RESOURCE SUBSTITUTION FOR THE REALIZATION OF

MOBILE INFORMATION SYSTEMS

Hagen H

opfner and Christian Bunse

International University in Germany

Campus 3; 76646 Bruchsal; Germany

Keywords:

Resource Awareness, Software Engineering, Adaptivity, Mobile Information Systems.

Abstract:

Recent advances in wireless technology have led to mobile computing, a new dimension in data communi-

cation and processing. Market observers predict an emerging market with millions of mobile users carrying

small, battery-powered terminals equipped with wireless connection, and as a result, the way people use in-

formation resources is predicted. However,the realization of mobile information systems (mIS) is affected by

the users need to handle complex data sets as well as the restrictions of used devices and networks. Hence,

software engineering has to bridge the gab between both worlds, and thus, has to balance given resources.

Extensive wireless data transmissions, that is expensive, slow, and energy intensive can - for example - be

reduced if mobile clients cache received data locally. In this short paper we discuss, which and how resources

are substitutable in order to enable more complex, more reliable and more efﬁcient mIS. Therefore, we ana-

lyze the resources used for data management with mobile devices and show how they can be considered by

software development approaches in order to implement mIS.

1 INTRODUCTION

Due to the recent advent of wireless technologies

evolve, the mobile systems and especially mobile in-

formation systems (mIS) have an impact on numer-

ous facets of our daily lives and the way business is

conducted. According to (Gurun and Krintz, 2003)

mIS are aimed at providing important data in real time

to assist decision makers, will exert great inﬂuence

on communications between businesses and their cus-

tomers, and will or already has transformed the way

we live our lives.

In detail, mobile information systems are of-

ten client server information systems with mobile

clients.

Mobile clients (e.g., laptops, PDAs, smart

phones, or even Java enabled mobile phones) connect

to static and powerful information system servers.

Since such devices are, on the one hand, widely used

but have, on the other hand, various limitations, this

paper focus on the usage of lightweight mobile de-

vices as information systems clients.

In this short paper we do not address pure mobile peer-

to-peer approaches that are discussed in literature but do not

play a bigger role in real life at the moment.

When realizing information systems that use or

support mobile devices not only functionality but also

resources play a major role. Important resources

for mobile information systems (mIS) are: memory

/energy consumption, wireless communication, CPU

performance etc. These resources cannot be viewed in

isolation since they are often inter-related with each

other (e.g., extensive wireless communication has a

negative impact onto energy consumption). Thus, bal-

ancing strategies are required. These strategies can be

subsumed by the term resource substitution. A good

example is the implementation of data caches on cell

phones to avoid mass data transmissions. However,

this requires available memory. Thus systems, with

limited memory and CPU power might use means of

communication for handling complex data. But, wire-

less communication is energy intensive and results in

a reducing the availability of the device. Thus, re-

source substitution requires careful thinking.

This paper presents some initial ideas on how sup-

port static resource substitution at development time

as well as dynamic resource substitution at runtime

can be achieved. The idea of substituting resources is

not new. It was already discussed in (Rudenko et al.,

283

Höpfner H. and Bunse C. (2007).

RESOURCE SUBSTITUTION FOR THE REALIZATION OF MOBILE INFORMATION SYSTEMS.

In Proceedings of the Second International Conference on Software and Data Technologies - SE, pages 283-289

DOI: 10.5220/0001332702830289

 SciTePress

1998) with regard to the energy consumption and on

a more abstract level in (Buchmann, 2002). However,

addressing resource substitution as part of the soft-

ware development process in order to support static

and dynamic resource substitution has not been prop-

erly addressed yet.

The remainder of the paper is structured as fol-

lows: Section 2 gives a brief overview of typical re-

source characteristics. Section 3 shows how these re-

sources are used in mobile (client server) information

systems. Section 4 discusses their substitutability.

Section 5.1 includes issues regarding resource substi-

tution at development time. Section 5.2 presents our

ideas on how to dynamically substitute resources at

runtime. Section 6 summarizes the paper, provides

some conclusions as well as an outlook on future re-

search.

2 RESOURCES

Mobile devices have to be mobile! This implies that

they have to be portable, almost independent from

power sockets and wired network connectors. How-

ever, users want to use them for purposes compara-

ble to tasks supported by classical desktop computers.

Beside communicating via voice or SMS, users want

to access (distributed) information (K

onig-Ries et al.,

2007). In order to implement software for mobile de-

vices that ﬁts the users needs one has to consider the

resources provided by the mobile device and the envi-

ronment infrastructure. In the following we describe

such resources:

CPU: There are various CPUs for mobile devices

on the marked, e.g. XScale or other processors

based on the ARM architecture. Furthermore,

there exist mobile devices that have dual CPUs

like Nokias E90. (Marwedel, 2007)

In fact, the performance of mobile CPUs in-

creases. Samsungs SCH-i760, that will be re-

leased in June 2007, will use a 400 MHz Samsung

SC32442 CPU connected over an 100 MHz inter-

nal system bus.

Memory: Storing data on mobile devices is differ-

ent from storing data on classic computers. In

contrast to big hard-discs (secondary storage) or

DVD-drives (ternary storage) mobile devices typ-

ically use ﬂash memory. Some PalmOS devices

use this media as main memory, too. Others, like

HP iPAQ hx4700 series Pocket PC, separate main

memory (SDRAM) and secondary storage (ﬂash).

Nearly all current mobile devices allow the use of

additional storage media in form of memory cards

(e.g. CompactFlash or SmartMedia). However,

these speciﬁcations describe only the interfaces

and size of the media. In addition, there are also

“real” hard discs (e.g., IBMs Microdrive) that can

be plugged into a CompactFlash Typ-II slot. Such

devices can store up to 4GB of data.

Energy supply: Mobile devices are battery powered.

Their up-time mainly depends on the intensity of

usage (e.g., a Motorola Razor V3 has a standby

time of more than a week. Using it for surﬁng the

web or chatting via GPRS the battery has drained

out after less than one day). Current research tries

to solve this problem by improving hardware as-

pects (e.g., using fuel cells). Although, this seems

to be a step into the right direction there are limita-

tions. For example Nokia decided in 2005 to stop

all research in this direction due to legal issues.

Wireless networks: Data communication in mobile

information systems is wireless. Today, a mo-

bile device has basically network access almost

everywhere. Wireless local-area networks are

hotspoted networks with a reasonable speed.

GSM-networks (and extensions like GPRS) are

slower but widely available. UMTS-infrastructure

has been installed all over Europe but wide-area

coverage is far from existing. In addition to

its limited availability, wireless communication is

energy intensive (see above).

3 RESOURCE USAGE IN MOBILE

INFORMATION SYSTEMS

As mentioned in Section 1 mobile information sys-

tems typically are client-server information-systems

using mobile clients. Therefore, they have to query

data from a server, compute and display them on the

mobile client, and synchronize changes (if existing)

with the server. The ﬁrst phase can be considered

to be a synchronization without client-side updates.

Hence, we are able to analyze the resources needed

for performing the remaining tasks:

If a client starts operation, it requests the required

data from the server. This can be done via replica-

tion, where the client stores the received data locally,

or by using simple query-answer approaches com-

parable to those of web browsing. Of course, lat-

ter can be supported by caching mechanisms. The

amount of required memory depends on whether data

has to be stored locally. Full replication, guaran-

teeing data availability, requires more memory than

caching received data using a cache replacement strat-

egy. Browser-like behavior does not require speciﬁc

ICSOFT 2007 - International Conference on Software and Data Technologies

284

memory for storing results, although in general each

computation requires memory.

Regarding the use of network facilities resource-

replication is one of the most efﬁcient strategies.

Since all required data might be available on the mo-

bile device, only the delta needs to be transmitted.

Caches might replace certain data, thus, if data is re-

quested later, it has to be retransmitted. Without repli-

cation and caching, data has to be transmitted at any

time it is required. Therefore, this approach is net-

work, and indirectly energy, intensive.

However, if data must not be locally stored, CPU

usage can be minimal since local data must not be

ﬁltered or stored locally. The replication of reused

date case requires that all data has to be available on

the mobile device and that requests are posted to the

local replication system. This increases CPU usage.

Concerning caching and especially semantic caching

complex algorithms are needed (Ren and Dunham,

2003). Since only fractions of requested data might be

available, the software system has (a) to ﬁnd reusable

parts, (b) request missing data, and (c) combine them

parts. Unfortunately, some of these algorithms are

known to be NP-hard and therefore CPU intensive.

Storing data is energy intensive but requesting

data wirelessly requires even more energy. Hence,

browsing approaches require more energy than ap-

proaches that store data locally. However, they are not

always the “best” solution since the energy consump-

tion of memory and CPU has to be considered as well.

Experiments (Marwedel, 2007) show, that even com-

plex calculations consume less energy than accessing

the memory. Hence, caching seams to be more energy

efﬁcient than replication. Nevertheless, missing data

must be retransmitted, which might countermand the

savings by caching.

In addition there are techniques that query and

cache data automatically with regard to user needs or

context. However, such hoarding or pre-fetching ap-

proaches have the nearly the same resource consump-

tions as replication and query-answer with caching.

The only difference being an increase in CPU usage

in order to do computations according to user needs

and context awareness.

4 SUBSTITUTABILITY OF

RESOURCES

In the Section 3 we have discussed, that different

techniques for handling data in mobile information-

systems have different resource consumptions. Ta-

ble 1 gives an general overview about possible sub-

stitutions that holds for all distributed systems, and

thus also for client/server information systems with

mobile clients. According to this table the resource

communication might be replaced by CPU or mem-

ory resources (e.g., do computations and store data

locally). Unfortunately, table 1 does not include en-

ergy as a separate resource due to its orthogonal na-

ture being affected by other resources. However, due

ever increasing functionality of mobile devices, lim-

ited battery capacities and environmental protection,

energy consumption has to be addressed too. In the

following we discuss in-depth the substitution pos-

sibilities regarding mobile information system (mIS)

with respect to energy consumption.

4.1 Cpu Vs. Communication

High CPU load can be substituted by data commu-

nication. CPU-intensive calculations might be out-

sourced to a server. Concerning replication or non-

caching approaches this is not relevant if not war-

ranted by the data-processing. However, there are sce-

narios where data processing is complex; e.g. (Ion

et al., 2007) prohibiting mobile phones to handle

them.

As mentioned earlier caching is a CPU-intensive

approach. (H

opfner, 2004) presents an approach for

migrating the task of ﬁnding reusable data in the

cache to the server. In addition, (H

opfner, 2006) an-

alyzes server site cache invalidation. Although, both

approaches require complex software operations on

the server, they drastically reduce client CPU load.

However, replacing communication by CPU load is

only applicable in modern systems given a low num-

ber of data changes concerning server and client. For

example mobile navigation systems do not need data-

communication since all required data is already lo-

cally available. However, most application scenarios

are of a more dynamic nature. Reducing communica-

tion by replication techniques then has an impact on

the resource memory.

4.2 Cpu Vs. Memory

From the database point of view the substitution of

CPU load by memory access and vice versa is the

question of materializing views in a database sys-

tem. Hence, the mobile client has to decide on stor-

ing temporary results. Solving this problem is not

easy (see also Section 2) out of the following rea-

sons: If the mobile device uses only ﬂash memory

as primary (main memory) and secondary storage,

computed data is materialized anyway. If the mobile

device uses a (slow) microdrive materialization may

cause serious performance problems.

RESOURCE SUBSTITUTION FOR THE REALIZATION OF MOBILE INFORMATION SYSTEMS

285

Table 1: Substitutable resources (Buchmann, 2002).

substitute CPU Communication Memory

CPU - Migration of computations Materialization and re-usage

to the server of temporary results

Communication Local execution of - Local data storage

calculations

Memory Data compression and Data management on -

compact data structures the server only

In order to save CPU cycles on the mobile client

one can also think about using additional or improved

index structures to ease data localization. In the con-

text of caching approaches it makes sense to gen-

eralize cache-descriptions (H

opfner and Schallehn,

2004), or to introduce a hierarchical access path in

order to reduce the complexity of the required al-

gorithms. Furthermore, standard compression ap-

proaches might be used for saving memory.

4.3 Memory Vs. Communication

As mentioned in Section 3 replication and caching

provide mechanisms for reducing the amount of com-

munication. However, full replication reduces com-

munication, whereas the efﬁciency of caching de-

pends on the re-usability of cached data (Caracas¸

et al., 2007). So, the beneﬁt of caching depends not

only on the memory but also on the CPU. In general

one can say, data that is available on a mobile device

must not be transmitted. But, redundant data might be

outdated. Hence, replication and caching need syn-

chronization mechanisms that connect to a server or

receive updates via a broadcast channel.

In summary, the substitution of memory by

communication is obvious but not practicable since

wireless data-transmission is expensive. Interest-

ingly, there are approaches following this substitution

strategy (e.g., the internet message access protocol

(IMAP) allows to read email without prior download).

4.4 Energy Vs. CPU, Memory,

Communication

Energy consumption is typically correlated with hard-

ware properties. However, there are strategic issues

that can be addressed from the software point of view:

• Energy consumption associated with wireless

data-transmission is not negligible (Feeney and

Nilsson, 2001).

• Storing data locally requires less energy than

wireless transmissions depending on the data-

size, storage time, etc..

• CPU usage needs comparatively less energy than

memory storage (Marwedel, 2007).

With regard to mobile information systems this

means: Storage- or transmission-intensive ap-

proaches (e.g., replication and query-response) have

a higher energy consumption than caching. However,

this assumption can not be proven now but is a focus

of our current research.

5 SOFTWARE ADAPTABILITY

AND STATIC RESOURCE

SUBSTITUTION

The previous sections talked about resource substitu-

tion strategies in order to reduce the energy consump-

tion of a mIS. Systematic development of such sys-

tems requires sound engineering techniques that sup-

port system adaptation. In general, we have to ex-

amine when such an adaptation occurs and how the

adaptation is performed. As for the former, adapta-

tion can occur at development, compile/linking, load

time, and runtime. For the latter, adaptation can be

accomplished by a change in algorithms/behavior, by

a change of parameters or by a change of the software

composition. In the following we discuss static and

dynamic resource substitution in more detail.

5.1 Static Resource Substitution

Interestingly, research concerning the impact of soft-

ware onto energy-consumption puts a speciﬁc focus

at the later phases of software development. In detail

these are the algorithmic level (i.e., using the most

efﬁcient algorithms and data structures) (Pakdeepai-

boonpol and Kittitornkun, 2006), high-level source

code transformations (Falk and Marwedel, 2005),

compiler optimizations (Chen et al., 2006), code-

compression (Barr and Asanovi, 2006), and operating

ICSOFT 2007 - International Conference on Software and Data Technologies

286

system support (Marwedel, 2007). Unfortunately, for

projects at this stage it is difﬁcult and costly to change

or optimize a system (Boehm, 1981). Thus, energy

saving by resource substitution has to be addressed

already in the earlier phases of software development

(i.e., at the architecture or design level).

Software engineering research mostly focus on a

speciﬁc resource but does not properly address sub-

stitution strategies at development time. In order to

do so two major pre-requisites have to be fulﬁlled.

We need mechanisms to analyze a system concern-

ing its resource usage with respect to its target plat-

form. This includes the prediction of resource usage

as well as the indication of “weak-points” within the

system. In a second step, we then need optimization

mechanisms in form of patterns, anti-patterns or even

idioms. In the long run the vision should then be “ef-

ﬁcient resource usage by construction”.

Concerning estimation (Eskanazi, ) presents a

methodology for estimating memory consumption of

systems built from source code components. He sug-

gests the use of an auxiliary “provides” interface for

each component to specify its memory consumption.

(de Jonge, 2003) presents an approach to memory es-

timation based on the runtime behavior of compo-

nents. The actual estimation is based on a careful

examination of sequence diagrams and the combina-

tion of memory claims and releases of each compo-

nent. (Murphy, 1998) presents an approach based on

information extracted from class diagrams. Concern-

ing energy consumption research mainly focused on

low-level optimization.

Unfortunately, existing approaches focus on a

speciﬁc property or resource and are not embedded

within a development methodology. We therefore,

have to extend existing methods such as the Uniﬁed

Process (Kruchten, 2000) or KobrA (Atkinson et al.,

2002). These approaches have to respect the iterative

and incremental nature of modern processes (i.e., pre-

diction has to become more exact as development pro-

ceeds), their use of modeling languages such as UML,

and the recursive structure of modern, component-

based systems. In summary, we need to know how

a resource might be represented in diagrams at the

design level. how resource interact, and how typical

patterns concerning resource look like.

The next step is then to address optimization, or

in other words how can we improve a system by in-

troducing resource substitution strategies. One such

approach is presented in (Balsamo, 2004). However,

the approach does not use prediction or analysis tech-

niques and does not take platform characteristics or

process issues into account. One solution is to deﬁne

patterns (solutions to recurring problems) on the ar-

chitecture, design, and code-level (i.e., idioms) com-

parable to those presented in (Gamma, 2004). Of

course, each pattern has to be evaluated in order to

create evidence concerning its effects. Based on iden-

tiﬁed weak-points suitable patterns can then be se-

lected and applied in order to establish resource sub-

stitution strategies within a system. In the long run

one might even think about a compiler that provides

options for optimizing code according to known con-

text factors and strategies.

5.2 Dynamic Resource Substitution

Section 5.1 discussed how resource substitution can

be realized at development or compile time. This

might even include a pre-selection of a speciﬁc strat-

egy. However, given the nature of mobile systems

this might not be the “best” solution. Optimization

of one resource (e.g., by using a speciﬁc substitution

strategy) might have unwanted side-effects. Thus,

improving energy consumption might increase CPU

load or vice-versa. Therefore, means have to be de-

veloped that allow systems to adapt themselves at run-

time. Run-time adaptation requires that a system is

aware of its context and status in order to select the

most appropriate strategy. To the extreme this might

also mean “graceful degradation”. Here a system is

degraded in such a manner that it continues to operate,

but provides a reduced quality/level of service rather

than failing completely.

In the context of model-based software develop-

ment, we have to examine the environment and its

varying context factors for deﬁning the level of adapt-

ability a system needs. Therefore, the acquisition

of relevant information and ﬂexible speciﬁcation is a

major challenge. In a second step, the chosen mod-

eling language has to be adapted in order to sup-

port speciﬁcation and translation of adaptive proper-

ties. Finally, standard mechanisms, including service

brokers, facilities for (semantic) lookup, speciﬁcation

matching and service composition, context manage-

ment and speciﬁc QoS functionalities need to be de-

ﬁned.

At the service-level (i.e., services are discretely

deﬁned sets of contiguous and autonomous business

or technical functionalities) there must be an aware-

ness of available resources and other service at run-

time. Concerning self-adaptation there have to be

means for lookup, negotiation and composition of

services. Thus, the speciﬁcation of a service has to

describe both, functional and non-functional proper-

ties. As for the functional speciﬁcation, a widely

recognized speciﬁcation technique is the component

interface deﬁnition language (CIDL) (management

RESOURCE SUBSTITUTION FOR THE REALIZATION OF MOBILE INFORMATION SYSTEMS

287

Group (OMG), 2001). A good survey on current

work in speciﬁcation techniques for non-functional

properties can be found in (Jin and Nahrstedt, 2004).

In the web-service community the web services de-

scription language (WSDL) is widely used. In order

to automate the identiﬁcation of the service capabil-

ities, matching of speciﬁcations and composition of

services, a semantic service speciﬁcation is required.

This, however, also implies the need for correspond-

ing infrastructure mechanisms (i.e. semantic match-

making).

Following the ideas presented in (Bastide et al.,

2006) an approach is needed that supports the dy-

namic adaptation of system or component structures

while preserving their behavior and services. This is

especially useful when thinking about the dynamic re-

deployment of component services according to the

available resources (e.g. CPU, memory). Another ap-

proach might be the design of a reﬂective architec-

ture (Cazzola et al., 2004) in order to provides ob-

jects with the ability of dynamically changing their

behavior using design information. Although both ap-

proaches are a step into the right direction they focus

on adaptation mechanisms but do not provide support

to actually install resource substitution at runtime.

In summary, dynamic resource substitution re-

quires, beneath efﬁcient strategies, systems that are

aware of their context and are able to adapt them-

selves while preserving functionality and/or a spe-

ciﬁc level service quality (QoS). From the software

engineering point of view we further have to think

about adaptable architectures, adaptability speciﬁca-

tion, process integration, and quality assurance of

such systems.

6 SUMMARY, CONCLUSIONS,

AND OUTLOOK

Given the rising importance of computing systems

and especially mobile devices, energy consumption

becomes increasingly important. beneath extending

the up-time of mobile devices, an increase in the use

of natural resources should be avoided. Currents esti-

mates by EUROSTATSpredict that in 2020 10-35 per-

cent (depending on which devices are taken into ac-

count) of the global energy consumption is consumed

by computers and that this value will likely rise even

higher. Therefore, means have to be found to reduce

this value.

The focus of this short-paper is on resource sub-

stitution as a means for energy saving. Therefore,

the paper presented a selection of possible substitu-

tion strategies and discussed how to systematically

use these within software development. Here we dis-

tinguished between development (static) and runtime

(dynamic) approaches and presented initial ideas how

this goal can be achieved. A realization of these ideas

will lead to a signiﬁcant energy saving which indi-

rectly will protect our environment.

As discussed we have presented some initial ideas.

Currently, we are preparing several projects in this re-

gard that are aimed at performing extensive research

in order to create evidence on the effectiveness of

the proposed strategies, as well as methods and tools

to support the development of energy-aware, mobile

systems.

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