EXPLORATION OF TECHNOLOGIES FOR AUTONOMIC

DEPENDABLE SERVICE PLATFORMS

Eila Ovaska

, Liliana Dobrica

, Anu Purhonen

and Marko Jaakola

VTT Technical Research Centre of Finland, Kaitoväylä 1, 90571, Oulu, Finland

University Politehnica of Bucharest, Splaiul Independentei 313, 060042, Bucharest, Romania

Keywords: Self-adaptive, Service architecture, Ontology, Dependability, Modeling.

Abstract: A city is smart if it can provide ambient services for citizens and especially for professionals who have to

tackle emergency situations, including small and wide scale accidents and incidents. These ambient services

embody intelligence of autonomic systems based on heterogeneous execution platforms enhanced with ser-

vices that provide mechanisms for self-adaptation of dependable applications. This paper explores enabling

technologies of autonomic dependable service platforms from three viewpoints: i) architectural options, ii)

ontology models for services, context and dependability, and iii) modeling methods and practices for

achieving high quality service platforms and intelligent applications. The main findings are summarized as a

set of research items that need to be carried out for achieving an autonomic dependable service platform.

1 INTRODUCTION

Besides situation based information, smart cities also

provide quality-critical services for their communi-

ties, i.e. high quality services for professionals such

as firemen, medical service providers, police, etc.

These workers are responsible for real-time reaction

in emergency situations. An emergency situation –

caused by an extensive accident - means that the

environment immediately changes, and therefore,

the type and amount of information required for de-

cision making and actions are also changing. These

changes affect on the use and availability of compu-

ting and communication resources and the applica-

tions and services they provide for problem solving.

Smart city environments heavily rely on a multi-

tude of sensor networks, embedded systems and

devices that produce a large amount of data to be

analyzed and reacted on in the short run by the secu-

rity and safety monitoring processes executed by

critical information systems. In this paper, we focus

on heterogeneous systems that embody large scale

sensor networks, embedded systems, mobile devices

and enterprise systems. On the one hand, sensors

interact with other nodes in various ways and com-

munication may be periodic or ad-hoc over wired

and/or wireless networks. Moreover, computers dif-

fer in their architecture and computing resources,

such as CPUs, operating systems, processing power,

amount of memory, energy

requirements, etc. For

example, motes that are tiny devices powered by a

battery and featuring low-power wireless communi-

cation capability bring challenges for application

developers. On the other hand, information systems

bring tight quality requirements, which are to be

fulfilled in any case, preventing human and econom-

ical damages, by reacting with adaptive and auto-

nomous behavior to changing situations and disap-

pearing resources.

The development of smart cities benefits from

the findings and experiences gained from worldwide

sensor webs (Balazinka et al., 2007) and service

oriented architectures, also applied to sensor webs

(Chu and Buyya, 2007). However, smart city appli-

cations require more; the platform shall be able to

adjust its behavior based on defined dependability

requirements, users’ goals, quality of services and

quality of available resources. Thus, this adaptation

requires intelligence that enables real-time identifi-

cation, reasoning and proactive reaction on alerts.

The objective of this paper is to explore the ex-

isting software technologies that are applicable for

developing a service platform that is able to make

autonomously the needed corrective and preventive

actions in abnormal situations, and thereby provide a

dependable infrastructure upon which adaptive ap-

plications can easily be developed and deployed. In

particular, we focus on how dependability of appli-

cations can be guaranteed in ad-hoc situations.

115

Ovaska E., Dobrica L., Purhonen A. and Jaakola M..

EXPLORATION OF TECHNOLOGIES FOR AUTONOMIC DEPENDABLE SERVICE PLATFORMS.

DOI: 10.5220/0003497301150124

In Proceedings of the 6th International Conference on Software and Database Technologies (ICSOFT-2011), pages 115-124

ISBN: 978-989-8425-76-8

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

The main contributions of this paper are 1) the

options of adaptive service architectures for auto-

nomic dependable service platform, 2) the inventory

of potential ontologies that could be exploited in the

development of a dependable service platform, and

3) the approaches applicable in the development of

quality critical and situation based smart city appli-

cations. In summary, self-adaptation is based on

context-awareness, realized as situation based beha-

vior that takes into account the functional and quali-

ty properties of the environment and system itself,

and the needs of system’s users.

The structure of the paper is as follows. Section 2

explains the concept of an autonomic dependable

service platform. Section 3 explores existing adap-

tive service architectures and platforms. Section 4

introduces a selected set of ontologies for

representing services, context and dependability.

Section 5 discusses modeling methods and tech-

niques applicable for modeling context and depen-

dability. Section 6 summarizes our findings, and

conclusion closes the paper.

2 THE CONCEPT

The increasing amount of cheap multi-purpose sen-

sors enables enhancing applications with intelligence

that adapts their behavior reactively and proactively

(Figure 1). Sensor data gathered from the physical

environment is clustered and merged with the sys-

tem’s internal state to define the application context.

Thereafter, an application adapts its behavior based

on the context, the quality of used services and

available resources. Even though applications are

founded on self-adaptive platform services, depend-

able applications require even more; the physical

environment is also to be reorganized. Due to high

cost of manual reorganization, sensor networks

ought to be self-organizing wireless networks.

An autonomic dependable service platform is a

key enabler of dependable smart city applications

with situation based behavior and ability to exploit

and tolerate the evolution of environment, technolo-

gy and application fields.

Therefore, the following technologies embody a

great potential for autonomic service platforms:

• Service oriented architectures are applied to in-

formation systems, embedded systems, mobile

devices and sensor networks. Thus, self-

adaptation based on service orientation would be

a widely exploitable cross-domain solution.

• Ontologies are used for sharing domain know-

ledge among application and platform

Wireless

Sensor

Networks

Wireless

Sensor

Networks

Reactive and proactive applications

Autonomic Dependable Service Platform

Self-adaptive

Services

Network

Self-organisation

Context

management

QoS

management

Wireless

Sensor

Networks

Environment

Figure 1: Overview of an autonomic dependable service

platform.

developers. Moreover, ontology orientation is

exploited for developing reusable assets and

managing the evolution of systems and their ap-

plications.

• Quality of service management is the mandatory

requirement because of the purpose of the plat-

form: to execute dependable applications on top

of an autonomic service platform.

• Model driven development helps in sharing

knowledge, information and artifacts in an unam-

biguous way, understood by both people and ma-

chines. Thus, models play an important role at

design time and at run time.

3 SELF-ADAPTATION

TECHNOLOGIES

3.1 Self-*ilities of Autonomic Systems

An autonomic system has six characteristics (Salehie

and Tahvildari, 2009) also called self-*ilities:

• Reflexivity. The system must have knowledge of

its capabilities, boundaries and interdependen-

cies, and be aware of its possible configurations

and their impact on particular quality require-

ments.

• Self-configuring. The system provides increased

responsiveness by adapting to dynamic changes

occurred internal of the system or in the external

operating environment.

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• Self-optimizing. The system provides operational

efficiency by tuning resources and balancing

workload.

• Self-healing. The system provides resiliency by

discovering and preventing disruptions as well as

recovering from malfunctions.

• Self-protecting. The system secures its assets by

anticipating, detecting and protecting against at-

tacks.

• Adapting. The core of the system is a control

loop - sensing, decision making, and acting. The

adaptive mechanisms are typically inspired by

work on machine learning, multi-agent systems,

and control theory.

Adaptive service platforms most often support self-

configuring or self-optimization (Nahrstedt et al.,

2001); (Zeng et al., 2004). However, recently self-

healing has started to receive more attention (Baresi

et al., 2008) (Cardellini et al., 2009c). As the system

is autonomic in its ‘normal’ operation, it should be

able to survive failures and to adjust system’s cha-

racteristics to altering loads and resources autono-

mously (Botts et al., 2008).

3.2 Adaptive Middleware Architecture

The quality management in service-oriented systems

requires additional features of the service brokering.

QoS Broker utilizes a centralized approach where

requests from clients are handled by a QoS-aware

broker that evaluates each request using a perfor-

mance model (Menasce and Dubey, 2007). In a

quality assurance framework the brokers are created

on-demand and all the service providers and con-

sumers can have their own brokers (Robinson et al.,

2008). AgFlow has a separate service composition

manager that forms a composite service from proper

sub-services, requested from the service broker

(Zeng et al., 2004).

When quality management is added to the sys-

tem it may cause that all the services are required to

be changed to support the new middleware. VO-

LARE (Papakos et al., 2009) adds an adaptation

middleware between Web services and the broker

module that monitors the resources and context of a

device, and adapts service requests accordingly. It

also adapts the QoS levels advertised by service

providers, to realistically reflect each provider’s ca-

pabilities at any given moment. The adapted service

descriptions and advertisements are syntactically

identical to un-adapted versions, allowing interope-

rability with non-VOLARE nodes.

In the ubiquitous environments the time spent for

quality management is critical. Consequently, the

quality-aware service discovery can be divided into

two levels (Cardellini et al., 2009a). First, the ser-

vice provisioning level identifies the actual pool of

concrete services that will be used to implement the

component functionalities so that the user’s end-to-

end requirements are fulfilled and the service bro-

ker’s utility function is maximized. At the second

phase, the role of the service selection level is to

determine, from the pool identified by the service

provisioning, the actual concrete services which are

bound to each incoming user request. The service

provisioning can operate at a slower pace than the

service selection.

MUSIC platform (Rouvoy et al., 2009) supports

self-adaptation in ubiquitous and service-oriented

environments. It provides an adaptation planning

framework for managing the frequent and unex-

pected changes in the execution context of mobile

applications. The purpose of the adaptation planning

framework is to evaluate the utility of alternative

configurations in response to context changes, to

select a feasible one for the current context and to

adapt the application accordingly.

The control loop of quality management can be

roughly decomposed into monitoring, analyzing,

planning and execution (Kephart and Chess, 2003).

Monitoring means collecting the data needed for

QoS adaptation from the system under interest. For

example, in (Robinson et al., 2008) monitors are

created at runtime to transparently intercept requests

and responses between consumers and providers.

Analyzing is the phase, where the collected data is

combined to form proper QoS metrics, and possibly

also predictions of future states are made. Predic-

tions are used for finding out quality violations

proactively (Robinson et al., 2008); (Papakos et al.,

2009). Planning is the control phase, where the re-

quired action is selected. Planning can be an aggre-

gate of local and global level reasoning, (Mokhtar et

al., 2007). Local QoS requirements are filled by in-

dividual capabilities and global QoS requirements

by the service composition. The optimization prob-

lem can be solved using, for example, models (Ro-

binson et al., 2008) (Cardellini et al., 2009b) or

fuzzy logic (Nahrstedt et al., 2001).

Execution of the decisions made may include

adaptations at two different levels (Nahrstedt et al.,

2001): 1) the resource management level performs

application-neutral adaptation, and 2) the service

management level is responsible of the application

adaptation. The application adaptation can be about

adjusting application components and configura-

tions, or about selecting appropriate service provid-

ers for services.

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Sometimes it is not possible to find any service

providers that can fulfill the quality requirements set

by the client. In that case negotiation is needed. In

(Cardellini et al., 2009a) the service provisioning

level takes care of the negotiation with the service

provides so that the actual service selection is made

faster. In (Robinson et al., 2008), the consumer bro-

ker negotiates with the provider brokers.

The existing adaptive middleware solutions seem

to cover collectively all the main features required

for a dependable services platform. For example, the

MUSIC platform is able to take into account both

context and quality issues and in addition, it is de-

signed for ubiquitous and service-oriented environ-

ments. However, although it seems to support self-

configuring and self-optimizing it is unclear how it

would suit to an self-healing and self-protecting en-

vironment.

The performance and dependability of the exist-

ing platforms themselves are not yet clear. Some of

them have been designed for ubiquitous environ-

ments, but they have been applied only in restricted

contexts. Especially, more information is needed

about how these systems would be able to support

the management of large amounts of data in a short

time frame required by the critical services in the

smart city environment.

3.3 Enhanced Intelligence

An intelligent system is commonly thought to have a

capability of learning. The goal of learning is to im-

prove the performance of a system with respect to its

environment. Machine learning paradigms can be

divided into three major areas (Könönen, 2004):

• Supervised learning: A teacher, which knows the

correct input-output pairs, provides these to the

system, which is learning. The system tries to

emulate the teacher’s behavior and also general-

ize unseen data.

• Unsupervised learning: No teacher, and thereby

no correct outputs exist in the learning process.

• Reinforcement learning: Neither here are correct

outputs known, but the system learns those by in-

teracting with its environment – the mechanism is

called rewarding.

Supervised and unsupervised techniques have both

training phases, although the unsupervised version

has no labels – correct ‘answers’ of the training data

– available (Jayaraj et al, 2008). A complete ma-

chine learning method includes steps of selecting a

candidate model, and then estimating parameters for

it. The estimation is done with a learning algorithm

and available data. In practice, supervised learning

utilizes often an error function, which should be na-

turally minimized. Unsupervised learning uses clus-

tering; similarity of elements in the same cluster

should be maximized, and similarity of elements in

different clusters should be minimized.

In the formal model of the reinforcement learn-

ing, the system has a (discrete) state, which perce-

ives either completely or partially, a group of actions

possible in that state, and a reward which is received

when a new state is entered. The system’s behavior

and knowledge of the environment are modeled with

a function. Usually learning is not about maximiza-

tion of direct reward belonging to the state transi-

tion, but long-term performance. Reinforcement

learning can be applied to proactively adapting the

service platform for stress peaks caused by users,

overwhelming data or/and increased attacks.

Prediction provides four kinds of improvements

to self-adaptation (Cheng et al., 2009): prevents un-

necessary self-adaptation, reduces disruption from

incremental adaptation, enables pre-adaptation to

seasonal behaviour, and improves overall choice of

adaptation. Smart city applications would benefit

from proactive capabilities, for example, with using

the sensor information for discovering activity pat-

terns that might lead to emergency situations. In that

way, it could be possible to act on the situations be-

fore they occur and possibly prevent them. The simi-

lar approach is common in smart-home systems. For

example, CASAS (Rashidi and Cook, 2009) is an

adaptive smart-home system that utilizes machine

learning and data-mining techniques in order to

detect activity patterns, generate automation policies

for those patterns, and also adapt to the changes in

those patterns.

Learning capabilities can be also used for self-

protection. For example, an approach for wireless

anomaly based intrusion detection and response sys-

tem uses learning for detecting complex malicious

attacks (Fayssal et al., 2008). Training sets are used

by the system to generate rules for the behaviour to

be considered normal. Those rules are used during

runtime to detect complex wireless attacks and gen-

erate counter measures to protect one or more wire-

less resources and the privacy of their users. Fast

recovery without human intervention requires proper

policy management mechanisms and automated

ways to learn and derive policies (Fuad, 2010). Un-

like the current self-healing systems that most often

diagnose and heal failures after they have occurred

rather than anticipating failures, in consequence-

oriented diagnosis and recovery the host predicts or

diagnoses the possible consequences from the symp-

toms (Dai et al., 2009).

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4 ONTOLOGIES

4.1 Service Ontologies

Ontologies are used to represent knowledge in a

uniform way that machines are able to process. On-

tologies provide knowledge for describing the re-

quired and provided capabilities of a service, ability

and rights of achieving a service, and the quality

guaranteed for a service. The eXtensible Markup

Language (XML), Resource Definition Framework

(RDF) (Hayes, 2004), and Web Ontology Language

(OWL) (McGuinness and van Harmelen, 2004)

schema provide a basis for service description lan-

guages and ontologies, such as Web Ontology Lan-

guage for Services (OWL-S) (Barstow et al., 2004),

Web Service Modeling Ontology (WSMO)

(WSMO, 2004), and Internet Reasoning Service

(IRS) (Motta et al., 2003), which in turn provide

building blocks for service semantics. The above

mentioned service ontologies describe functionality

of services. Another set of ontologies focuses on

service context and quality properties. However,

existing service ontologies focus mainly on Web

services, and none of them provides complete sup-

port for service descriptions as required in adaptive

service platforms. After analysis of the existing on-

tologies (Kantorovitch and Niemelä, 2008), a con-

clusion is that the existing service ontologies have to

be enhanced.

In the software architecture field, the use of

viewpoints is a community-wide accepted approach

to cluster stakeholder-related concerns into a single

view. This principle can be lent to describing seman-

tics of services. The use of multiple views is a ne-

cessity; the interests of stakeholders differ, applica-

tion domains differ, and service functionality and

quality differ according to the usage and execution

contexts. Moreover, different application domains,

for example, information systems and pervasive

computing applications, require modeling languages

that take into account the characteristics of a domain

by providing a notation that can be enhanced and

adapted by domain specific extensions. The ap-

proach used in software product line engineering,

namely the separation of commonality and variabili-

ty, would be a viable approach that solves the prob-

lems in separation of common and domain specific

semantics, and the integrated use of the defined ser-

vice ontologies.

4.2 Context Ontologies

(Dey and Newberger, 2009) define context: ‘Context

is any information that can be used to characterize

the situation of an entity.’ Understanding of context

information is heavily improved during the last five

years. Recently published articles, e.g. (Bettini et al.,

2010) (Meier et al., 2009) (Kapitsaki et al., 2009)

indicate that knowledge on specification, modeling

and usage of context information might be mature

enough for realizing context-aware smart space ap-

plications. Typically, context information has three

dimensions; physical, computational and user con-

text (Bettini et al., 2010). In order to assist for

achieving interoperability on the levels that concern

context data and change of context, the context shall

(Preuveneers and Berbers, 2008) i) have a complete

domain coverage and terminology; ii) be expressive

and without semantic ambiguity; iii) be processed

without complexity; and iv) be evolvable.

Three types of context modeling and reasoning

approaches (Bettini et al., 2010) have been identi-

fied: an object-role based model, a spatial model and

an ontology based model. The object-role based ap-

proach supports various stages of the software engi-

neering process. Its weakness is a ‘flat’ information

model, i.e. all context types are represented as atom-

ic facts. The spatial context model is well suited for

context-aware applications that are mainly location-

based, like many mobile applications. The main con-

sideration of the spatial context model is the choice

of the underlying location model. Relational location

models are easier to build up than geographic loca-

tion models. SOUPA (Chen et al., 2005), as the only

standardized context ontology, provides the most

promising starting point for enhanced context ontol-

ogy of smart cities. Therefore, an initial version of a

context ontology introduced in (Pantsar-Syväniemi

et al., 2010) is an enhancement of SOUPA. This

context ontology defines three levels; i) the physical

context deals with raw context data gathered from

the environment by sensors; ii) the digital context

exploits physical context information and merges it

with the system’s internal context information re-

lated to applications and information; and finally iii)

the situation context clusters and abstracts the digital

context information in a way that it matches to the

application in hand and the preferences of its user.

Thereafter, the situation context is used for adapting

the application according to the view of the whole

context information that relates to the application.

4.3 QoS and Dependability Ontologies

Quality of Service (QoS) has a traditional meaning

as a property of communication technologies, in-

cluding throughput, latency, jitter, error rate, availa-

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119

bility, and network security. In service oriented ar-

chitectures, QoS is defined as dependability, main-

tainability, usability and scalability (O'Brien et al.,

2007). For end-users, QoS is the degree to which an

executed service meets its quality requirements.

Quality characteristics are often referred as non-

functional requirements, although many of them

(e.g. performance and dependability) are intertwined

with functionality of software. Typically, existing

quality ontologies have a specific focus. For exam-

ple, a quality ontology may deal with one or few

quality attribute(s) in defining, managing, or match-

ing quality properties. However, to guarantee QoS

requires comprehensive support for defining and

managing all the relevant quality attributes of ser-

vices, at design time and at run time.

There are several studies on QoS ontologies re-

lated to quality of Web Services. In (Anderson et al.,

2007), an overview of resilience knowledge base

(RKB) is described, in which dependability and se-

curity ontology is derived from the taxonomies of

(Avizienis et al., 2004) and developed specifically

for the RKB. The ontology is represented in OWL

and incorporates 166 terms related to Dependability

and Security, and 23 to Systems. Moreover, there are

QoS attribute ontologies and QoS-aware discovery

solutions based on service level agreements (Me-

nasce, 2002). Some papers also discuss performance,

dependability and service cost as well as mecha-

nisms of their aggregation (Yang et al., 2006) (Lock

and Dobson, 2009). Other dependability-related

metadata included into the description is i) the de-

velopment metadata, i.e. information about service

developers and implementation technology, and ii)

the deployment metadata, i.e. information related to

the hosting organization, location, deployment envi-

ronment, network connection capacity, etc.. Adding

this meta-information will allow clients to decide

how to use services by decreasing common mode

failures.

Dynamic operational state parameters, such as

current service load, CPU and memory usage, net-

work loading, etc. might also be added to the ex-

tended description. Extending a service description

with dependability metadata will bring us closer to a

dependable semantic service platform.

5 MODELING APPROACHES

5.1 Service Modeling

Service modeling can apply ontology based service

engineering, software engineering or/and domain

engineering modeling techniques. Knowledge engi-

neering applies ontologies for capturing and struc-

turing topic knowledge shared across people, organ-

izations, computers and software. Several methods

for ontology development exist, e.g. METHON-

TOLOGY (Fernandez-Lopez et al., 1997), and a set

of languages, such as XML, RDF, and OWL that

can be applied to represent knowledge in a machine

readable format. Moreover, OWL-S as a specific

service description language can be used for describ-

ing service semantics.

Semantic Web, Ontology Engineering, Semantic

Annotations, Semantic Search, Intelligent Services

(Modeling, Discovery and Integration) are standards

from W3C (www.w3c.org) and FIPA

(www.fipa.org) for describing semantics models.

The use of standards and open source tools (as W3C

standards and OWL in Protégé 2000 environment)

helps in sharing and using ontologies. Therefore,

open standards and open source tools are the key

enablers of semantics modeling. The advancement in

open source tools has greatly improved the ability to

test and build ontologies from scratch or/and to

reuse existing ontologies.

Application programming interfaces for ontolo-

gy languages provide programming language depen-

dent means to load ontologies, manipulate the ontol-

ogy classes and relations, perform reasoning, and

provide persistent storage for the model. Jena and

OWLS API are the most popular Java frameworks

for building semantic Web applications. These tools

provide an application developer with a program-

ming language for working with ontologies. Reason-

ing tools, such as FaCT++, Pellet, and RacerPro,

provide a standardized XML interface to description

logics systems. These tools help in ontology testing

and in the development of application level intelli-

gence based on ontologies described in OWL. Do-

main ontology specific editors such as OWLS Editor

and WSMO design studio help in creating error free

semantic descriptions based on a specific ontology.

Domain specific modeling addresses the specif-

ics of an application domain in the meta-models

from which a domain specific language is derived

(Kelly and Tolvanen, 2008). Although UML2 is a

generic modeling language, it also provides con-

structs to extend the language with domain specific

concepts. Thus, UML2 enhanced with domain spe-

cific ontologies that extends the language with ser-

vice, context and dependability ontologies makes it

applicable to the development of autonomic service

platforms.

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5.2 Context Modeling

Context-aware service engineering can be classified

into two classes (Kapitsaki et al., 2009); language

based approaches and model-driven approaches.

Language based approaches such as context-oriented

programming and aspect-oriented programming fol-

low the separation of concerns; applications are kept

context-free and context is handled as a first-class

entity of the programming language while separate

constructs are used to inject context-related behavior

into the adaptable skeleton of an application. Con-

text-aware aspects programming is one step further;

the aspects are driven by context, i.e. a particular

aspect may or may not be executed depending on the

context of use.

When trying to solve the complexity of context-

aware applications, the approaches for context mod-

eling and reasoning, namely object, spatial and on-

tology based have the following strengths and weak-

nesses;

• The object-role based approach supports various

stages of the software engineering process but

has an information model not suitable for model-

ing context information of smart cities.

• The spatial context modeling suits well for loca-

tion based applications, like mobile applications.

The drawback is the effort the special context

model takes to gather and keep up to date the lo-

cation data of the context information. Thus, this

model is suitable for those smart city applications

that do not have critical performance and depen-

dability requirements as in emergency situations.

• Ontological context models provide clear advan-

tage both in terms of support for heterogeneity

and interoperability. User-friendly graphical tools

make the design of ontological context models

viable to developers that are not particularly fa-

miliar with description logics. However, there is

very little support for modeling temporal aspects

in ontologies. However, the main problem might

be that reasoning with OWL poses serious per-

formance issues.

Programming based on the spatial context models

(Meier et al., 2009) uses a small set of predefined

types for composing context information. Thus, it is

a topographical approach for modeling a space, i.e.

the context of actors is modeled as a geometric

shape based on a sequence of coordinates. This

enables actors to independently define and use po-

tentially overlapping spatial context in a consistent

manner, when relationships between spatial objects

are defined implicitly, i.e. as the positions of the

spatial objects shapes within the coordinate system.

Thus, the spatial programming model enables effi-

cient integration of heterogeneous systems into a

global smart space. Although the programming

model might be a too sophisticated and overesti-

mated approach for developing smart cities, it is a

feasible enabler for self-organized sensor networks.

As mentioned, all approaches have weaknesses

that make their use such as unfeasible in autonomic

dependable service platforms. However, the generic

model for context monitoring and situation based

adaptation of application logic (Dey and Newberger,

2009) is part of a viable solution, as described in

(Pantsar-Syväniemi et al., 2010).

5.3 Dependability Modeling

Dependability is to be considered from three view-

points; as a system property; as a service capability,

and a failure free operation. Dependability of a com-

puting system is its ability to deliver a service that

can justifiably be trusted. Dependability of a service

is its behavior as it is perceived by the service us-

er(s). Based on the definition of failure, an alternate

definition of dependability exists, which comple-

ments the other definitions in providing a criterion

for adjudicating whether the delivered service can be

trusted or not: the ability of a system to avoid fail-

ures that are more frequent or more severe, and out-

age durations that are longer than is acceptable to the

user(s). The first two definitions relate to the system

and software design and implementation. The third

one relates to the space’s ability to survive under

failures. Thus, it relates to self-healing and self-

protecting, the characteristics of autonomic systems.

We understand dependability as a general con-

cept that manages four quality attributes; reliability,

availability, security and safety. Safety is not com-

mon in smart cities but extremely important in safe-

ty-critical systems, e.g. in trains and airplanes. Thus,

when using sensor information for making context-

aware smart city applications, we focus on reliabili-

ty, availability and security. Especially, our interest

is on how to deal with these quality properties in a

situation based manner and how to assure that quali-

ty requirements are met when ad-hoc situation based

adaptations are made.

Survivability concerns autonomic systems and is

a system’s capability to fulfill its mission, in a time-

ly manner, in the presence of attacks, failures or ac-

cidents. There are two aspects of survivability: pro-

tection and adaptation. Survival by protection refers

to run-time security management. Survival by adap-

tation is an ability of a system to adapt its behavior

to the changes that occur either in the system or ex-

ternally in the operating environment and users’ re-

EXPLORATION OF TECHNOLOGIES FOR AUTONOMIC DEPENDABLE SERVICE PLATFORMS

121

quirements (Tarvainen, 2007). Thus being self-

adaptive and self-protecting, the dependable service

platform should support survivability.

Security mechanisms like access control and en-

cryption attempt to ensure survivability by protect-

ing applications from harmful, accidental or mali-

cious changes in the environment. Applications can

also survive by adapting themselves to the changing

conditions. Survival by adaptation typically involves

monitoring and changing the quality goals so that

they can be achieved. In order to exploit architectur-

al design knowledge for runtime adaptation, the fol-

lowing activities should be supported; a) identifica-

tion of the internal and external contexts of the sys-

tem, b) reasoning the change of context, c) reasoning

the activities to be taken in order to achieve the qual-

ity goals defined, and finally, d) reconfiguring the

system in a manageable way. In proactive adapta-

tion, these activities have to be made before they

occur. Thus, appropriate learning techniques are

used for predicting the system’s behavior and mak-

ing it survivable by proactive actions.

Dependability modeling has four main modeling

phases. First, the semantics of dependability is de-

scribed at the design time by applying the quality-

driven architecture design and quality analysis me-

thodology (Ovaska et al., 2010). As a result, the sub-

attributes of dependability are described as separate

ontologies utilized for defining quality requirements.

Second, quality requirements are mapped to the

elements of software architecture models (Niemelä

et al., 2008). Third, the designed architecture is eva-

luated in order to detect whether required quality is

met or not. Fourth, quality of the implemented soft-

ware is measured and compared to requirements. In

practice, the above described approach with support-

ing techniques and tools can be exploited but the

approach needs enhanced middleware services that

able to use the design knowledge, represented in the

service, context and dependability ontologies, in

monitoring, reasoning and adapting dependability of

smart city applications.

6 DISCUSSION

As presented there are several technologies and solu-

tions for the development of autonomic dependable

systems composed of heterogeneous subsystems

such as sensors, networks, and storage systems.

While these subsystems can boost dependability,

research is required towards a holistic approach that

will consider large complex networked systems as a

whole. Therefore, the focus should be on an ap-

proach that hides the heterogeneity of sensor tech-

nologies, provides proactive strategies for QoS adap-

tation and easiness to use and understand the sensor

web and its applications. Moreover, the key enabling

technologies required for the future pervasive com-

puting environments to be explored extensively in

the ongoing and future research projects are:

• Semantics modeling (cf. section 4.1). A novel

semantics modeling technique is required. It shall

consist of i) stakeholder-centric views, ii) support

for the generic ontologies such as quality

attribute ontology of dependability and perfor-

mance, iii) a core ontology of the technology

domain (e.g. sensor webs), iv) application specif-

ic domain ontologies, and v) integrated orchestra-

tion of the developed ontologies in service engi-

neering.

• Dependability metrics and measuring techniques

(cf. section 4.3). Uniform quality metrics for ex-

ecution qualities are required. Moreover, com-

mon measuring techniques are needed for depen-

dability (i.e. reliability, availability and security)

and performance. A reuse-oriented approach is to

be established for exploiting the dependability

ontology in modeling semantics of autonomic

dependable service platforms and applications.

• Proactive adaptation (cf. section 3.3, 4.2, 5.2 and

5.3). A novel approach is required for measuring,

monitoring, adapting and predicting of system’s

behavior from quality point of view. QoS-driven

proactive adaptation requires innovative solutions

for 1) managing semantic descriptions at run-

time; 2) deriving quality indicators from basic

QoS measurements; and 3) enhanced adaptation

and learning algorithms. Furthermore, the proac-

tive QoS adaptation mechanism has to take into

account the constraints of the used technology;

how to deal with context awareness and resource

constraints of computing and communication en-

vironment.

• Dynamic semantic middleware (cf. section 3.1,

3.2, 5.1 and 5.2). There is a need for a dynamic

semantic middleware that allows proactive ser-

vice discovery, service composition and negotia-

tion, and evolution management of cross-domain

service platforms intended for heterogeneous

networked systems, devices, actuators and ap-

pliances used for environmental monitoring. The

middleware shall deal with interoperability of

sensors, devices and services in heterogeneous

multi-vendor environments.

7 CONCLUSIONS

In this paper we explored the existing technologies

ICSOFT 2011 - 6th International Conference on Software and Data Technologies

122

applicable for use in the development of autonomic

dependable service platforms that embody the tech-

nical challenges of pervasive computing environ-

ments, the business challenges of the multi-vendor

product development and the quality of service chal-

lenge of trusted services that insist on a dependable

and high efficient service platform.

We scoped our work by smart cities, the context

where intelligence of services is benefited the most

and where end-users should be supported with novel

software and service engineering technologies.

Moreover, we adopted an approach that exploits and

enhances legacy systems because making running

systems more intelligent and self-adaptive is a big

enough challenge.

As a conclusion, we identified four research top-

ics that need extensive research and developments,

namely i) semantics modeling, ii) dependability me-

trics and measuring techniques, iii) proactive adapta-

tion architectures, and iv) middleware support for

handling dynamism of self-organizing (ad-hoc) sen-

sor networks. Our future work will address these

topics.

ACKNOWLEDGEMENTS

This work is supported by the SOFIA/Artemis

project, co-funded by EU, Tekes, and VTT and the

Smash project, funded by VTT. The work of Liliana

Dobrica was supported by Romanian Scientific

Council CNCSIS –UEFISCSU, project number PNII

– IDEI 1238/2008.

REFERENCES

Anderson T., Andrews Z. H., Fitzgerald J.S., Randell B.,

Glaser H., Millard I.C., 2007. The ReSIST Resilience

Knowledge Base. 37th Annual IEEE/IFIP Intl. Conf.

on. Dependable Systems and Networks (DSN’2007).

Avizienis A., Laprie J-C., Randell B., Landwehr C., 2004.

Basic Concepts and Taxonomy of Dependable and Se-

cure Computing. IEEE Trans. on Dependable and Se-

cure Computing, vol. 1, no. 1, pp. 11-33.

Balazinka M., Deshpande A., Flanklin M. J., Gibbons

P.B., Gray J., Nath S., Hansen M., Liebhold M., Sza-

lay A. and Tao V., 2007. Data management in the

worldwide sensor web. Pervasive Computing, June-

April, 30-40.

Baresi L., Guinea S. and Pasquale L., 2008. Towards a

unified framework for the monitoring and recovery of

BPEL processes. In: Testing, Analysis and Verification

of Web Software, TAV-WEB, pp. 15-19.

Barstow A., Hendler J. and Skall M., 2004. OWL Web

Ontology Language for Services, W3C. http://xml.

coverpages. org/ni2004-01-08-a.html

Bettini C., Brdiczka O., Henricksen K., Indulska J., Niclas

D., Ranganathan A., and D. Riboni. 2010. A survey of

context modelling and reasoning techniques. Pervasive

and Mobile Computing.

Botts M., Percivall G., Reed C. and J. Davidson, 2008.

OSG

Sensor Web Enablement: Overview and High

Level Architecture. LNCS 4540, Springer-Verlag, pp.

175–190.

Cardellini V., Casalicchio E., Grassi V., Presti F. L. and.

Mirandola R, 2009a. A scalable approach to QoS-

aware self-adaption in service-oriented architectures.

In: QSHINE, pp. 431-447.

Cardellini V., Casalicchio E., Grassi V., Presti F. Lo and

Mirandola R.,2009b. QoS-driven runtime adaptation

of service oriented architectures. In: The 7th Joint Eu-

ropean Soft. Eng. Conf. and ACM SIGSOFT Symp. on

the Foundations of Soft. Eng., pp. 131-140.

Cardellini V., Casalicchio E., Grassi V., Presti F. Lo and

R. Mirandola, 2009c. Towards self-adaptation for de-

pendable service-oriented systems. In: Architecting

Dependable Systems VI, pp. 24-48.

Chen H., Finin T. and Joshi A., 2005. The SOUPA Ontol-

ogy for Pervasive Computing. Whitestein Series in

Software Agent Technologies, Springer.

Cheng, S-W, Poladian, V., Garlan, D., Schmerl, B., 2009.

Improving Architecture-Based Self-Adaptation

through Resource Prediction, In: Self-Adaptive Sys-

tems, LNCS 5525, Springer-Verlag, pp.71-88.

Chu X. and Buyya R., 2007. Service oriented sensor web.

Sensor Networks and Configuration, Springer-Verlag,

51-74.

Dai, Y., Xiang, Y., Zhang, G., 2009. Self-healing and

Hybrid Diagnosis in Cloud Computing. In: M.G. Jaa-

tun, G.Zhao, and C.Rong (Eds.): CloudCom 2009,

LNCS 5931, Springer-Verlag, pp- 45-56.

Dey A. K. and Newberger A., 2009. Support for Context-

Aware Intelligibility and Control. In: CHI 2009, Pro-

gramming Tools and Architectures, USA.

Fayssal, S., Al-Nashif, Y., Uk Kim, B., Hariri, S., 2008. A

Proactive Wireless Self-Protection System. In:

ICPS’08, July 6-10, Sorrento Italy, ACM, pp.11-20.

Fernandez-Lopez M., Gomez-Perez A., Juristo N., 1997.

Methontology: from ontological art towards ontologi-

cal engineering. In: Procs. Spring Symposium on onto-

logical engineering of AAAI.

Fuad, M.M., 2010. Issues and Challenges of an Inductive

learning Algorithm for Self-healing Applications. The

7th Intl. Conf. on Information Technology: New

Generations (ITNG10), IEEE Press, pp. 264-269.

Hayes P., 2004. RDF Semantics, W3C, http://www.w3.org

/TR/rdf-schema/

Jayaraj A., Venkatesh T. and Murthy C.S.R., 2008. Loss

classification in optical burst switching networks using

machine learning techniques: improving the perfor-

mance of TCP. IEEE Journal on Selected Areas in

Communications, vol.26, no.6, pp.45-54.

Kantorovitch J. and Niemelä E., 2008. Service Descrip-

tion Ontologies. Encyclopedia of Information Science

and Technology, Vol. VII, pp. 3445-3451.

EXPLORATION OF TECHNOLOGIES FOR AUTONOMIC DEPENDABLE SERVICE PLATFORMS

123

Kapitsaki G., Prezerakos G., Tselikas N., and I. Venieris.

2009. Context-aware service engineering: A survey.

The Journal of Systems and Software, 83, 1885-1297.

Kelly S, and Tolvanen J., 2008. Domain-Specific Model-

ling: Enabling Full Code Generation. Wiley.

Kephart J. O. and Chess D. M., 2003. The vision of auto-

nomic computing. Computer 36(1), pp. 41-50.

Könönen V., 2004. Multiagent reinforcement learning in

Markov games: asymmetric and symmetric approach-

es, Doctoral thesis, Helsinki University of Technolo-

gy, Report D8, Espoo, Finland.

Lock R. and Dobson G., 2009. Developing an ontology for

QoS. Dependability interdisciplinary research Colla-

boration (Internal Annual Project Conference), Nesc.

(National e-Science centre).

McGuinness D. and van Harmelen F., 2004. OWL Web

Ontology Language Overview, W3C., http://www.

w3.org/TR/owl-features/

Meier R., Harrington A., Beckmann K., and Cahill, V.,

2009. A framework for incremental construction of

real global smart space applications. Pervasive and

Mobile Computing, 5, 350-368.

Menasce D. A. and Dubey V., 2007. Utility-based QoS

brokering in service oriented architectures. In: IEEE

Intl Conf on Web Services, ICWS 2007, pp. 422-430.

Menasce D. A., 2002. QoS Issue in Web Services. IEEE

Internet Computing, vol. 6, issue 6, pp. 49-68.

Mokhtar S. B., Georgantas N. and Issarny V., 2007. CO-

COA: COnversation-based service COmposition in

pervAsive computing environments with QoS support,

Journal of Systems and Software 80(12), pp. 1941-

1955.

Motta E., Domingue J., Cabral L.and Gaspari M., 2003.

IRS-II: A Framework and Infrastructure for Semantic

Web Services. ISWC 2003, in Sanibel Island, USA.

Nahrstedt K., Xu D., Wichadakul D. and Li B., 2001.

QoS-aware middleware for ubiquitous and heteroge-

neous environments. IEEE Communications Maga-

zine, vol. 39, no. 11, pp. 140-148.

Niemelä E., Evesti A. and Savolainen P., 2008. Modeling

Quality Attribute Variability. 3

International Confe-

rence on Evaluation of Novel Approaches of Software

Engineering, ENASE 2008.

O'Brien L., Merson P. and Bass L., 2007. Quality

Attributes for Service-Oriented Architectures. Interna-

tional Workshop on Systems Development in SOA En-

vironments (SDSOA'07).

Ovaska E., Evesti A., Henttonen K., Palviainen M. and

Aho P., 2010. Knowledge Based Quality Driven Ar-

chitecture Design and Evaluation. Information and

Software Technologies, 52 (6), 577-601.

Pantsar-Syväniemi S., Simula K., Ovaska E., 2010. Con-

text-awareness in smart spaces. 1

International

Workshop on Semantic Information for Smart Spaces,

SISS 2010, IEEE, pp. 1023-1028.

Papakos P., Rosenblum D. S., Mukhija A. and Capra L.,

2009. VOLARE: Adaptive web service discovery

middleware for mobile systems. 2nd Intl DisCoTec on

Context-Aware Adaptation Mechanisms for Pervasive

and Ubiquitous Services (CAMPUS 2009).

Preuveneers D., and Berbers Y., 2008. Internet of Things:

A Context-Awareness Perspective, The Internet of

Things: From RFID to the Next Generation Pervasive

Networked Systems, CRC Press, 287-307.

Rashidi P. and Cook D. J., 2009. Keeping the resident in

the loop: adapting the smart home to the user. IEEE

Transactions on Systems, Man and Cybernetics, Part

A (Systems and Humans), vol. 39, no.5, pp. 949-959.

Robinson D., Kotonya G. and Department C., 2008. A

self-managing brokerage model for quality assurance

in service-oriented systems. In: 11th IEEE High As-

surance Systems Eng. Symp., HASE, pp. 424-433.

Rouvoy R., Barone P., Ding Y., Eliassen F., Hallsteinsen

S., Lorenzo J., Mamelli A. and Scholz U., 2009. MU-

SIC: Middleware support for self-adaptation in ubi-

quitous and service-oriented environments. In: Softw.

Eng. for Self-Adaptive Systems, pp. 164-182.

Salehie M. and Tahvildari L., 2009. Self-Adaptive Soft-

ware: Landscape and Research Challenges. ACM

Trans. on Autonomous and Adaptive Systems, 4(2).

Tarvainen P., 2007. Adaptability evaluation of software

architectures; a case study. 31

Annual IEEE Int.

Computer Software and Applications Conf. (COMP-

SAC 2007). IEEE Computer Science.

WSMO, 2004. WSMO studio, http://www. wsmostu-

dio.org/

Yang S., Lan B. and Chung J-Y., 2006. Analysis of QoS

Aware Web Services. Intl. Comp. Symp. on Web

Technologies and Information Security (ICS) .

Zeng L., Benatallah B., Ngu A.H., Dumas M., Kalagna-

nam J., Chang H., 2004. QoS-aware middleware for

web services composition, IEEE Trans. Software Eng.

30(5), pp. 311-327.

ICSOFT 2011 - 6th International Conference on Software and Data Technologies

124