autonomic computing
The 4th IEEE International Conference on Autonomic Computing

Jacksonville, Florida, USA   June 11-15, 2007
 Best student paper award
 Presentations slides
 Conference photos
 General Chairs
 Mazin Yousif, Intel Corporation, USA
 Omer F. Rana, Cardiff University, UK
 Program Chairs
 José Fortes, Univ. of Florida, USA
 Kumar Goswami, HP Labs, USA
 Call for Papers
 Call for Tutorials/Workshops
 Call for Demonstrations/Exhibits
 Important Dates
  10:00 PM PST, Jan 12, 2007
 Full paper:
  10:00 PM PST, Jan 22, 2007
 Workshop proposals:
  Dec 11, 2006
 Demo/Exhibit proposals:
  Mar 17, 2007
 Tutorial proposals:
  Mar 17, 2007
 Author notification: Mar 7, 2007
 Final manuscripts: Apr 3, 2007
 Conference: Jun 11-15, 2007
 Further Information
 icac2007 AT
 Contact Us
 Mazin Yousif
   mazin.s.yousif AT
 Omer F. Rana
   o.f.rana AT
 José Fortes
   fortes AT
 Kumar Goswami
   kumar.goswami AT
 Renato Figueiredo
   renato AT


Workshop on Adaptive Methods in  Autonomic Computing Systems (AMACS)
[Program] [CFP] [Website]

2nd International Workshop on Engineering Emergence in Decentralised Autonomic Systems (EEDAS 2007)
[CFP] [[Website]


2nd Workshop on Hot Topics in Autonomic Computing (HotAC II)
[CFP] [Website]

1st International Workshop on Policy-Based Autonomic Computing (PBAC 2007)
[CFP] [Website]

Workshop on QoS in Autonomic Communication Networks (ACN 2007)  (Canceled)


Autonomic Networking: Theory and Practice
John Strassner, Motorola Research Lab

Schedule: 8:30AM – 12:00PM, 1:30PM – 5:00PM, June 11 2007


The increasing complexity of computing systems is beginning to overwhelm the capabilities of software developers and system administrators to design, evaluate, integrate, and manage these systems. Autonomic networking is a collection of technologies and mechanisms that enable systems and components to govern their behavior in accordance with policies. This enables business needs to drive the services and resources available from the network. This tutorial is aimed at giving the participant a reasonably deep understanding of the motivation for autonomic networking, concentrating on the semantic and behavioral aspects of network management. After defining autonomic computing and networking, this tutorial will first describe different architectural styles of autonomics, and then elaborate on how to build autonomic elements. Then, a novel autonomic networking architecture will be examined in detail, including examining the motivation for this architecture, the role that each of its components play, and up to date progress in implementing this architecture. This theory will be reinforced with use cases and practical examples, including a demonstration of ongoing research work in Motorola Labs.

Adaptive Middleware for Autonomic Communications
Simon Dobson, Dave Lewis, and Paddy Nixon, University College Dublin

Schedule: 8:30AM – 12:00PM, June 11 2007


Traditional systems engineering has concentrated on the correctness and robustness of systems within a well-defined and clearly delineated environment. Recent trends in open systems, ad hoc networking, self-management and pervasive computing are leading to a situation in which a system’s configuration and behaviour must adapt to a more dynamic environment in order to continue to deliver its required services. Autonomic computing and communications form an active area of current research aiming to address these issues. From a systems perspective, autonomic systems aim to reduce the costs of ownership by devolving some responsibility for a system’s administration onto the system itself. From a programming perspective, this leads to a whole new range of concerns whereby programmers must address self-configuration, self-management, self-optimisation and a range of other self-* properties. This poses significant challenges for developers. The traditional approach to managing complexity in distributed systems has been through middleware providing a platform which abstracts-away from the details of the underlying communications system. While traditional middleware does not directly address self-* issues, recent trends in middleware design are leading to platforms that are themselves significantly self-managing and self-optimising, and which lend themselves very well to the development of highly autonomic applications and systems.


The purpose of this tutorial is to introduce attendees to the issues and tools – both existing and emerging – in the area of adaptive and autonomic middleware. The emphasis is on understanding the available tools and techniques, and how these may be used together to build systems with self-* properties. These techniques include decentralisation, peer-to-peer networking, gossipping, knowledge-based decision-making, ontologies and content-directed networking. Such techniques are to a large extent technology-neutral, and so may be applied across a range of scales from sensor networks to enterprise information systems.

Learning Objectives:

Attendees will acquire a familiarity with the business and technological drivers that affect adaptive autonomic systems development. They will acquire an high-level view of the application of these techniques to existing and emerging tools, platforms and standards. The target audience is developers wanting to understand how to develop self-managing applications. It would also be of interest to systems architects and other strategic managers wanting to understand the impact that this technology will have on their core business systems. The tutorial will be fully supported by slides on a web site, as well as a comprehensive reading list for those wishing to dig deeper into the topics covered.

About the Presenters:

Simon Dobson and Paddy Nixon are with UCD Dublin’s Systems Research Group, while Dave Lewis is a researcher in the School of Computer Science and Statistics at Trinity College Dublin. Between them they have over forty years’ experience in large-scale distributed systems, advanced communications and programming languages within both academia and industry.

Design and Implementation of Intelligent Agents for Autonomic Computing
Benno Overeinder, Vrije University Amsterdam

Schedule: 1:30PM – 5:00PM, June 11 2007


Agent systems have been used for various applications in complex domains. The agent approach decomposes a problem in small autonomous entities, while the agents manage and schedule their activities to achieve a goal. Multiple agents can coordinate their activities to achieve a common goal; to facilitate this, a number of cooperation and coordination schemes exist. By the autonomous behavior of multi-agent systems, many similarities exist with autonomic computing. In autonomic computing (in its most general sense) entities can be both software (applications, services, etc.) as well as hardware (host, network, hard disk, etc.). In general, both software and hardware entities are monitored by software components. The software components monitoring and managing the entities in an autonomic system can be considered a multi-agent system using cooperation and coordination schemes well-known in AI research. In the tutorial, an introduction to intelligent agents will be given. Typically issues like weak and strong notion of agency will be introduced, defining the autonomous behavior of agents in both system terms and their translation into high-level (human) notions used in reasoning. After a general introduction to agents, an in-depth presentation of system requirements for multi-agent systems will be given. General principles of agent runtime environments (middleware) will be introduced and the specific implementation in the AgentScape middleware discussed. Multi-agent systems also set a number of requirements to agent platforms, e.g., life-cycle management, (optionally) mobility, security and resource management. All these aspects will be discussed and examples in the AgentScape middleware will be given. In the third part, a practical example will be presented for a resource negotiation scenario where agents need to acquire resources to complete a task. Resources are also autonomous entities and agents have to negotiate to settle the terms of resource access and usage. Optionally, the tutorial can include a number of practical hands-on exercises. In these exercises, different aspects of agents, e.g., cooperation, coordination, negotiation, etc., can be exemplified, resulting in a deeper understanding of these concepts. The agents will be programmed in the Java programming language and run on the AgentScape middleware.

Target Audience:

The tutorial is targeted to any researcher and/or professional that is active in autonomic computing and has an interest in the design and implementation of autonomic computing architectures. The tutorial presents a general agent-based approach to autonomic computing rather than a specific topic in autonomic computing. With the introduction of intelligent agents, some common techniques from Artificial Intelligence, such as cooperation, coordination, and/or diagnosis, will be presented in context of the autonomic computing.

Duration of the Tutorial:

Half-day tutorial, or a full-day tutorial including a practical hands-on session (about 2 + 3 hours sessions).

Prerequisite Knowledge:

No prerequisite knowledge is required, other than some general knowledge of distributed systems (which is considered to be known to the autonomic computing audience). For the practical hands-on, a basic knowledge of the Java programming language is useful.


Schedule: 3:00PM - 3:30PM, June 12 2007

Policy-Driven Autonomic Management in Enterprise-Scale Information Flows

Vibhore Kumar, Brian F. Cooper, Greg Eisenhauer, Srihari Govindharaj, Chaitanya Karlekar, Mohamed Mansour, Karsten Schwan, Sangeetha Seshadri, Balasubramaniam Seshasayee
(Georgia Tech and Yahoo! Research)

A Standards Based Self-managing Resource Framework

Peter Brittenham, Balan Subramanian
(IBM Tivoli Autonomic Computing)

Conventional WSDM: Plumbing for Autonomic Computing

Andrew Eberbach, Balan Subramanian
(IBM Tivoli Autonomic Computing)

Virtualized Electronic Trading with EMS

Duncan Johnston Watt

Adaptive HP Infrastructure for SAP

Sven Graupner
(HP Labs/SAP Research)

SLA Decomposition: Translating Service Level Objectives to System Level Thresholds

Yuan Chen
(HP Labs)

IPOP: A Self-Organizing Virtual Network for Wide-Area Environments

Arijit Ganguly, David Wolinsky, P. Oscar Boykin, Renato J. Figueiredo
(University of Florida)

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