Cloud Ecosystem Research Articles

Data Guardianship: Safeguarding Compliance in AI/ML Cloud Ecosystems

AI has found widespread application across various sectors, including security, healthcare, finance, and national defense. However, alongside its transformative advancements, there has been an unfortunate trend of malicious exploitation of AI capabilities. Concurrently, the rapid evolution of cloud computing technology has introduced cloud-based AI systems. Regrettably, vulnerabilities inherent in cloud computing infrastructure also pose risks to the security of AI services. We observe that the integrity of training data is pivotal, as any compromise therein directly impacts the efficacy of AI systems. Against this backdrop, we assert the paramount importance of preserving data integrity within AI systems. To address this imperative, we propose a data integrity architecture guided by the National Institute of Standards and Technology (NIST) cyber security framework. Leveraging block chain technology and smart contracts emerges as a fitting solution to tackle integrity challenges, owing to their characteristics of shared and decentralized ledgers. Smart contracts facilitate automated policy enforcement, enable continuous monitoring of data integrity, and mitigate the risk of data tampering.

The cloud sovereignty nexus: How the European Union seeks to reverse strategic dependencies in its digital ecosystem

AbstractHow does the European Union balance the need to migrate data to the cloud with the imperative of reducing its dependence on foreign cloud providers? Cloud computing is a critical technology for the competitiveness of the European Union (EU) in the digital economy. This paper argues that the EU is adopting a host of regulatory requirements and industrial policy tools—which fall under the umbrella term of ‘data sovereignty’—not only to protect the confidentiality of European data, but also to counter the dominance of US vendors in the European cloud market. To demonstrate how data sovereignty principles are woven into current EU policy initiatives, the paper presents two case studies: Gaia‐X and the European Alliance for Industrial Data, Edge, and Cloud. Linking data sovereignty to the lack of a competitive European cloud ecosystem sheds light on the strategic dimension of cloud computing in a way that treating them separately would not do.

NEIM-07 HARNESSING END-TO-END CLOUD-BASED WORKFLOWS AND MULTI-MODAL DATA ANALYTICS IN PEDIATRIC BRAIN TUMOR IMAGING AT THE CHILDREN’S BRAIN TUMOR NETWORK (CBTN)

Abstract Neuroimaging is an essential component of the standard of care in pediatric brain tumor patients. Developing efficient and agile workflows for image processing and integration of imaging with other data types to perform predictive analytics is an increasing need. At CBTN, we developed user-friendly workflows to support the entire imaging data lifecycle and to bridge access to multi-modal datasets with scalable analytics, to empower researchers to make breakthrough discoveries that will advance patient care. An automated deep learning-based brain extraction and tumor subregion segmentation model based on a multi-institutional dataset was developed that can reliably segment the treatment-naïve MRI scans of children across a variety of brain tumor histologies. This model was subsequently integrated into an end-to-end imaging pipeline for collecting, managing and analyzing clinically acquired radiology exams of the CBTN consortium with processing of 19,975 exams (1,443 subjects) to date. Critically, the constituent software tools enable the preparation of images for downstream AI analytics, from acquisition to feature extraction. A public website was also created to allow users to search and explore the dataset based on sequence labels, and clinical and imaging attributes. In addition, we established a scalable workflow and processes for de-identification, ingestion, quality control, and management of digital pathology slides for CBTN (over 8,000 slides to date). Finally, we evaluated the feasibility of interoperability between imaging (Flywheel) and molecular (CAVATICA) platforms deployed in cloud ecosystems which can allow streamlined, user-friendly workflows to ingest and harmonize files, perform cohort selection, prepare data using standard processing packages and cloud resources, and conduct analysis on extracted multi-modal feature sets. The described workflow lays the foundation for the broad use of imaging studies and access to multi-modal datasets and analytics, with the goal of empowering researchers to make breakthroughs in patient care.

Cloud Services Monitoring: Implementing a Stratified Approach for Enhanced Visibility and Efficiency

- The research paper introduces a novel stratified approach to monitor cloud services, addressing the unique challenges arising from the distributed and dynamic nature of cloud environments. The main objectives of the study are to enhance the effectiveness of cloud service monitoring and management while ensuring the availability, performance, and security of cloud- based applications. The proposed stratified monitoring framework consists of four distinct layers: Infrastructure, Platform, Application, and Security. Each layer focuses on specific monitoring aspects, encompassing infrastructure resources, platform services, application performance, and security events. Methodologically, the paper explores various monitoring tools and techniques suitable for each layer to provide a comprehensive view of the cloud ecosystem. The research includes an empirical case study to demonstrate the practicality and effectiveness of the stratified monitoring approach in real-world cloud deployments. A comparison with traditional monitoring methods is conducted to showcase the advantages of the proposed approach. The obtained results indicate that the stratified monitoring approach significantly improves visibility into the cloud infrastructure, enables early detection of anomalies, and enhances security measures. This approach empowers cloud service providers and users to optimize resource utilization and make informed decisions concerning cloud performance and security. The research paper's significance lies in offering a structured and adaptable framework to monitor cloud services effectively, thus supporting organizations in maintaining reliable and efficient cloud-based applications. The proposed approach fosters a deeper understanding of the cloud ecosystem, mitigates risks, and facilitates prompt issue resolution. Key Words: Cloud computing, Cloud services, Monitoring, Stratified approach, Infrastructure, Platform, Application, Security.

A Behaviour Study on Cloud Eco-System: Data Security Perspective

Cloud eco-system is a revolution now, which modifies the way in which the IT-based services are being delivered to end customers. It is increasing or we can also say grown-up technology that delivers multiple benefits whether in terms of economics or in terms of cost-effective resource utilization. The ability to install and improve their services on that platform is made possible by the advent of cloud computing, which opens up new options for long-term solutions. Cloud computing's environmental and economic impacts must be considered while assessing its long-term viability. A growing number of organizations, businesses, and personal users are depending on services supplied by the cloud and keeping crucial information in the cloud because of its easy-to-use characteristics. The cloud, despite its widespread use, nevertheless has a number of drawbacks when it comes to data security. Customers are concerned about how their personal information is transported to and from the cloud. Research articles in this topic have been thoroughly analyzed and examined in this report.

Delay and Price Differentiation in Cloud Computing: A Service Model, Supporting Architectures, and Performance

Many cloud service providers (CSPs) offer an on-demand service with a small delay. Motivated by the reality of cloud ecosystems, we study non-interruptible services and consider a differentiated service model to complement the existing market by offering multiple service level agreements (SLAs) to satisfy users with different delay tolerance. The model itself is incentive compatible by construction. Two typical architectures are considered to fulfill SLAs: (i) non-preemptive priority queues and (ii) multiple independent groups of servers. We leverage queueing theory to establish guidelines for the resultant market: (a) Under the first architecture, the service model can only improve the revenue marginally over the pure on-demand service model and (b) under the second architecture, we give a closed-form expression of the revenue improvement when a CSP offers two SLAs and derive a condition under which the market is viable. Additionally, under the second architecture, we give an exhaustive search procedure to find the optimal SLA delays and prices when a CSP generally offers multiple SLAs. Numerical results show that the achieved revenue improvement can be significant even if two SLAs are offered. Our results can help CSPs design optimal delay-differentiated services and choose appropriate serving architectures.

IMG-03. HARNESSING COMPUTATIONAL IMAGING, SCALABLE CLOUD-BASED WORKFLOWS, AND MULTI-MODAL DATA ANALYTICS WITH INTEROPERABLE SOFTWARE PLATFORMS IN PEDIATRIC NEURO-ONCOLOGY; THE ROAD AHEAD FOR CHILDREN’S BRAIN TUMOR NETWORK (CBTN)

Abstract Neuroimaging is acquired as part of the clinical standard of care for treatment of brain tumor patients. Capitalizing on this data and integration of imaging with other data types to perform predictive analytics is an increasing need. Here, we review the efforts at CBTN in the past couple of years to establish user-friendly workflows to support the entire imaging data lifecycle and to bridge access to multi-modal datasets with scalable analytics scale, to empower researchers to make breakthrough discoveries that will advance patient care. An automated deep learning-based brain extraction and tumor subregion segmentation model based on a multi-institutional dataset was developed that can reliably segment the MRI scans of children across a variety of brain tumor histologies. This model was subsequently integrated into an end-to-end imaging pipeline for collecting, managing, and analyzing clinically acquired radiology exams of the CBTN consortium with processing of 12,500 exams to date. A public website was also created to allow users to search and explore the dataset based on sequence labels, and clinical and imaging attributes. In addition, we established a scalable workflow and processes for de-identification, ingestion, quality control, and management of digital pathology slides on CBTN (over 8,000 slides to date). Finally, we established interoperability between imaging (Flywheel) and molecular (CAVATICA) platforms deployed in cloud ecosystems which allows streamlined, user-friendly workflows to ingest and harmonize files, perform cohort selection, prepare data using standard processing packages and cloud resources, and conduct analysis on extracted multi-modal feature sets. The described workflow lays the foundation for broad use of imaging studies and access to multi-modal datasets and analytics, with the goal of empowering researchers to make breakthroughs in patient care. The next step is to apply these tools in the context of clinical trials.

Development of chlorophyll-a concentration estimation algorithm for turbid productive inland waters in India

Estimation of Chlorophyll-a (Chl-a) concentration from remote-sensing reflectance derived from satellite imagery is highly effective in monitoring the spatial and temporal optical variations in water bodies. In turbid, productive and inland waters, implementation of traditional Chl-a estimation algorithms using the blue and green wavelength bands results in higher error in estimated Chl-a. To minimize the absorption and scattering effects of other water constituents like colored dissolved organic matter and particulate matter, the wavelengths bands in red and Near Infrared (NIR) are generally used for estimating Chl-a concentration in productive waters. Owing to a widely observed optical variability in natural waters, a single universal algorithm is not applicable to all waterbodies, hence, regional tuning of algorithms is necessary for estimating Chl-a in turbid and inland productive waters. Here, a 2-band based algorithm of the form and a 3-band algorithm of form were tuned for Chl-a estimation based on field measurements carried out in three water bodies, Chilika Lake of Orissa, Ujjani Reservoir of Maharashtra, Vallabh Sagar Reservoir of Gujarat, India, wherein the observed Chl-a concentration range is 5–33 mg/m3. Together with measured in situ reflectance spectra and concurrent Chl-a concentrations, tuning of red-NIR algorithms is performed. The optimal wavelengths to estimate Chl-a found by the proximal sensing method are = 691 nm and = 667 nm in case of 2-band red-NIR type algorithm. For 3-band red-NIR algorithm, the optimal wavelengths are = 670 nm, = 696 nm and = 740 nm. The mean absolute percentage error (MAPE) and root-mean-square error (RMSE) values obtained for the estimated Chl-a using the tuned 2-band red-NIR algorithm are 29.9% and 5.35 respectively. Similarly, RMSE and MAPE values for Chl-a concentration estimated using the 3-band tuned red-NIR algorithm are 4.24 and 22.5%, respectively. The tuned 2-band and 3-band algorithms resulted in better Chl-a estimates than the fourteen blue-green and red-NIR algorithms from previous studies. The 2-band and 3-band algorithms were re-calibrated to wavelengths present in the ocean land color imager (OLCI) sensor on Sentinel-3 satellite to monitor these water bodies. Spatio-temporal analysis of OLCI imagery derived Chl-a concentrations over Chilika lake (2019 and 2020) indicated seasonal variations. The tuned 3-band red-NIR algorithm exhibited robust performance in all seasons despite wide variations in colored dissolved organic matter and suspended sediments. The tuned red-NIR algorithms are useful in monitoring inland turbid productive waters with upcoming missions such as Plankton, Aerosol, Cloud Ecosystem (PACE) and Oceansat-3.

Practical Exploration of Innovation and Entrepreneurship Education in Colleges and Universities of Guangdong-Hong Kong-Macau Greater Bay Area under “Internet+”

The Guangdong-Hong Kong-Macau Greater Bay Area (GBA) has become a vital part of the national development strategy, where innovation and entrepreneurship play pivotal roles in its progress. In order to make meaningful contributions to the GBA’s advancement, regional colleges and universities must leverage the Internet’s advantages to drive educational and teaching reforms. Adopting an “Internet+” perspective, this research investigates the prospects and challenges encountered by GBA higher education institutions in the realm of innovation and entrepreneurship education. By drawing inspiration from successful international practices, it has proposed practical models encompassing the creation of online course platforms, the establishment of resource sharing systems, and the optimization of the innovation and entrepreneurial cloud ecosystem.

Quantum bionic advantage on near-term cloud ecosystem

The race to conquer the quantum information domain has accelerated the pace of research and innovation toward developing radical new approaches for quantum-based technologies. Quantum computing, communication, sensing combinatorial optimization, and quantum machine learning are among the most competitive and dynamic research and development areas currently on the verge of unlocking practical new quantum capabilities that will have a profound effect on almost every aspect of our lives. This paper presents the advantages of superconducting qubits and a summary of the various archetypes that show promise for the long-term goal of constructing larger-scale fault-tolerant quantum computers. The paper presents the implementation of the relevant NISQ era algorithms for near-term integrated hybrid cloud-based quantum computing systems, including the advances and implications of these algorithms which make them particularly suitable for quantum machine learning, and combinatorial optimization. In addition, this paper explores the practical realities of both quantum sensing for ultrasensitive measurements and quantum communications to provide unparalleled security, data rates, and efficiency.

A combined computing framework for load balancing in multi-tenant cloud eco-system

<span>Since the world is getting digitalized, cloud computing has become a core part of it. Massive data on a daily basis is processed, stored, and transferred over the internet. Cloud computing has become quite popular because of its superlative quality and enhanced capability to improvise data management, offering better computing resources and data to its user bases (UBs). However, there are many issues in the existing cloud traffic management approaches and how to manage data during service execution. The study introduces two distinct research models: data center virtualization framework under multi-tenant cloud-ecosystem (DCVF-MT) and collaborative workflow of multi-tenant load balancing (CW-MTLB) with analytical research modeling. The sequence of execution flow considers a set of algorithms for both models that address the core problem of load balancing and resource allocation in the cloud computing (CC) ecosystem. The research outcome illustrates that DCVF-MT, outperforms the one-to-one approach by approximately 24.778% performance improvement in traffic scheduling. It also yields a 40.33% performance improvement in managing cloudlet handling time. Moreover, it attains an overall 8.5133% performance improvement in resource cost optimization, which is significant to ensure the adaptability of the frameworks into futuristic cloud applications where adequate virtualization and resource mapping will be required.</span>

AUTONOMOUS MOBILE ROBOTS’ INTRODUCTION IN SHIPYARDS' MANUFACTURING PROCESS

In the context of an exponential growth of digitalization in the naval production process, the introduction of emerging technologies becomes a key point for the future competitiveness of companies. As a matter of fact, it is fundamental to optimize the process, exploiting newest solutions to improve production tracking, quality control and timesaving. Specifically, autonomous mobile robot technologies (AMR) are becoming increasingly relevant in recent years, thanks to the advances in the scientific community and to the growing number of market options. In this scenario, it was decided to use AMRs to face different problems in the shipyard, such as autonomous inspections and material transport, to provide our employees with a concrete and automated support during the tasks of every day. As a result, our work led to a cloud ecosystem where our workers have the possibility to assign specific tasks to robots, like setting up real stocktaking missions or making requests for missing material, while putting their effort in more worthwhile activities. Humans and different kinds of mobile robots can operate within the same environment without hindering each other, and the autonomous mode can be easily turned into a controlled one. In the following, we are going to present the research and development journey which took us to the deployment of a concrete solution for improving both efficiency and quality of our shipyards, which fits perfectly into the framework of digital innovation of our company.

Cost-Effective Web Application Replication and Deployment in Multi-Cloud Environment

Multi-cloud is becoming a popular cloud ecosystem because it allows enterprise users to share the workload across multiple cloud service providers to achieve high-quality services with lower operation cost and higher application resilience. In multi-cloud, cloud services are widely distributed at different locations with differentiated prices. Therefore, Web application providers face the challenge to select proper cloud services for application replication and deployment with the aim of minimizing the deployment cost. Meanwhile, the deployed application replicas must satisfy the constraint on request response time to maintain the quality of user experience. To meet the two major requirements, this article studies a new problem of Web application replication and deployment in multi-cloud (WARDMC) that jointly considers both the cost minimization and constraints on average response time, including particularly request processing time and network latency. To address the problem, we develop a new approach named MCApp. MCApp combines iterative mixed integer linear programming with domain-tailored large neighborhood search to optimize both application replicas deployment and user requests dispatching. Extensive experiments using the real-world datasets demonstrate that MCApp significantly outperforms several recently proposed approaches.

Correction to: Priority-MECE: A Mobile Edge Cloud Ecosystem Based on Priority Tasks Offloading

Correction to: Priority-MECE: A Mobile Edge Cloud Ecosystem Based on Priority Tasks Offloading

Fog Computing

Managing the knowledge generated by the Internet of Things (IoT) sensors and actuators is one of the most important. Challenges in introducing IoT systems. Traditional cloud-based IoT systems We challenged the large scale, non- uniformity, and high latency observed in some cloud ecosystems. One solution is to decentralize applications, management, and data analytics within the network itself. Use of distributed and integrated computing models. This approach is called the nebula Calculate. This document introduces a conceptual model of fog and mist computing. They refer to the cloud-based computing model of the IoT. This document shows further features Key characteristics and aspects of fog computing, including service models and deployments It provides a baseline of what strategy and fog computing are, and how to use them.

Cloud Functions and Serverless Computing

Abstract: Cloud Function in the simplest words is a Function-as-a-Service (FaaS). FaaS is actually a family of the serverless computing category. “Serverless” means that the user can focus on its application logic without dealing with infrastructure at all. Painless development, deployment, and maintenance of a web API is still not a turn-key solution, although modern web application frameworkshave improved dramatically in the last few years. Serverless is without any doubt a game-changer. The event-driven approach combined with a scalable and robust cloud ecosystem offered by the maintop cloud vendors opens endless opportunities. Cloud Functions is Google Cloud’s event-driven serverless compute platform. FaaS is a real NoOpstechnology, it completely abstracts away servers. Cloud Functions are developed with the sole purpose to build event-driven architectures. These can react to events like file changes in storage, messages in the queue, or any HTTP request. Index Terms: Cloud Computing, Serverless Technology, Function as a Service, LambdaFunctions and Functions

Intensify Cloud Security and Privacy Against Phishing Attacks

The world of computation has shifted from centralized (client-server, not web-based) to distributed systems during the last three decades. We are now reverting to virtual centralization, i.e., Cloud Computing (CC). In the world of computation, the location of data and processes makes all the difference. A person has complete control over the data and operations in their computer. On the other side, CC involves a vendor providing service and data upkeep. At the same time, the client/customer is ignorant of where the processes are operating or where the data is kept. As a result, the client does not influence it and doesn't have the right to do it. When it comes to data security in cloud computing, the vendor must guarantee service level agreements (SLAs) to persuade the client. As a result, the SLA must define several degrees of security and their complexity depending on the benefits for the client to comprehend the security rules in place. Phishing is a social engineering assault that is frequently used to obtain user information, such as login passwords and credit card details. It happens when an attacker poses as a trustworthy entity and tricks the victim into opening an e-mail, instant message, or text message. In this research paper, the methodology that tries to identify the phishing attack in the cloud eco-system has been explored and mentioned. The approach used here classifies the malicious and non-malicious URLs.

A Secure Two-Factor Authentication Framework in Cloud Computing

Cloud computing technology has brought tremendous evaluation in the arena of IT (information technology). This technology paves the path of starting business with lowest investment by availing infrastructure as a service (IAAS), platform as a service (PAAS) and software as a service (SAAS) pay per uses model. Cloud computing services can be quickly and easily provisioned and discharged with minimum efforts and service provider (SP) relationship. Cloud computing characteristics such as on demand self-service, broad network access, resource pooling, and rapid elasticity lead the demand of computing. Despite these features, this platform is free to security issues and attacks specifically in terms of communication because of unsecure authentication and privacy. However, strong user authentication procedure impedes illegal access to the SP which is the principal requirement for securing cloud computing ecosystem. In this regard, we attempt to propose possible counter measures for the cloud ecosystem. Hence, this paper presented a novel one way hash and nonce-based two-factor secure authentication scheme with traditional user IDs, password, and OTP verification procedure that resist brute force attack, session and account hijacking attack, MITM attacks, and replay attacks.

Blueprint for Security: Designing Secure Cloud Architectures

The research paper delves into the imperative of robust security frameworks within cloud computing environments, addressing the prevalent challenges and vulnerabilities that accompany the cloud's expansive utilization across industries. This study underscores the necessity of foundational security principles, such as least privilege, role-based access control, and comprehensive data encryption, aiming to fortify cloud infrastructures against escalating cyber threats. The paper provides a thorough analysis of contemporary security practices, including network segmentation, the deployment of intrusion detection systems, and rigorous compliance protocols, to ensure resilient and secure cloud operations. Additionally, it evaluates cutting-edge tools and technologies, like automated security solutions and AI-driven threat detection systems, which are pivotal in enhancing security postures and mitigating risks in dynamic cloud ecosystems. Through a series of case studies, the paper highlights successful implementations and extracts critical lessons from historical security breaches, offering actionable insights and strategies to design inherently secure cloud architectures. This study not only synthesizes complex security requirements into actionable architecture blueprints but also anticipates future challenges and technological advancements, making it a vital resource for professionals and organizations aiming to leverage cloud computing securely and efficiently.

Cloud-IoT Integration: Cloud Service Framework for M2M Communication

With the ongoing revolution in the Internet of Things (IoT) and cloud computing has made the potential of every stack holder that is connected through the Internet, to exchange and transfer data. Various users perceive this connection and interaction with devices as very helpful and serviceable in their daily life. However, an improperly configured network system is a soft target to security threats, therefore there is a dire need for a security embedded framework for IoT and cloud communication models is the latest research area. In this paper, different IoT and cloud computing frameworks are discussed in detail and describes the importance of the daily life of people. The main focus is to design the Cloud-IoT integration that is used to implement IoT and Cloud Framework for M2M communication, also building a relationship between different devices to connect through a cloud and also find different security methods to secure those devices. Extensive papers finding and results showed different ways they have been introduced to manipulate M2M in the digital field of health care and the virtual world. While focusing on the methodology used in M2M it is also imperative to concentrate on security levels from different inside and outside attacks on IoT and cloud ecosystem. There is a need to create a strong and secure connection between all of our IoT devices with a cloud so that there should be a fixed and safe connection between cloud environments concerning M2M connection between all wired and wireless devices. Meanwhile in contemplation of security mode also conducive to maintain M2M connection between IoT devices and Cloud and in which areas these methodologies have been implemented.