Ontological Formalization for Workflow-based Computational Experiments

AbstractWorkflow-based computational experiment is a widespread way to organize distributed simulations. But the lack of IT experience and skills is the critical issue which scientists usually face with. By this paper we describe the reasoning capabilities, which are obtained from the proposed hierarchical structure for expert's knowledge formalization. The contribution of this paper is the ontological representation of a structure, which make end-users to deal with domain models compiled of fine-grained domain and infrastructural entities in order to generate an executable workflow as a result. A task of forecasting of storm surges and decision support for gates maneuvering is presented a use-case of the paper

STRIPS Action Discovery

The problem of specifying high-level knowledge bases for planning becomes a hard task in realistic environments. This knowledge is usually handcrafted and is hard to keep updated, even for system experts. Recent approaches have shown the success of classical planning at synthesizing action models even when all intermediate states are missing. These approaches can synthesize action schemas in Planning Domain Definition Language (PDDL) from a set of execution traces each consisting, at least, of an initial and final state. In this paper, we propose a new algorithm to unsupervisedly synthesize STRIPS action models with a classical planner when action signatures are unknown. In addition, we contribute with a compilation to classical planning that mitigates the problem of learning static predicates in the action model preconditions, exploits the capabilities of SAT planners with parallel encodings to compute action schemas and validate all instances. Our system is flexible in that it supports the inclusion of partial input information that may speed up the search. We show through several experiments how learned action models generalize over unseen planning instances.Comment: Presented to Genplan 2020 workshop, held in the AAAI 2020 conference (https://sites.google.com/view/genplan20) (2021/03/05: included missing acknowledgments

Bridging the gap between business process models and service-oriented architectures with reference to the grid environment

In recent years, organisations have been seeking technological solutions for enacting their business process models using ad-hoc and heuristic approaches. However, limited results have been obtained due to the expansion of business processes across geographical boundaries and the absence of structured methods, frameworks and/or Information Technology (IT) infrastructures to enact these processes. In an attempt to enact business process models using distributed technologies, we introduce a novel architectural framework to bridge the gap between business process models and Grid-aware Service-Oriented Architectures (GSOA). BPMSOA framework is aligned with the Model-Driven Engineering (MDE) approach and is instantiated for role-based business process models [in particular Role Activity Diagramming (RAD)], using mobile process languages such as pi-ADL. The evaluation of the BPMSOA framework using the Submission process from the digital libraries domain has revealed that role-based business process models can be successfully enacted in GSOA environments with certain limitations. © 2011 Inderscience Enterprises Ltd

Contributions to Service Level Agreement (SLA), Negotiation and Monitoring in Cloud Computing

Cloud computing is a dynamic field of research, as the latest advances in the cloud computing applications have led to development of a plethora of cloud services in the areas of software, hardware, storage, internet of things connected to the cloud, and 5G supported by the cloud networks. Due to ever increasing developments and the subsequent emergence of a wide range of cloud services, a cloud market was created with cloud providers and customers seeking to buy the cloud services. With the expansion of the cloud market and the presence of a virtual environment in which cloud services are provided and managed, the face to-face meetings between customers and cloud providers is almost impossible, and the negotiation over the cloud services using the state-of-the-art autonomous negotiation agents has been theorized and researched by several researchers in the field of cloud computing, however, the solutions offered by literature are less applicable in the real-time cloud market with the evolving nature of services and customers’ requirements. Therefore, this study aimed to develop the solutions addressing issues in relation to negotiation of cloud services leading to the development of a service-level agreement (SLA), and monitoring of the terms and conditions specified in the SLA. We proposed the autonomous service-level framework supported by the autonomous agents for negotiating over the cloud services on behalf of the cloud providers and customers. The proposed framework contained gathering, filtering, negotiation and SLA monitoring functions, which enhanced its applicability in the real-time cloud market environment. Gathering and filtering stages facilitated the effectiveness of the negotiation phase based on the requirements of customers and cloud services available in the cloud market. The negotiation phase was executed by the selection of autonomous agents, leading to the creation of an SLA with metrics agreed upon between the cloud provider and the customer. Autonomous agents improved the efficiency of negotiation over multiple issues by creating the SLA within a short time and benefiting both parties involved in the negation phase. Rubinstein’s Alternating Offers Protocol was found to be effective in drafting the automated SLA solutions in the challenging environment of the cloud market. We also aimed to apply various autonomous agents to build the new algorithms which can be used to create novel negotiation strategies for addressing the issues in SLAs in cloud computing. The monitoring approach based on the CloudSim tool was found to be an effective strategy for detecting violations against the SLA, which can be an important contribution to building effective monitoring solutions for improving the quality of services in the cloud market

A knowledge-based approach to scientific workflow composition

Scientific Workflow Systems have been developed as a means to enable scientists to carry out complex analysis operations on local and remote data sources in order to achieve their research goals. Systems typically provide a large number of components and facilities to enable such analysis to be performed and have matured to a point where they offer many complex capabilities. This complexity makes it difficult for scientists working with these systems to readily achieve their goals. In this thesis we describe the increasing burden of knowledge required of these scientists in order for them to specify the outcomes they wish to achieve within the workflow systems. We consider ways in which the challenges presented by these systems can be reduced, focusing on the following questions: How can metadata describing the resources available assist users in composing workflows? Can automated assistance be provided to guide users through the composition process? Can such an approach be implemented so as to work with the resources provided by existing Scientific Workflow Systems? We have developed a new approach to workflow composition which makes use of a number of features: an ontology for recording metadata relating to workflow components, a set of algorithms for analyzing the state of a workflow composition and providing suggestions for how to progress based on this metadata, an API to enable both the algorithms and metadata to utilise the resources provided by existing Scientific Workflow Systems, and a prototype user interface to demonstrate how our proposed approach to workflow composition can work in practice. We evaluate the system to show the approach is valid and capable of reducing some of the difficulties presented by existing systems, but that limitations exist regarding the complexity of workflows which can be composed, and also regarding the challenge of initially populating the metadata ontology