuses on migrating at execution points where no native state need to be migrated

CloneCloud

does not

virtualize

access to native resources that are not virtualized already and are not available on clone

No concurrency

Trust worthiness of clone

Conclusion

A design that achieves basic augmented execution of mobile applications on the

cloud

Prototype delivers up to 20x speed up and 20x energy

reduction

Programmer involvement is

not

required

Opens up a path for a rich research agenda in hybrid mobile-cloud systems

Thanks