LEVERAGING BLOCKCHAIN TECHNOLOGY FOR SLA ENFORCEMENT IN HEALTH CARE CLOUD PARTNERSHIPS

The healthcare industry is rapidly adopting cloud-based solutions to improve operational efficiency and patient outcomes. However, healthcare cloud partnerships often face challenges related to the lack of scalability, trust, and Service Level Agreement (SLA) enforcement, and has a notable impact on consumer care quality. To address this issue, the study proposed leveraging blockchain technology to enhance SLA enforcement by using smart contracts in health care cloud partnerships for small and medium-sized facilities. The research questions were: Q.1 What are the current challenges facing small to medium sized healthcare facilities in enforcing SLAs in cloud partnerships? Q.2 How can BC-based smart contracts helps enhance scalability in cloud computing systems in healthcare SMEs by enforcing Service Level Agreements (SLAs) in a safe and efficient manner? Q.3 What are the factors that affect the implementation of blockchain-based smart contracts for SLA enforcement in healthcare SMEs cloud partnerships? The project utilized case studies to demonstrate the effectiveness of using BC technology based smart contracts to enhance SLA enforcement and improve patient outcomes. The findings and conclusions were as follows: 1. Current challenges facing healthcare SMEs in enforcing SLAs in cloud partnerships: SMEs may lack bargaining power, resources, and technical expertise to effectively negotiate, monitor, and enforce SLAs in cloud partnerships, leading to service disruptions, compliance issues, and financial losses. 2. BC-based smart contracts can enhance the scalability of cloud computing systems in healthcare SMEs by automating SLA execution, ensuring real-time data integrity, transparency, and accountability, reducing fraud, error, and transaction costs, and enabling decentralized trust among stakeholders. 3. Factors affecting the implementation of BC-based smart contracts to better SLA enforcement in healthcare SMEs cloud partnerships: regulatory uncertainty, interoperability, standardization, privacy, security, cost, complexity, governance, and user adoption, and 4. Unique Trends and challenges in the healthcare industry for its data analysis: increasing demand for real-time, patient-centered, personalized, and evidence-based care, generating and integrating large volumes of diverse and complex data from multiple sources, ensuring data quality, privacy, and security, complying with regulations and standards, and fostering collaboration and innovation across stakeholders. MedRec, SimplyVital Health, and Medical Chain demonstrate how BC provides secure data sharing, encryption and access control mechanisms, and promotes interoperability through standard data formats and protocols. Results showed improved scalability, trust, and SLA enforcement with the use of BC technology. Further research in the other domains of this area is recommended. It is required to address broader aspects related to the topic. The areas for further study that emerged from the findings and conclusions of this project include: 1. interoperability,2. trusted monitoring solutions, 3.user experience, 4. privacy and security,5. med tokens, cost and 6. integration with existing BSS and OSS. Keywords: Cloud computing, Blockchain technology, SLA enforcement, Smart Contracts, Healthcare cloud, Blockchain-based SLA enforcement, Smart Healthcare, e-healthcare, Scalability

Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations

Towards the Use of Big Data in Healthcare: a literature review

The interest in new and more advanced technological solutions is paving the way for the diffusion of innovative and revolutionary applications in healthcare organizations. The application of an artificial intelligence system to medical research has the potential to move toward highly advanced e-Health. This analysis aims to explore the main areas of application of big data in healthcare, as well as the restructuring of the technological infrastructure and the integration of traditional data analytical tools and techniques with an elaborate computational technology that is able to enhance and extract useful information for decision-making. We conducted a literature review using the Scopus database over the period 2010-2020. The article selection process involved five steps: the planning and identification of studies, the evaluation of articles, the extraction of results, the summary, and the dissemination of the audit results. We included 93 documents. Our results suggest that effective and patient-centered care cannot disregard the acquisition, management, and analysis of a huge volume and variety of health data. In this way, an immediate and more effective diagnosis could be possible while maximizing healthcare resources. Deriving the benefits associated with digitization and technological innovation, however, requires the restructuring of traditional operational and strategic processes, and the acquisition of new skills

Health Care Equity Through Intelligent Edge Computing and Augmented Reality/Virtual Reality: A Systematic Review

Intellectual capital is a scarce resource in the healthcare industry. Making the most of this resource is the first step toward achieving a completely intelligent healthcare system. However, most existing centralized and deep learning-based systems are unable to adapt to the growing volume of global health records and face application issues. To balance the scarcity of healthcare resources, the emerging trend of IoMT (Internet of Medical Things) and edge computing will be very practical and cost-effective. A full examination of the transformational role of intelligent edge computing in the IoMT era to attain health care equity is offered in this research. Intelligent edge computing-aided distribution and collaborative information management is a possible approach for a long-term digital healthcare system. Furthermore, IEC (Intelligent Edge Computing) encourages digital health data to be processed only at the edge, minimizing the amount of information exchanged with central servers/the internet. This significantly increases the privacy of digital health data. Another critical component of a sustainable healthcare system is affordability in digital healthcare. Affordability in digital healthcare is another key component of a sustainable healthcare system. Despite its importance, it has received little attention due to its complexity. In isolated and rural areas where expensive equipment is unavailable, IEC with AR / VR, also known as edge device shadow, can play a significant role in the inexpensive data collection process. Healthcare equity becomes a reality by combining intelligent edge device shadows and edge computing

A Framework for Integrating Transportation Into Smart Cities

In recent years, economic, environmental, and political forces have quickly given rise to “Smart Cities” -- an array of strategies that can transform transportation in cities. Using a multi-method approach to research and develop a framework for smart cities, this study provides a framework that can be employed to: Understand what a smart city is and how to replicate smart city successes; The role of pilot projects, metrics, and evaluations to test, implement, and replicate strategies; and Understand the role of shared micromobility, big data, and other key issues impacting communities. This research provides recommendations for policy and professional practice as it relates to integrating transportation into smart cities

Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability

Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive and Scalable Communication Network