Wico Mulder

Vo Management Solutions VO Management e-Services

A virtual organization is usually considered to be a set of cooperating independent entities, which to the outside world provides a set of services and functionality as if they were one organization. Virtual organization management denotes the organisation, allocation and co-ordination of resources and their activities as well as their inter-organisational dependencies to achieve the objectives within the required time, cost and quality frame. Collaboration and management therefore have to be tackled by provision of adequate methods and services for the single users involved, in dependence of their roles and profiles within the network. Based on the outlined results described in the previous 2 Chapters the following chapter concentrates on the description of e-Services as Solutions for Virtual Organization management.

Grid management support by means of collaborative learning agents

The complex and dynamic settings of grid environments lead to challenges on their operational maintenance. The growth of these environments in terms of size and usage requires supporting systems to be of a more sophisticated level. Contemporary tools lack the ability to relate and infer events. Communication across organizational domains and interoperability between existing monitoring tools is subject to improvement. In this paper we present an information system, based on collaborative agents, that supports system administrators in monitoring the grid. While observing log files, the agents learn patterns about job-traffic in their own local domain of the grid and share information to provide global or multi-domain overviews. The agents represent their knowledge in the form of deterministic finite automata (DFA). We discuss our collaborative learning mechanism and show the results of our experiments with data of two grid-sites. Our system generated job-traffic overviews that gave new insights in the performance of the grid environment.

Distributed Information Services Supporting Collaborative Network Management

Information retrieval is one of the key processes in the management of collaborative networks (CNs). For large and complex networks manual processes need to be supported by automated retrieval systems.

Collaborative learning agents supporting service network management

Service oriented systems need to be maintained to keep the requestedlevel of service. This is challenge in large grid- and saas based networks that aremanaged by numerous entities. This paper is about supporting multi agent systemsthat operate in the network and support its management by learning actualstructures from life observed logging data. We focus on a collaborative grammarinduction mechanism in which agents share local models in order toretrieve a model of the structure of the total service network. We studied theperformance of groups of agents while varying the size and degree of communication.We motivate the application of the mechanism in the domain of serviceoriented system and show the results of experiments using a distributedagent-based monitoring system. We promote further research in the overlappingscientific disciplines of multi agent systems and machine learning in the applicationdomain of service oriented systems.

Supervision Of Collaborative Processes In VOs

VO managers need a sound information basis to fulfil their management tasks. Hence, performance measurement is one of the key processes in the management of Virtual Organisations (VOs). In case VOs are complex in terms of dimensions, interactions or the level of heterogeneity, information retrieval and performance measurement becomes a demanding task and causes high efforts. A defined methodology that guides the VO manager through the process and support by means of ICT services can help to obtain the needed information basis in an efficient way. The constraints and characteristics of the networks imply that the applied methodologies and services must be able to cope with distributed, dynamic, heterogeneous environments. They also require a performance measurement approach that is tuned towards the behaviour of the network as a whole, which might differ from traditional approaches that are used in single organisations or static cooperation such as supply chains. This chapter is about the need and the challenges of a collaborative performance measurement approach and the requirements of its corresponding tool support, aligned to the specific conditions of VOs and relevant aspects for VO management.

Towards a Formal Foundation for Aggregating Scientific Workflows

In e-Science, scientific workflow systems are used to share data and knowledge in collaborative experiments. In recent work we discussed the concepts of a workflow bus [1], allowing multiple workflow systems to be coupled in a meta-workflow system with multiple execution models. In this paper we propose an approach for a formal model to perform the task of reasoning of about the execution models of such workflow systems. We propose that I/O Automata can be used as a formalism to prove the correctness of complicated workflows involving multiple workflow engines and execution models.

Agent-Mining of Grid Log-Files: A Case Study

Grid monitoring requires analysis of large amounts of log files across multiple domains. An approach is described for automated extraction of job-flow information from large computer grids, using software agents and genetic computation. A prototype was created as a first step towards communities of agents that will collaborate to learn log-file structures and exchange knowledge across organizational domains.

Real-time context aware reasoning in on-board intelligent traffic systems: An Architecture for Ontology-based Reasoning using Finite State Machines

In-vehicle information management is vital in intelligent traffic systems. In this paper we motivate an architecture for ontology-based context-aware reasoning for in-vehicle information management. An ontology is essential for system standardization and communication, and ontology-based reasoning allows context-awareness, inference and advanced reasoning capabilities. However, the amount of computational power it requires often conflicts with the computational limitations of on-board units, as well as the high demand for timeliness and safety. Our approach uses ontology-based reasoning and a finite state machine (FSM). By combining ontology and FSM, we illustrate how a heavy-weight reasoning-solution could be applied in a light-weight computational environment.

Towards Ontology-Based CNO Matching Applied to Squads

As the interest in Collaborative Networks grows, and the number of organizations participating in a Virtual Breeding Environment increases, tools will be needed to support the creation of Collaborative Network Organizations. An important driver for this support is that in modern commercial environments a rapid response to changes in the market is essential to remain competitive. We are working on a domain-ontology and a model that supports the composition of CNOs using semantic capability descriptions. We apply this model in the field of software development, where multiple expertise teams form dynamic task-forces, called Squads, in order to build and manage computer applications. In this paper, we focus on the development of the domain-ontology and the elements of the composition model. We are working on a prototype that supports the creation of Squads by means of capability matching.

MDL in the limit

We show that within the Gold paradigm for language learning an informer for a superfinite set can cause an optimal MDL learner to make an infinite amount of mind changes. In this setting an optimal learner can make an infinite amount of wrong choices without approximating the right solution. This result helps us to understand the relation between MDL and identification in the limit in learning: MDL is an optimal model selection paradigm, identification in the limit defines recursion theoretical conditions for convergence of a learner.