Recently, there has been a considerable interest in policy-based, goal-oriented service management and autonomic computing. Much work is still required to investigate designs and policy models and associate meta-reasoning systems for policy-based autonomic systems. In this paper we outline a proposed autonomic middleware control service used to orchestrate selfhealing of distributed applications. Policies are used to adjust the systems autonomy and define self-healing strategies to stabilize/correct a given system in the event of failures

Badr, N.

Reilly, D.

Taleb-Bendiab, A.

English

Enlighten

      Badr, N. and Reilly, D. and Taleb-Bendiab, A. (2004) Policy-based autonomic control service. In, Proceedings of the Fifth IEEE International Workshop on Policies for Distributed Systems and Networks (POLICY’04), 7-9 June 2004, pages pp. 99-102, Yorktown Heights, New York.        http://eprints.gla.ac.uk/3388/     Glasgow ePrints Service http://eprints.gla.ac.uk 1Policy-Based Autonomic Control ServiceN. BadrSchool of Computing and Mathematical Science,Liverpool John Moores University,Byrom Street, LiverpoolL3 3AF, UKcmsnbadr@livjm.ac.ukA. Taleb-BendiabSchool of Computing and Mathematical Science,Liverpool John Moores University,Byrom Street, LiverpoolL3 3AF, UKa.talebbendiab@livjm.ac.ukD. ReillySchool of Computing and Mathematical Science,Liverpool John Moores University,Byrom Street, LiverpoolL3 3AF, UKd.reilly@livjm.ac.ukAbstractRecently, there has been a considerable interest in policy-based, goal-oriented service management andautonomic computing. Much work is still required to investigate designs and policy models and associatemeta-reasoning systems for policy-based autonomicsystems. In this paper we outline a proposed autonomic middleware control service used to orchestrate self-healing of distributed applications. Policies are used to adjust the systems autonomy and define self-healingstrategies to stabilize/correct a given system in the event of failures. 1. IntroductionRecently, there has been a considerable interest in both policy-based management, goal-based servicemanagement and autonomic distributed systemsmanagement [1-9]. Policies are often encoded as rules to specify for instance systems security, managementproperties, allowable states and/or obligations. Based on an ongoing study into requirements for self-adaptivesoftware engineering and management, this paper outlines the interplay of self-managing and self-healing policies and deliberative reasoning models to orchestrate, enable and/or adjust application autonomy. The paper describes a prototype policy-based autonomic system, which is deployed as a middleware service. The system is based on: service manager, system controller and JavaSpaces,and self-healing policy repository. The remainder of the paper is structured as follows: section 2 provides background material relating to our approach and in particular describes current trends in autonomic-based management and policy-basedmanagement approaches. Section 3, provides an overview of the policy-based autonomic control service. Section 4, describes the development of the control service based on Jini middleware technology. Section 5, presents a case study and section 6, draws overall conclusions andmentions future work.2. BackgroundRecent research has focused on the development of methods and techniques to support runtime management and self-governance of distributed applications, which is reviewed briefly below.2.1 Policy-Based ManagementMuch research has addressed the use of policies for dynamic management of large distributed systems [1-3].Moffett et al. [3] stated the necessity of representing and manipulating policy management of distributed systems. In policy management, action policies are represented as a policy hierarchy, where each policy in the hierarchyrepresents a plan that meets a specific objective. Slomanand Lupu [10] studied authorization and obligationpolicies’ specification for programmable networks. Such policies are interpreted to facilitate runtime activation, de-activation and/or their modification without having to shutdown the network node. Other approaches to policy-based management have used condition-action rules to support a static policy configuration-based solution, in which human intervention is required for systemreconfiguration and policy deployment. Moffett et al. [11]have proposed a framework for supporting automated policy deployment and flexible event triggers to permit dynamic policy configuration, focusing on solutions for dynamic adaptation of policies in response to changes within the managed environment. Other research efforts have focused on policy specification and enforcement for dynamic service management. For instance, the IETFProceedings of the Fifth IEEE International Workshop on Policies for Distributed Systems and Networks (POLICY’04) 0-7695-2141-X/04 $ 20.00 © 2004 IEEE 2Policy Working Group is developing a QoS networkmanagement framework using the X.500 directory service, where policies are encoded as If-Then rules and stored in directories [12].     Figure 1: Autonomic management process model.2.2 Autonomic-Based ManagementWithin the distributed system community work isunderway to understand and design autonomic computing systems endowed with self-management and abilities to adjust to unpredictable changes [13]. The two mainelements of autonomic management are the functionalunit and the management unit. The functional unitperforms the main operation and is provided by elements such as web services or databases. The management unit is responsible for system resources and operationalperformance and hence the reconfiguration of resources according to adaptive changes [5]. Autonomic systems have been defined by IBM [6] as system that have “… Theability to manage themselves and dynamically adapt to change in accordance with policies and objectives…”.In other words, they have the ability to monitor, diagnose and heal themselves. This entails that systems have the ability to dynamically insert and remove code at runtime. Hot swapping [8, 9] is proposed as a means to enable autonomic software systems to interpose and/or replacecomponents (code) in response of either failure orsoftware maintenance.3. Policy-Based Autonomic Control ServiceAutonomic systems must discharge the intended self-management functionality in a safe, controllable and predictable manner avoiding any emerging, accidentalerrors or undesirable features [9-12]. This thereforerequires that autonomic systems extend and integrateautonomic control models with policy-based service [9]. This formed the motivation for our work, which provides a self-governance mechanism based on a developedExtensible Believe, Desire and Intension (EBDI) model for deliberative systems. The EBDI model is used to guide and manage an autonomic application’s self-healingprocesses.Figure 1 illustrates the autonomic management and interaction model used to monitor an application. The overall process starts with the monitoring process to detect conflicts using the control service internal and external polices. This followed by the diagnosis process and then the repair strategies, which are based on the proposed EBDI model (Sec. 4). The strategies areembedded in an external format such as XML, which may be parsed and translated into executable format by the system interpreter. In addition the distributed shared space is used to facilitate the remote integration and coordination of the distributed control services.Figure 2:The control  service architectural  layers.4. Design of the Proposed Control ServiceAs shown in Figure 2, the design of the policy-basedautonomic control service combines three main services: • Service Manager: is used to adapt the structural components and the dynamic behaviour of the services they provide. Structural components can evaluate their behaviour and environment against their specified goals with capabilities to revise their structure and behaviour accordingly [20]. This separation into distinct service managers eases their management by decentralizing thecontrol of each individual application-levelservice. Service managers look after theirapplication-level service and monitor itsbehaviour using an external and internal policies.• JavaSpace Service: is a persistent distributed shared memory used by the self-healing process for awareness and coordination. It stores therequired information and reports service states for use by the system controller service to repair and reconfigure the system in the event of any Proceedings of the Fifth IEEE International Workshop on Policies for Distributed Systems and Networks (POLICY’04) 0-7695-2141-X/04 $ 20.00 © 2004 IEEE 3service failures. The JavaSpace service usesexternal policies to notify the system controller directly with remote events. The remote events may either notify of changes in application-levelservice states (as reported by the servicemanager) or notify the system controller if the lease of a service manager should expire. • System Controller: is responsible for thedynamic reconfiguration of a given application by coordinating the activities of individualservice managers. Each manager is used as a meta-service to monitor, repair and adapt itsassociated application-level services in the event of conflicts/failures. Service managers may then report their associated application-level service states using the JavaSpace service. Associated external policies can either trigger a notification event to the system controller or request asystem controller service’s activation to monitor a given application service states. For example, the action shown below may be used if theaverage latency of the control process exceeds the maximum allowed latency. If such a caseoccurs the control rule detects and triggers an execution failure notification leading to a self-healing process.The development of system repair strategies and self-healing control is based on our proposed ExtensibleBeliefs, Desires and Intension (EBDI) model [11-13]. The model is concerned with situated intentional software that continuously monitors and/or observes its environment and acts to changes in accordance with its sets of policies.This architecture is based on a control service model that follows a cycle of monitoring the target application, detecting undesirable behaviours (events), identifyingconflicts/errors, prescribing remedial action plans andenacting change plans through reconfiguration1.1 A full description of the design and implementation of theautonomic middleware control service is out of the scope of this paper and can be found in a longer version of this paper [12].Figure 3: The Architectural view of the EmergeITS  appl icat ion. 5. Case StudyThe policy-based autonomic control service has been developed and demonstrated through an industrial case-study, namely EmergeITS [12]. The latter was developedusing Jini middleware as a proof-of-concept for self-healing software for intelligent networked appliances(vehicles). The case-study was developed in collaboration with the Merseyside Emergency Fire Service (Fig. 4). One of the EmergeITS services evaluated was the 3-in-1 phone, which allows a mobile phone or PDA device to be used in one of three different modes: a cellular phone, a WAP phone or a walkie-talkie. The 3-in-1 phone may be used for either voice communication or to receive multimediacontent, subject to the requirements of the user and availability of a communication service provider. Anautonomic middleware control service prototype wasincorporated into the case study to provide the meta-control software to support EmergeITS over the network.The 3-in-1phone service is hosted by an in-vehiclecomputer, which also acts as a gateway. If the localservice deployment should fail, then a service manager is started to provide self-monitoring, self-diagnosis, self-repair and self-adaptation. In the event of failure, the service manager will select an appropriate repair strategy, thereby providing a degree of conflict resolution and fault tolerance. The service manager stores the 3-in-1 phone service state in a JavaSpace service and the systemcontroller may check these states against external policies. If (control_avLatency is larger than                     control_maxLatency)   start        conflict_monitor(true);        start_control_process();     end; Proceedings of the Fifth IEEE International Workshop on Policies for Distributed Systems and Networks (POLICY’04) 0-7695-2141-X/04 $ 20.00 © 2004 IEEE 4Figure 6:The XML descript ion of a self−repair strategy.7. Conclusions and Future Work     In this paper we have described the development of a policy-based autonomic control service, which has been implemented using Jini middleware technology. Thecontrol service incorporates the policies (either internal or external) within the architecture of the autonomic control service to achieve policy-based autonomic control ofdistributed applications at run time.   We have briefly described the proposed Extensible BDI (EBDI), which is a policy (normative)-based model that is used to examine the system external policies and forgenerating the appropriate repair strategy at runtime.   We have described the main services of our policy-based autonomic control service (service manager,JavaSpace service and system controller service) and demonstrated their use to control a 3-in-1-phoneapplication service. In addition we have evaluated the performance of the autonomic control service using the elapsed time and the average latency as metrics of the system at runtime.   In our future work, we intend to improve/enhance the autonomic control service on two main fronts: self-protection and machine learning. There is a need to provide self-protection to provide security inuntrustworthy environments. There is also a need to equip the current model with the machine learning capabilities so that the policy-based autonomic control service canaccess history and knowledge relating to the previous failure cases.8. References1. E. Lupu, et al. A Policy Based Role Framework for Access Control. in First ACM/NIST Workshop on Role-Based Access Control. 1995. Maryland USA: ACM.2. J. Moffett and M.Sloman, Policy Hierarchies forDistributed Systems Management. IEEE Journal on Selected Areas in Communications, 1993. 11: p. 1404-1414.3. K. Yoshihara, M.Isomura, and H.Horiuchi. DistributedPolicy-based Management Enabling PolicyAdaptation on Monitoring using Active Network Technology. in 12th IFIP/IEEE InternationalWorkshop on Distributed Systems: Operations and Management. 2001. Nancy France.4. D. Chess, C.Palmer, and S.White, Security anautonomic computing environment. IBM SYSTEMS JOURNAL, 2003. 42.5. J. Appavoo, et al., Enabling autonomic behavior in systems software with hot  swapping,. IBM SYSTEMS JOURNAL, 2003. 42.6. M. Sloman and E. Lupu. Policy Specification for Programmable Networks. in First InternationalWorking Conference on Active Networks  (IWAN'99).1999. Berlin.7. K. Barber, et al. Conflict Representation andClassification in a Domain Independent Conflict Management Framework . in the Third International Conference on Autonomous Agents. 1999. SeattleWA.8. P. Horn, Autonomic Computing: IBM's Perspective on the State of Information Technology. IBMCorporation, 2001.9. D. Reilly, et al. An Instrumentation and Control-Based Approach for Distributed ApplicationManagement and Adaptation. in Workshop on Self-Healing Systems (WOSS'02). 2002. Charleston SCUSA.10. N. Badr, D. Reilly, and A. Taleb-Bendiab. A Conflict Resolution Control Architecture for Self-AdaptiveSoftware, in the International Workshop onArchitecting Dependable Systems: WADS 2002(ICSE 2002). 2002. Florida USA.11. N. Badr, An Investigation into AutonomicMiddleware Control Service to Support Distributed Self-Adaptive Software, PhD thesis, School ofComputing and Mathematical Sciences, LiverpoolJMU, 2003, www.cms.livjm.ac.uk/cmsnbadr.12. N. Badr, D. Reilly and A. Taleb-Bendiab, "Policy-Based Autonomic Control Service", TechnicalReport, School of Computing and MathematicalSciences, Liverpool JMU, 2004,www.cms.livjm.ac.uk/cmsnbadr.13. M. Bratman, Intentions, Plans, and Practical Reason.Harvard University Press, 1987.Proceedings of the Fifth IEEE International Workshop on Policies for Distributed Systems and Networks (POLICY’04) 0-7695-2141-X/04 $ 20.00 © 2004 IEEE 

A Conflict Resolution Control Architecture for Self-Adaptive Software,

A Policy Based Role Framework for Access Control.

An Instrumentation and ControlBased Approach for Distributed Application Management and Adaptation.

An Investigation into Autonomic Middleware Control Service to Support Distributed Self-Adaptive Software,

Autonomic Computing: IBM's Perspective on the State of Information Technology.

C.Palmer, and S.White, Security an autonomic computing environment.

Conflict Representation and Classification in a Domain Independent Conflict Management Framework.

Distributed Policy-based Management Enabling Policy Adaptation on Monitoring using Active Network Technology.

Enabling autonomic behavior in systems software with hot swapping,.

Hierarchies for Distributed Systems Management.

Policy Specification for Programmable Networks.

PolicyBased Autonomic Control Service&quot;,

Policy-based autonomic control service

Enlighten: Publications

 
 
 
 
 
 
Badr, N. and Reilly, D. and Taleb-Bendiab, A. (2004) Policy-based 
autonomic control service. In, Proceedings of the Fifth IEEE 
International Workshop on Policies for Distributed Systems and 
Networks (POLICY’04), 7-9 June 2004, pages pp. 99-102, Yorktown 
Heights, New York.
 
 
 
 
 
 
 
 
http://eprints.gla.ac.uk/3388/ 
 
 
 
 
Glasgow ePrints Service 
http://eprints.gla.ac.uk 
1Policy-Based Autonomic Control Service
N. Badr
School of Computing and 
Mathematical Science,
Liverpool John Moores 
University,
Byrom Street, Liverpool
L3 3AF, UK
cmsnbadr@livjm.ac.uk
A. Taleb-Bendiab
School of Computing and 
Mathematical Science,
Liverpool John Moores 
University,
Byrom Street, Liverpool
L3 3AF, UK
a.talebbendiab@livjm.ac.uk
D. Reilly
School of Computing and 
Mathematical Science,
Liverpool John Moores 
University,
Byrom Street, Liverpool
L3 3AF, UK
d.reilly@livjm.ac.uk
Abstract
Recently, there has been a considerable interest in 
policy-based, goal-oriented service management and
autonomic computing. Much work is still required to 
investigate designs and policy models and associate
meta-reasoning systems for policy-based autonomic
systems. In this paper we outline a proposed autonomic 
middleware control service used to orchestrate self-
healing of distributed applications. Policies are used to 
adjust the systems autonomy and define self-healing
strategies to stabilize/correct a given system in the event 
of failures. 
1. Introduction
Recently, there has been a considerable interest in 
both policy-based management, goal-based service
management and autonomic distributed systems
management [1-9]. Policies are often encoded as rules to 
specify for instance systems security, management
properties, allowable states and/or obligations. Based on 
an ongoing study into requirements for self-adaptive
software engineering and management, this paper outlines 
the interplay of self-managing and self-healing policies 
and deliberative reasoning models to orchestrate, enable 
and/or adjust application autonomy. The paper describes 
a prototype policy-based autonomic system, which is 
deployed as a middleware service. The system is based 
on: service manager, system controller and JavaSpaces,
and self-healing policy repository. 
The remainder of the paper is structured as follows: 
section 2 provides background material relating to our 
approach and in particular describes current trends in 
autonomic-based management and policy-based
management approaches. Section 3, provides an overview 
of the policy-based autonomic control service. Section 4, 
describes the development of the control service based on 
Jini middleware technology. Section 5, presents a case 
study and section 6, draws overall conclusions and
mentions future work.
2. Background
Recent research has focused on the development of 
methods and techniques to support runtime management 
and self-governance of distributed applications, which is 
reviewed briefly below.
2.1 Policy-Based Management
Much research has addressed the use of policies for 
dynamic management of large distributed systems [1-3].
Moffett et al. [3] stated the necessity of representing and 
manipulating policy management of distributed systems. 
In policy management, action policies are represented as a 
policy hierarchy, where each policy in the hierarchy
represents a plan that meets a specific objective. Sloman
and Lupu [10] studied authorization and obligation
policies’ specification for programmable networks. Such 
policies are interpreted to facilitate runtime activation, de-
activation and/or their modification without having to 
shutdown the network node. Other approaches to policy-
based management have used condition-action rules to 
support a static policy configuration-based solution, in 
which human intervention is required for system
reconfiguration and policy deployment. Moffett et al. [11]
have proposed a framework for supporting automated 
policy deployment and flexible event triggers to permit 
dynamic policy configuration, focusing on solutions for 
dynamic adaptation of policies in response to changes 
within the managed environment. Other research efforts 
have focused on policy specification and enforcement for 
dynamic service management. For instance, the IETF
Proceedings of the Fifth IEEE International Workshop on Policies for Distributed Systems and Networks (POLICY’04) 
0-7695-2141-X/04 $ 20.00 © 2004 IEEE 
2Policy Working Group is developing a QoS network
management framework using the X.500 directory service, 
where policies are encoded as If-Then rules and stored in 
directories [12].
     
Figure 1: Autonomic management process model.
2.2 Autonomic-Based Management
Within the distributed system community work is
underway to understand and design autonomic computing 
systems endowed with self-management and abilities to 
adjust to unpredictable changes [13]. The two main
elements of autonomic management are the functional
unit and the management unit. The functional unit
performs the main operation and is provided by elements 
such as web services or databases. The management unit 
is responsible for system resources and operational
performance and hence the reconfiguration of resources 
according to adaptive changes [5]. Autonomic systems 
have been defined by IBM [6] as system that have “… The
ability to manage themselves and dynamically adapt to 
change in accordance with policies and objectives…”.
In other words, they have the ability to monitor, diagnose 
and heal themselves. This entails that systems have the 
ability to dynamically insert and remove code at runtime. 
Hot swapping [8, 9] is proposed as a means to enable 
autonomic software systems to interpose and/or replace
components (code) in response of either failure or
software maintenance.
3. Policy-Based Autonomic Control Service
Autonomic systems must discharge the intended self-
management functionality in a safe, controllable and 
predictable manner avoiding any emerging, accidental
errors or undesirable features [9-12]. This therefore
requires that autonomic systems extend and integrate
autonomic control models with policy-based service [9]. 
This formed the motivation for our work, which provides a 
self-governance mechanism based on a developed
Extensible Believe, Desire and Intension (EBDI) model for 
deliberative systems. The EBDI model is used to guide 
and manage an autonomic application’s self-healing
processes.
Figure 1 illustrates the autonomic management and 
interaction model used to monitor an application. The 
overall process starts with the monitoring process to 
detect conflicts using the control service internal and 
external polices. This followed by the diagnosis process 
and then the repair strategies, which are based on the 
proposed EBDI model (Sec. 4). The strategies are
embedded in an external format such as XML, which may 
be parsed and translated into executable format by the 
system interpreter. In addition the distributed shared 
space is used to facilitate the remote integration and 
coordination of the distributed control services.
Figure 2:The control  service architectural  layer
s.
4. Design of the Proposed Control Service
As shown in Figure 2, the design of the policy-based
autonomic control service combines three main services: 
• Service Manager: is used to adapt the structural 
components and the dynamic behaviour of the 
services they provide. Structural components can 
evaluate their behaviour and environment against 
their specified goals with capabilities to revise 
their structure and behaviour accordingly [20]. 
This separation into distinct service managers 
eases their management by decentralizing the
control of each individual application-level
service. Service managers look after their
application-level service and monitor its
behaviour using an external and internal policies.
• JavaSpace Service: is a persistent distributed 
shared memory used by the self-healing process 
for awareness and coordination. It stores the
required information and reports service states 
for use by the system controller service to repair 
and reconfigure the system in the event of any 
Proceedings of the Fifth IEEE International Workshop on Policies for Distributed Systems and Networks (POLICY’04) 
0-7695-2141-X/04 $ 20.00 © 2004 IEEE 
3service failures. The JavaSpace service uses
external policies to notify the system controller 
directly with remote events. The remote events 
may either notify of changes in application-level
service states (as reported by the service
manager) or notify the system controller if the 
lease of a service manager should expire. 
• System Controller: is responsible for the
dynamic reconfiguration of a given application 
by coordinating the activities of individual
service managers. Each manager is used as a 
meta-service to monitor, repair and adapt its
associated application-level services in the event 
of conflicts/failures. Service managers may then 
report their associated application-level service 
states using the JavaSpace service. Associated 
external policies can either trigger a notification 
event to the system controller or request a
system controller service’s activation to monitor 
a given application service states. For example, 
the action shown below may be used if the
average latency of the control process exceeds 
the maximum allowed latency. If such a case
occurs the control rule detects and triggers an 
execution failure notification leading to a self-
healing process.
The development of system repair strategies and self-
healing control is based on our proposed Extensible
Beliefs, Desires and Intension (EBDI) model [11-13]. The 
model is concerned with situated intentional software that 
continuously monitors and/or observes its environment 
and acts to changes in accordance with its sets of policies.
This architecture is based on a control service model 
that follows a cycle of monitoring the target application, 
detecting undesirable behaviours (events), identifying
conflicts/errors, prescribing remedial action plans and
enacting change plans through reconfiguration1.
1 A full description of the design and implementation of the
autonomic middleware control service is out of the scope of this 
paper and can be found in a longer version of this paper [12].
Figure 3: The Architectural view of the EmergeIT
S  
appl icat ion. 
5. Case Study
The policy-based autonomic control service has been 
developed and demonstrated through an industrial case-
study, namely EmergeITS [12]. The latter was developed
using Jini middleware as a proof-of-concept for self-
healing software for intelligent networked appliances
(vehicles). The case-study was developed in collaboration 
with the Merseyside Emergency Fire Service (Fig. 4). One 
of the EmergeITS services evaluated was the 3-in-1 phone, 
which allows a mobile phone or PDA device to be used in 
one of three different modes: a cellular phone, a WAP 
phone or a walkie-talkie. The 3-in-1 phone may be used for 
either voice communication or to receive multimedia
content, subject to the requirements of the user and 
availability of a communication service provider. An
autonomic middleware control service prototype was
incorporated into the case study to provide the meta-
control software to support EmergeITS over the network.
The 3-in-1phone service is hosted by an in-vehicle
computer, which also acts as a gateway. If the local
service deployment should fail, then a service manager is 
started to provide self-monitoring, self-diagnosis, self-
repair and self-adaptation. In the event of failure, the 
service manager will select an appropriate repair strategy, 
thereby providing a degree of conflict resolution and fault 
tolerance. The service manager stores the 3-in-1 phone 
service state in a JavaSpace service and the system
controller may check these states against external policies.
 If (control_avLatency is larger than           
          control_maxLatency) 
  start 
       conflict_monitor(true); 
       start_control_process(); 
    end; 
Proceedings of the Fifth IEEE International Workshop on Policies for Distributed Systems and Networks (POLICY’04) 
0-7695-2141-X/04 $ 20.00 © 2004 IEEE 
4Figure 6:The XML descript ion of a self−repair str
ategy.
7. Conclusions and Future Work 
    In this paper we have described the development of a 
policy-based autonomic control service, which has been 
implemented using Jini middleware technology. The
control service incorporates the policies (either internal or 
external) within the architecture of the autonomic control 
service to achieve policy-based autonomic control of
distributed applications at run time.
   We have briefly described the proposed Extensible BDI 
(EBDI), which is a policy (normative)-based model that is 
used to examine the system external policies and for
generating the appropriate repair strategy at runtime.
   We have described the main services of our policy-
based autonomic control service (service manager,
JavaSpace service and system controller service) and 
demonstrated their use to control a 3-in-1-phone
application service. In addition we have evaluated the 
performance of the autonomic control service using the 
elapsed time and the average latency as metrics of the 
system at runtime.
   In our future work, we intend to improve/enhance the 
autonomic control service on two main fronts: self-
protection and machine learning. There is a need to 
provide self-protection to provide security in
untrustworthy environments. There is also a need to equip 
the current model with the machine learning capabilities so 
that the policy-based autonomic control service can
access history and knowledge relating to the previous 
failure cases.
8. References
1. E. Lupu, et al. A Policy Based Role Framework for 
Access Control. in First ACM/NIST Workshop on 
Role-Based Access Control. 1995. Maryland USA: 
ACM.
2. J. Moffett and M.Sloman, Policy Hierarchies for
Distributed Systems Management. IEEE Journal on 
Selected Areas in Communications, 1993. 11: p. 1404-
1414.
3. K. Yoshihara, M.Isomura, and H.Horiuchi. Distributed
Policy-based Management Enabling Policy
Adaptation on Monitoring using Active Network 
Technology. in 12th IFIP/IEEE International
Workshop on Distributed Systems: Operations and 
Management. 2001. Nancy France.
4. D. Chess, C.Palmer, and S.White, Security an
autonomic computing environment. IBM SYSTEMS 
JOURNAL, 2003. 42.
5. J. Appavoo, et al., Enabling autonomic behavior in 
systems software with hot  swapping,. IBM SYSTEMS 
JOURNAL, 2003. 42.
6. M. Sloman and E. Lupu. Policy Specification for 
Programmable Networks. in First International
Working Conference on Active Networks  (IWAN'99).
1999. Berlin.
7. K. Barber, et al. Conflict Representation and
Classification in a Domain Independent Conflict 
Management Framework . in the Third International 
Conference on Autonomous Agents. 1999. Seattle
WA.
8. P. Horn, Autonomic Computing: IBM's Perspective 
on the State of Information Technology. IBM
Corporation, 2001.
9. D. Reilly, et al. An Instrumentation and Control-
Based Approach for Distributed Application
Management and Adaptation. in Workshop on Self-
Healing Systems (WOSS'02). 2002. Charleston SC
USA.
10. N. Badr, D. Reilly, and A. Taleb-Bendiab. A Conflict 
Resolution Control Architecture for Self-Adaptive
Software, in the International Workshop on
Architecting Dependable Systems: WADS 2002
(ICSE 2002). 2002. Florida USA.
11. N. Badr, An Investigation into Autonomic
Middleware Control Service to Support Distributed 
Self-Adaptive Software, PhD thesis, School of
Computing and Mathematical Sciences, Liverpool
JMU, 2003, www.cms.livjm.ac.uk/cmsnbadr.
12. N. Badr, D. Reilly and A. Taleb-Bendiab, "Policy-
Based Autonomic Control Service", Technical
Report, School of Computing and Mathematical
Sciences, Liverpool JMU, 2004,
www.cms.livjm.ac.uk/cmsnbadr.
13. M. Bratman, Intentions, Plans, and Practical Reason.
Harvard University Press, 1987.
Proceedings of the Fifth IEEE International Workshop on Policies for Distributed Systems and Networks (POLICY’04) 
0-7695-2141-X/04 $ 20.00 © 2004 IEEE 


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