PenChain: A Blockchain-Based Platform for Penalty-Aware Service Provisioning

Service provisioning is of paramount importance as we are now heading towards a world of integrated services giving rise to the next generation of service ecosystems. The huge number of service offerings that will be available to customers in future scenarios require a novel approach to service registry and discovery that allows customers to choose the offerings that best match their preferences. One way to achieve this is to introduce the provider’s reputation, i.e., a quality indicator of the provisioned service, as an additional search criterion. Now, with blockchain technology in our hands, automated regulation of service-level agreements (SLAs) that capture mutual agreements between all involved parties has regained momentum. In this article, we report on our full-fledged work on the conception, design, and construction of a platform for SLA-minded service provisioning called PenChain. With our work, we demonstrate that penalty-aware SLAs of general services–if represented in machine-readable logic and assisted by distributed ledger technology–are programmatically enforceable. We devise algorithms for ranking services in a search result taking into account the digitized values of the SLAs. We offer two scenario-based evaluations of PenChain in the field of precision agriculture and in the domain of automotive manufacturing. Furthermore, we examine the scalability and data security of PenChain for precision agriculture

Blockchain-enabled Real-time SLA Monitoring for Cloud-hosted Services

Cloud computing is an important technology for businesses and individual users to obtain computing resources over the Internet on-demand and exibly. Although cloud computing has been adopted across diverse applications, the owners of time-and-performance critical applications require cloud service providers' guarantees about their services, such as availability and response times. Service Level Agreements (SLAs) are a mechanism to communicate and enforce such guarantees typically represented as service level objectives (SLOs), and financial penalties are imposed on SLO violations. Due to delays and inaccuracies caused by manual processing, an automatic method to periodically verify SLA terms in a transparent and trustworthy manner is fundamental to effective SLA monitoring, leading to the acceptance and credibility of such service to the customers of cloud services. This paper presents a blockchain-based distributed infrastructure that leverages fundamental blockchain properties to achieve immutable and trustworthy SLA monitoring within cloud services. The paper carries out an in-depth empirical investigation for the scalability of the proposed system in order to address the challenge of transparently enforcing real-time monitoring of cloud-hosted services leveraging blockchain technology. This will enable all the stakeholders to enforce accurate execution of SLA without any imprecisions and delays by maintaining an immutable ledger publicly across blockchain network. The experimentation takes into consideration several attributes of blockchain which are critical in achieving optimum performance. The paper also investigates key characteristics of these factors and their impact to the behaviour of the system for further scaling it up under various cases for increased service utilization

Leveraging Architectural Approaches in Web3 Applications -- A DAO Perspective Focused

Architectural design contexts contain a set of factors that influence software application development. Among them, \textit{\textbf{organizational}} design contexts consist of high-level company concerns and how it is structured, for example, stakeholders and development schedule, heavily impacting design considerations. Decentralized Autonomous Organization (DAO), as a vital concept in the Web3 space, is an organization constructed by automatically executed rules such as via smart contracts, holding features of the permissionless committee, transparent proposals, and fair contribution by stakeholders. In this work, we conduct a systematic literature review to summarize how DAO is structured as well as explore its benefits\&challenges in Web3 applications

Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, finetuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGCdriven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks

14th Conference on DATA ANALYSIS METHODS for Software Systems

DAMSS-2023 is the 14th International Conference on Data Analysis Methods for Software Systems, held in Druskininkai, Lithuania. Every year at the same venue and time. The exception was in 2020, when the world was gripped by the Covid-19 pandemic and the movement of people was severely restricted. After a year’s break, the conference was back on track, and the next conference was successful in achieving its primary goal of lively scientific communication. The conference focuses on live interaction among participants. For better efficiency of communication among participants, most of the presentations are poster presentations. This format has proven to be highly effective. However, we have several oral sections, too. The history of the conference dates back to 2009 when 16 papers were presented. It began as a workshop and has evolved into a well-known conference. The idea of such a workshop originated at the Institute of Mathematics and Informatics, now the Institute of Data Science and Digital Technologies of Vilnius University. The Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea, which gained enthusiastic acceptance from both the Lithuanian and international scientific communities. This year’s conference features 84 presentations, with 137 registered participants from 11 countries. The conference serves as a gathering point for researchers from six Lithuanian universities, making it the main annual meeting for Lithuanian computer scientists. The primary aim of the conference is to showcase research conducted at Lithuanian and foreign universities in the fields of data science and software engineering. The annual organization of the conference facilitates the rapid exchange of new ideas within the scientific community. Seven IT companies supported the conference this year, indicating the relevance of the conference topics to the business sector. In addition, the conference is supported by the Lithuanian Research Council and the National Science and Technology Council (Taiwan, R. O. C.). The conference covers a wide range of topics, including Applied Mathematics, Artificial Intelligence, Big Data, Bioinformatics, Blockchain Technologies, Business Rules, Software Engineering, Cybersecurity, Data Science, Deep Learning, High-Performance Computing, Data Visualization, Machine Learning, Medical Informatics, Modelling Educational Data, Ontological Engineering, Optimization, Quantum Computing, Signal Processing. This book provides an overview of all presentations from the DAMSS-2023 conference

A Survey of Intelligent Network Slicing Management for Industrial IoT: Integrated Approaches for Smart Transportation, Smart Energy, and Smart Factory

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordNetwork slicing has been widely agreed as a promising technique to accommodate diverse services for the Industrial Internet of Things (IIoT). Smart transportation, smart energy, and smart factory/manufacturing are the three key services to form the backbone of IIoT. Network slicing management is of paramount importance in the face of IIoT services with diversified requirements. It is important to have a comprehensive survey on intelligent network slicing management to provide guidance for future research in this field. In this paper, we provide a thorough investigation and analysis of network slicing management in its general use cases as well as specific IIoT services including smart transportation, smart energy and smart factory, and highlight the advantages and drawbacks across many existing works/surveys and this current survey in terms of a set of important criteria. In addition, we present an architecture for intelligent network slicing management for IIoT focusing on the above three IIoT services. For each service, we provide a detailed analysis of the application requirements and network slicing architecture, as well as the associated enabling technologies. Further, we present a deep understanding of network slicing orchestration and management for each service, in terms of orchestration architecture, AI-assisted management and operation, edge computing empowered network slicing, reliability, and security. For the presented architecture for intelligent network slicing management and its application in each IIoT service, we identify the corresponding key challenges and open issues that can guide future research. To facilitate the understanding of the implementation, we provide a case study of the intelligent network slicing management for integrated smart transportation, smart energy, and smart factory. Some lessons learnt include: 1) For smart transportation, it is necessary to explicitly identify service function chains (SFCs) for specific applications along with the orchestration of underlying VNFs/PNFs for supporting such SFCs; 2) For smart energy, it is crucial to guarantee both ultra-low latency and extremely high reliability; 3) For smart factory, resource management across heterogeneous network domains is of paramount importance. We hope that this survey is useful for both researchers and engineers on the innovation and deployment of intelligent network slicing management for IIoT.Engineering and Physical Sciences Research Council (EPSRC)Singapore University of Technology and Design (SUTD)Hong Kong RGC Research Impact Fund (RIF)National Natural Science Foundation of ChinaShenzhen Science and Technology Innovation Commissio

Digital supply chain transformation: emerging technologies for sustainable growth

This book shows how organisations can leverage emerging digital technologies to achieve operational effectiveness, build new capabilities, and develop innovative business models to underpin transformation in their supply chains. The contributing authors provide deep insights not only on how these emerging technologies work, but also how to use them to create value for multiple stakeholders and deliver sustainable supply chain outcomes. The book will be of great value to practitioners, students and academics who want to learn about state-of-the-art digital developments in the supply chain field. It helps readers to appreciate how various digital paradigms and tools can be used to create innovative products and services with supply chain viability. It also develops the reader’s critical ability to assess the range of technological solutions used to address contemporary supply chain issues and problems

Towards Data Sharing across Decentralized and Federated IoT Data Analytics Platforms

In the past decade the Internet-of-Things concept has overwhelmingly entered all of the fields where data are produced and processed, thus, resulting in a plethora of IoT platforms, typically cloud-based, that centralize data and services management. In this scenario, the development of IoT services in domains such as smart cities, smart industry, e-health, automotive, are possible only for the owner of the IoT deployments or for ad-hoc business one-to-one collaboration agreements. The realization of "smarter" IoT services or even services that are not viable today envisions a complete data sharing with the usage of multiple data sources from multiple parties and the interconnection with other IoT services. In this context, this work studies several aspects of data sharing focusing on Internet-of-Things. We work towards the hyperconnection of IoT services to analyze data that goes beyond the boundaries of a single IoT system. This thesis presents a data analytics platform that: i) treats data analytics processes as services and decouples their management from the data analytics development; ii) decentralizes the data management and the execution of data analytics services between fog, edge and cloud; iii) federates peers of data analytics platforms managed by multiple parties allowing the design to scale into federation of federations; iv) encompasses intelligent handling of security and data usage control across the federation of decentralized platforms instances to reduce data and service management complexity. The proposed solution is experimentally evaluated in terms of performances and validated against use cases. Further, this work adopts and extends available standards and open sources, after an analysis of their capabilities, fostering an easier acceptance of the proposed framework. We also report efforts to initiate an IoT services ecosystem among 27 cities in Europe and Korea based on a novel methodology. We believe that this thesis open a viable path towards a hyperconnection of IoT data and services, minimizing the human effort to manage it, but leaving the full control of the data and service management to the users' will