El paisaje cultural a la escala del barrio

Figure 18 illustrates the relationship between the problem statements P1-

P6, and the contributions of the solution framework as well as the relationship

between the solution framework and the publications in Part II. The sub-

contributions that are orange boxes have been applied in the work reported in all

papers.

The solution framework 

Considering P1, the capability-based computing architecture (Figure 9) is

applied. The RM-based functionality is intended to meet the general properties.

The capability concept is needed to meet the core properties A1, A2 and A3.

The capability, service performance and SLA concepts, however, are the con-

cepts needed to meet the core properties A2 and A3. The use of the capability,

capability performance, service performance and RM-based specification con-

cepts can be found in all papers. The concept SLA, however, is used in Paper E,

Paper F, Paper G and Paper H for the provisioning of QoS in adaptable service

systems.

Concerning P2, The capability configuration management is applied in the

service architecture as illustrated in Figure 4 for capability management func-

tionality modeling. The capability initialization is related to the core property

A1. The capability re-initialization and allocation adaptation are related to the

core properties A2 and A3. The capability initialization is demonstrated in Pa-

per B and Paper C. The capability re-initialization is demonstrated in Paper B

and Paper D. The capability allocation adaptation is dealt with in Paper E, Paper

F, Paper G and Paper H. The formalized models of capability initialization, re-

initialization and capability allocation adaptation models are in Paper G.

Concerning P3, autonomic capability management requires the capability

configuration management to generate capability initialization, re-initialization

and allocation adaptation configurations dynamically. Based on the generic rea-

soning model, the capability configuration management mechanism is activated

and de-activated autonomously. The application service systems are monitored.

Changes in the application service systems as well as the environment will trig-

ger a feedback loop constituting the capability allocation adaptation mechanism

to manage the behavior of the service systems. Important settings for the capa-

bility allocation adaptation are policies and parameters. The policies of the ca-

pability allocation adaptation mechanism can be either static policies or dynam-

ic policies. The parameters of the capability allocation adaptation can also be

produced by the parameter production. The capability initialization can be

found in Paper B and C. The capability re-initialization can be found in Paper B

and D. The capability allocation adaptation and the dynamic policies can be

found in Paper E, Paper F and Paper G. The parameter production for the capa-

bility allocation adaptation can be found in Paper H. The software implementa-

tion of the generic reasoning model can be found in Report I.

Concerning P4, the ontology to be used has been defined as the concept

ontology: capability, capability performance, service performance, SLA, EFSM

and RM in the capability-based computing architecture. The capability, capabil-

ity performance, service performance and SLA ontology are needed to be stored

and made available. These ontology instances are included in the EFSM-related

and RM-related data entities. Standard representation models – Common Infor-

mation Model (CIM) and Resource Definition Framework (RDF) are applied to

model the concepts: capability, capability performance, service performance

and SLA as presented in Paper A and Paper B. The RM-based data representa-

tion for modeling the RM-related data entities have been formalized in Paper E,

Paper F, Paper G and Paper H.

Concerning P5, The policy-based reasoning is required to model capability

configuration management functionality with respects to modularity, reusabili-

ty, expressive power and flexibility. The policy-based specification is flexible,

expressive and powerful. RM-based role figures have ability to download and

execute policies that can be stored, managed and easily distributed. The generic

policy system definition allows policies to be composed in a modular and reus-

able away. The ontology-based capability selection mechanism gives expressive

reasoning power to the capability configuration management to reason based on

a defined capability ontology model. The RM-based role figures have been for-

malized and used in Paper E, Paper F, Paper G and Paper H. The generic policy

system definition has been demonstrated in Paper C. The ontology-based capa-

bility selection mechanism has been demonstrated in Paper A.

The dynamic policies and parameter productions are techniques that can be

used to evaluate the usability and goodness of the policies and parameters of a

capability configuration management system. The goodness of the used policies

and parameters are observed based on feedbacks, income functions and cost

functions. The dynamic policies have been formalized, demonstrated, evaluated

and used together with the capability allocation adaptation in Paper E, Paper F

and Paper G. The parameter production has been formalized, demonstrated,

evaluated and used with the capability allocation adaptation in Paper H.

Concerning P6, the solution framework is evaluated and validated by dem-

onstration and simulation. The five scenarios defined by the contribution C5

are: S1: Ontology-based capability selection for a Tele-school application, S2:

Printing system capability initialization and re-initialization, S3: Capability re-

The solution framework 

initialization of RM-based role figurers, S4: Policy-based capability allocation

adaptation for streaming service, and S5: Policy-based parameter production for

policy-based capability allocation adaptation