12,311 research outputs found
ACHIEVING AUTONOMIC SERVICE ORIENTED ARCHITECTURE USING CASE BASED REASONING
Service-Oriented Architecture (SOA) enables composition of large and complex
computational units out of the available atomic services. However, implementation of
SOA, for its dynamic nature, could bring about challenges in terms of service
discovery, service interaction, service composition, robustness, etc. In the near future,
SOA will often need to dynamically re-configuring and re-organizing its topologies of
interactions between the web services because of some unpredictable events, such as
crashes or network problems, which will cause service unavailability. Complexity and
dynamism of the current and future global network system require service architecture
that is capable of autonomously changing its structure and functionality to meet
dynamic changes in the requirements and environment with little human intervention.
This then needs to motivate the research described throughout this thesis.
In this thesis, the idea of introducing autonomy and adapting case-based reasoning
into SOA in order to extend the intelligence and capability of SOA is contributed and
elaborated. It is conducted by proposing architecture of an autonomic SOA
framework based on case-based reasoning and the architectural considerations of
autonomic computing paradigm. It is then followed by developing and analyzing
formal models of the proposed architecture using Petri Net. The framework is also
tested and analyzed through case studies, simulation, and prototype development. The
case studies show feasibility to employing case-based reasoning and autonomic
computing into SOA domain and the simulation results show believability that it
would increase the intelligence, capability, usability and robustness of SOA. It was
shown that SOA can be improved to cope with dynamic environment and services
unavailability by incorporating case-based reasoning and autonomic computing
paradigm to monitor and analyze events and service requests, then to plan and execute
the appropriate actions using the knowledge stored in knowledge database
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A classification of emerging and traditional grid systems
The grid has evolved in numerous distinct phases. It started in the early â90s as a model of metacomputing in which supercomputers share resources; subsequently, researchers added the ability to share data. This is usually referred to as the first-generation grid. By the late â90s, researchers had outlined the framework for second-generation grids, characterized by their use of grid middleware systems to âglueâ different grid technologies together. Third-generation grids originated in the early millennium when Web technology was combined with second-generation grids. As a result, the invisible grid, in which grid complexity is fully hidden through resource virtualization, started receiving attention. Subsequently, grid researchers identified the requirement for semantically rich knowledge grids, in which middleware technologies are more intelligent and autonomic. Recently, the necessity for grids to support and extend the ambient intelligence vision has emerged. In AmI, humans are surrounded by computing technologies that are unobtrusively embedded in their surroundings.
However, third-generation gridsâ current architecture doesnât meet the requirements of next-generation grids (NGG) and service-oriented knowledge utility (SOKU).4 A few years ago, a group of independent experts, arranged by the European Commission, identified these shortcomings as a way to identify potential European grid research priorities for 2010 and beyond. The experts envision grid systemsâ information, knowledge, and processing capabilities as a set of utility services.3 Consequently, new grid systems are emerging to materialize these visions. Here, we review emerging grids and classify them to motivate further research and help establish a solid foundation in this rapidly evolving area
Towards goal-based autonomic networking
The ability to quickly deploy and efficiently manage services is critical to the telecommunications industry. Currently, services are designed and managed by different teams with expertise over a wide range of concerns, from high-level business to low level network aspects. Not only is this approach expensive in terms
of time and resources, but it also has problems to scale up to new outsourcing and/or multi-vendor models, where subsystems and teams belong to different organizations. We endorse the idea, upheld among others in the autonomic computing community, that the network and system components involved in the provision of a service must be crafted to facilitate their management. Furthermore, they should help bridge the gap between network and business concerns. In this paper, we sketch an approach based on
early work on the hierarchical organization of autonomic entities that possibly belong to different organizations. An autonomic entity governs over other autonomic entities by defining their goals. Thus, it is up to each autonomic entity to decide its line of actions in order to fulfill its goals, and the governing entity needs not know about the internals of its subordinates. We illustrate the approach with a simple but still rich example of a telecom service
Dynamic Model-based Management of Service-Oriented Infrastructure.
Models are an effective tool for systems and software design. They allow software architects to abstract from the non-relevant details. Those qualities are also useful for the technical management of networks, systems and software, such as those that compose service oriented architectures. Models can provide a set of well-defined abstractions over the distributed heterogeneous service infrastructure that enable its automated management. We propose to use the managed system as a source of dynamically generated runtime models, and decompose management processes into a composition of model transformations. We have created an autonomic service deployment and configuration architecture that obtains, analyzes, and transforms system models to apply the required actions, while being oblivious to the low-level details. An instrumentation layer automatically builds these models and interprets the planned management actions to the system. We illustrate these concepts with a distributed service update operation
Incorporating prediction models in the SelfLet framework: a plugin approach
A complex pervasive system is typically composed of many cooperating
\emph{nodes}, running on machines with different capabilities, and pervasively
distributed across the environment. These systems pose several new challenges
such as the need for the nodes to manage autonomously and dynamically in order
to adapt to changes detected in the environment. To address the above issue, a
number of autonomic frameworks has been proposed. These usually offer either
predefined self-management policies or programmatic mechanisms for creating new
policies at design time. From a more theoretical perspective, some works
propose the adoption of prediction models as a way to anticipate the evolution
of the system and to make timely decisions. In this context, our aim is to
experiment with the integration of prediction models within a specific
autonomic framework in order to assess the feasibility of such integration in a
setting where the characteristics of dynamicity, decentralization, and
cooperation among nodes are important. We extend an existing infrastructure
called \emph{SelfLets} in order to make it ready to host various prediction
models that can be dynamically plugged and unplugged in the various component
nodes, thus enabling a wide range of predictions to be performed. Also, we show
in a simple example how the system works when adopting a specific prediction
model from the literature
Semantic-based policy engineering for autonomic systems
This paper presents some important directions in the use of ontology-based semantics in achieving the vision of Autonomic Communications. We examine the requirements of Autonomic Communication with a focus on the demanding needs of ubiquitous computing environments, with an emphasis on the requirements shared with Autonomic Computing. We observe that ontologies provide a strong mechanism for addressing the heterogeneity in user task requirements, managed resources, services and context. We then present two complimentary approaches that exploit ontology-based knowledge in support of autonomic communications: service-oriented models for policy engineering and dynamic semantic queries using content-based networks. The paper concludes with a discussion of the major research challenges such approaches raise
An architecture for autonomic web service process planning
Web service composition is a technology that has received
considerable attention in the last number of years. Languages and tools to aid in the process of creating composite web services have been received specific attention. Web service composition is the process of linking single web services together in order to accomplish more complex tasks. One area of web service composition that has not received as much attention is the area of dynamic error handling and re-planning, enabling autonomic composition. Given a repository of service descriptions and a task to complete, it is possible for AI planners to automatically create a plan that will achieve this goal. If however a service in the plan is unavailable or erroneous the plan will fail. Motivated by this problem, this paper suggests autonomous re-planning as a means to overcome dynamic problems. Our solution involves automatically recovering from faults and creating a context-dependent alternate plan
Dynamic integration of context model constraints in web service processes
Autonomic Web service composition has been a challenging topic for some years. The context in which composition takes places determines essential aspects. A context model can provide meaningful composition information for services process composition. An ontology-based approach for context information integration is the basis of a constraint approach to dynamically integrate context validation into service processes. The dynamic integration of context constraints into an orchestrated service process is a necessary direction to achieve autonomic service composition
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