Three Decision-making Mechanisms to facilitate Negotiation of Service Level Agreements for Web Service Compositions

The negotiation of Service Level Agreements for composite web services is a very complex process. It involves the coordination of the negotiation process so that the end-to-end QoS requirements of the user request are satisfied while ensuring that the atomic QoS requirements are also simultaneously satisfied. This paper summarizes three decision-making mechanisms which support the process of Service Level Agreement negotiation for composite web services. The mechanisms include: the decomposition of the overall user preferences into the preferences of individual negotiation agents representing each atomic services within the composition; the selection of the prospective negotiation partners for the actual interaction from a list of potential service providers and finally the negotiation of Service Level Agreement with the selected provider agents while ensuring that the end-to-end QoS is satisfied

Towards A Comprehensive Cloud Decision Framework with Financial Viability Assessment

Most organizations moving their legacy systems to the cloud base their decisions on the naïve assumption that the public cloud provides cost savings. However, this is not always true. Sometimes the migration complexity of certain applications outweighs the benefits to be had from a public cloud. Moreover, the total cost of ownership does not necessarily decrease by moving to a public cloud. Therefore, there is a need for a disciplined approach for choosing the right cloud platform for application migration. In this paper, we propose a comprehensive cloud decision framework that includes an extensible decision criteria set, associated usage guidelines, a decision model for cloud platform recommendation, and a cost calculator to compute the total cost of ownership (TCO). The decision process works as follows. It begins with the ordering of relevant criteria, either according to industry best practice or the enterprise’s specific requirements and preferences. A technical recommendation is made on the basis of the criteria classification, which is then assessed for financial viability. By providing traceability of the cost items in the public/private TCO calculators to the decision criteria, the framework enables users to iterate through the decision process, determining and eliminating (if possible) the main cost drivers until a right balance is found between the desirable criteria and the available budget. We illustrate the need, benefits and value of our proposed framework through three different real-world use case scenarios

Towards a Flexible Cloud Architectural Decision Framework for Diverse Application Architectures

End user privacy is a critical concern for all organizations that collect, process and store user data as a part of their business. Privacy concerned users, regulatory bodies and privacy experts continuously demand organizations provide users with privacy protection. Current research lacks an understanding of organizational characteristics that affect an organization’s motivation towards user privacy. This has resulted in a “one solution fits all” approach, which is incapable of providing sustainable solutions for organizational issues related to user privacy. In this work, we have empirically investigated 40 diverse organizations on their motivations and approaches towards user privacy. Resources such as newspaper articles, privacy policies, and internal privacy reports that display information about organizational motivations and approaches towards user privacy were used in the study. We could observe organizations to have two primary motivations to provide end users with privacy as voluntary driven inherent motivation, and risk driven compliance motivation. Building up on these findings, we developed a taxonomy of organizational privacy approaches and further explored the taxonomy through limited exclusive interviews. With his work, we encourage authorities and scholars to understand organizational characteristics that define an organization’s approach towards privacy, in order to communicate regulations that enforce and encourage organizations to consider privacy within their business practices

Towards Case Completion with inferencing and solution identification using ‘Nested CBR’

Case Based Reasoning (CBR) provides a framework to capture past problems and their solutions to solve future problems. Problem cases are typically complete; however, it is not always possible to have a complete problem case due to complexity, lack of data, or availability of human expertise. The limitations of existing approaches for handling incomplete cases include a reliance upon manual input, such as Conversational CBR (CCBR) and Incremental CBR (ICBR), or a rigid structure of relationships maintained using a semantic ontology, to infer the missing feature values. Using the case base to infer feature values increases the efficiency and likelihood of identifying a relevant solution compared with manual interactions because the case base is based upon proven problem to solution correlation. Therefore, in this work-in-progress paper, we propose \u27Nested CBR\u27 as an approach for the automated completion of partial problem cases, and the subsequent solution identification, thereby avoiding manual input and improving solution efficiency and meaning

Towards a Cloud Architectural Decision Framework using Case-Based Reasoning and Rule-Based Reasoning

Correct decision making about the cloud platform architecture is crucial for the success of any cloud migration project; bad decisions can lead to undesirable consequences. Rules Based Reasoning (RBR), a popular approach for solving clearly defined problems, can be used for cloud platform recommendation if a comprehensive set of requirements are available. However, the responsibility of decision-making is increasingly moving away from the hands of the technical subject matter experts, and into the hands of the business sponsors who, despite being the end-all, be-all decision-makers, typically do not have access to sufficient information at the initial stages of the project lifecycle. Therefore, in this paper, we propose combining Case Based Reasoning (CBR) with RBR to assist business sponsors in making strategic decisions between public, private and hybrid cloud with a high level of confidence even at the initial stages of the project

Smart Contract-based Consensus Building for Collaborative Medical Decision-Making

Medical decision-making is moving away from the traditional one-off dyadic encounter between the patient and physician, and transitioning towards a more inclusive, shared decision-making process that also considers the inputs from other stakeholders. This ensures that a patient's decision is not only based on a medical opinion, but also includes other considerations such as impact on family members, legal and financial implications, and experiences of patients in similar situations. However, given the sensitive nature of health data and decisions, there are several challenges associated with safeguarding the privacy, security and consent of all contributors and assuring the integrity of the process. We propose a collaborative medical decision-making platform that uses a consensus building mechanism implemented using Blockchain-based Smart Contracts to address some of the above challenges, thereby giving the participants confidence that both the decision-making process and the outcome(s) can be trusted. We also present a proof-of-concept implementation using the private Ethereum Blockchain to demonstrate practicability

A2C: A Modular Multi-stage Collaborative Decision Framework for Human-AI Teams

This paper introduces A2C, a multi-stage collaborative decision framework designed to enable robust decision-making within human-AI teams. Drawing inspiration from concepts such as rejection learning and learning to defer, A2C incorporates AI systems trained to recognise uncertainty in their decisions and defer to human experts when needed. Moreover, A2C caters to scenarios where even human experts encounter limitations, such as in incident detection and response in cyber Security Operations Centres (SOC). In such scenarios, A2C facilitates collaborative explorations, enabling collective resolution of complex challenges. With support for three distinct decision-making modes in human-AI teams: Automated, Augmented, and Collaborative, A2C offers a flexible platform for developing effective strategies for human-AI collaboration. By harnessing the strengths of both humans and AI, it significantly improves the efficiency and effectiveness of complex decision-making in dynamic and evolving environments. To validate A2C's capabilities, we conducted extensive simulative experiments using benchmark datasets. The results clearly demonstrate that all three modes of decision-making can be effectively supported by A2C. Most notably, collaborative exploration by (simulated) human experts and AI achieves superior performance compared to AI in isolation, underscoring the framework's potential to enhance decision-making within human-AI teams

A coordinated architecture for the agent-based service level agreement negotiation of web service composition

Recent progress in the field of Web services has made it possible to integrate inter-organizational and heterogeneous services on the Web at runtime. If a user request cannot be satisfied by a single Web service, it is (or should be) possible to combine existing services in order to fulfill the request. However, there are several challenging issues that need to be addressed before this can be realized in the true sense. One of them is the ability to ensure end-to-end QoS of a Web service composition. There is a need for a SLA negotiation system which can ensure the autonomous QoS negotiation of Web service compositions irrespective of the application domain. In this paper we propose agent-based coordinated-negotiation architecture to ensure collective functionality, end-to-end QoS and the stateful coordination of complex services. We describe a prototype implementation to demonstrate how this architecture can be used in different application domains. We have also demonstrated how the negotiation system on the service provider\u27s side can be implemented both as an agent based negotiation system and as a Web service based negotiation system

Report on the 2nd Workshop on Human Centric Software Engineering & Cyber Security (HCSE&CS 2021)

As the creators, designers, coders, testers, users, and occasional abusers of all software systems-including cyber security systems - humans should be at the centre of all design and development efforts. Despite this, most software engineering and cyber security research and practices tend to be function, data, or process oriented. In contrast, human-centric software engineering focuses on the human-centric issues critical to successful software systems' engineering. The aim of the International Workshop on Human Centric Software Engineering & Cyber Security (HCSE&CS) was to provide a venue for sharing research ideas and outcomes on enhanced theory, models, tools, and capability for next-generation human-centric software engineering and cyber security. The Second HCSE&CS Workshop was held on 15 November 2021 in conjunction with ASE 2021, the 36th IEEE/ACM International Conference on Automated Software Engineering. It was originally intended to be held in Melbourne, Australia but was instead held virtually due to the COVID-19 pandemic. This post-workshop report provides an overview of the aims and motivation of the workshop as well as a summary of the presentations and discussions that took place during the workshop

Agent enabled adaptive management of QoS assured provision of composite services

The assurance of quality-of-service (QoS) is critical for the successful deployment of service-oriented applications, especially in open, dynamic, and distributed cross-organizational environments. Adaptive management of the QoS assured provision of composite services is required for more reliable, fault-tolerant, and flexible service delivery in such environments. It can be realized with software agents offering a unified framework and necessary capabilities for carrying out different adaptive management tasks across the whole lifecycle of composite service provision