Abstract: Cloud native technologies have completely changed how people create, develop, and deploy their software, and it has given organizations the ability to rapidly innovate using containerization, micro services, and automated Continuous Integration (CI)/Continuous Deployment (CD) pipelines. These new features of cloud native technologies have also greatly increased the attack surface of the software supply chain, allowing attackers to use sophisticated methods to exploit software including malicious dependencies, exploited build pipelines, insider attacks, zero-day vulnerability exploits, and potentially soon, AI driven attacks. Traditional mechanisms for protecting the software supply chain were based on static analysis and perimeter-based controls that did not protect against the rapid changing nature of cloud-native applications. The purpose of this document is to present a cloud-native security architecture to improve the resilience of software supply chains to both AI and non-AI attacks. This architecture includes secure, and isolated build environments, artifact integrity validation, container and runtime protection, and continuous monitoring within a defense in depth architecture aligned with current orchestration platforms. Additionally, architecture will include AI assisted detection and trust evaluation to detect anomalous behavior, zero-day exploit patterns, and integrity violations across all phases of the software development lifecycle. This approach provides the security controls needed for software supply chain assurance by integrating them directly into cloud-native primitives and DevSecOps workflows, therefore minimizing operational friction, and maximizing end-to-end supply chain assurance. The conclusion of this document will discuss the trade-offs between various architectures, and consider deployment options, and provide potential avenues for future research for securing software supply chains in an increasing number of automated and AI enabled cloud environments.

Abstract: The Space-Air-Ground Integrated Network (SAGIN) has emerged as a promising architecture to support diverse and wide-area communications. In this context, a hybrid Radio Frequency/Free Space Optics (RF/FSO) approach offers a practical solution by utilizing FSO links as primary channels due to their high throughput, and RF links as secondary channels, given their tolerance to cloud-induced attenuation. As 5G and beyond networks demand increasingly diverse Quality of Service (QoS) requirements, network slicing has gained significant attention for its ability to isolate traffic flows and ensure end-to-end QoS guarantees. However, its application in hybrid RF/FSO-enabled SAGINs remains relatively unexplored, particularly in the context of dynamic channel conditions and moving non-terrestrial nodes. In this work, we propose a deep learning-assisted network slicing framework that dynamically coordinates RF and FSO links by leveraging predicted link attenuations. Simulation results indicate that integrating deep learning leads to optimal use of FSO links, resulting in a higher acceptance of services compared to conventional slicing approaches. Moreover, it demonstrates more stable connections and greater robustness in dynamic cloud environments, particularly under increased reslicing delays.

Abstract: The Managed Cloud Services Market is experiencing significant growth, driven by enterprises’ increasing reliance on cloud infrastructure and the need for expert management of complex IT environments. This market encompasses a wide range of services, including migration, security, maintenance, and performance optimization, across public, private, and hybrid clouds. Key drivers include digital transformation initiatives, scalability demands, cost optimization, and the rising adoption of data-driven capabilities. Despite challenges such as cybersecurity concerns, opportunities abound in emerging markets and industry verticals, with leading providers continuously innovating to meet evolving enterprise needs. The report offers a detailed analysis of market dynamics, segmentation, geographic trends, and competitive landscape, highlighting the factors shaping the present and future of managed cloud services. Access Full Report- https://www.nextmsc.com/report/managed-cloud-services-market

Abstract: Cloud native technologies have completely changed how people create, develop, and deploy their software, and it has given organizations the ability to rapidly innovate using containerization, micro services, and automated Continuous Integration (CI)/Continuous Deployment (CD) pipelines. These new features of cloud native technologies have also greatly increased the attack surface of the software supply chain, allowing attackers to use sophisticated methods to exploit software including malicious dependencies, exploited build pipelines, insider attacks, zero-day vulnerability exploits, and potentially soon, AI driven attacks. Traditional mechanisms for protecting the software supply chain were based on static analysis and perimeter-based controls that did not protect against the rapid changing nature of cloud-native applications. The purpose of this document is to present a cloud-native security architecture to improve the resilience of software supply chains to both AI and non-AI attacks. This architecture includes secure, and isolated build environments, artifact integrity validation, container and runtime protection, and continuous monitoring within a defense in depth architecture aligned with current orchestration platforms. Additionally, architecture will include AI assisted detection and trust evaluation to detect anomalous behavior, zero-day exploit patterns, and integrity violations across all phases of the software development lifecycle. This approach provides the security controls needed for software supply chain assurance by integrating them directly into cloud-native primitives and DevSecOps workflows, therefore minimizing operational friction, and maximizing end-to-end supply chain assurance. The conclusion of this document will discuss the trade-offs between various architectures, and consider deployment options, and provide potential avenues for future research for securing software supply chains in an increasing number of automated and AI enabled cloud environments.

Abstract: Securing data in transit across distributed cloud environments requires protection that extends beyond traditional TLS. Modern microservices, hybrid networks, and API-driven architectures face threats such as token replay, lateral movement, and payload tampering that channel encryption alone cannot prevent. The Data-in-Transit Defender Architecture (DITDA)—a Zero Trust–aligned model embeds security directly within the message using JSON Web Encryption (JWE). DITDA applies identity-bound claims, audience constraints, and layered payload encryption to ensure messages remain confidential, tamper-resistant, and verifiable across untrusted networks. The architecture integrates with API gateways, service meshes, and telemetry pipelines. Results demonstrate improved resilience, reduced attack surface, and enhanced compliance for enterprise-scale digital service delivery.

Abstract: In 2026, Pikpak continues to revolutionize the way we store, manage, and access our digital files with its powerful cloud storage solutions. Whether you are an individual user looking for seamless file access across devices, a creative professional managing large media libraries, or a business in need of secure and scalable storage, Pikpak has something for everyone. Right now, new users can take advantage of the Pikpak Invitation Code 2026 "39002515", which offers 10% off on all plans, making this the perfect moment to upgrade your cloud storage experience with extra savings. Using the Pikpak Invitation Code 2026 "39002515" is a simple and effective way to enjoy premium cloud features at a lower cost. In this comprehensive article, we’ll dive into everything you need to know about Pikpak in 2026 — from its core features and plan structures to tips on maximizing your productivity and security with cloud storage. Whether you’re new to Pikpak or considering an upgrade, this guide will help you make the most informed decision. What Is Pikpak? Pikpak is a modern cloud storage service designed to make file storage and management as easy and efficient as possible. With intuitive interfaces on both mobile and desktop platforms, Pikpak allows users to upload, organize, share, and backup files securely in the cloud. It supports a wide variety of file types — including documents, photos, videos, and more — making it a versatile solution for personal, educational, and professional use. What sets Pikpak apart from many other cloud services is its focus on speed, reliability, and user-friendly design, combined with powerful features such as large storage capacities, collaboration tools, and advanced sharing options. The Pikpak Invitation Code 2026 "39002515" enhances this value proposition by unlocking a 10% discount across all subscription plans, giving you more storage at a lower price. Why Use Cloud Storage in 2026? In today’s digital age, cloud storage has become essential. From backing up irreplaceable memories to accessing work files on the go, cloud solutions provide convenience and peace of mind. Here are some compelling reasons why cloud storage — and specifically Pikpak — is indispensable in 2026: 1. Access Files Anywhere, Anytime With cloud storage, your files are no longer tied to a single device. Whether you’re at home, traveling, or working remotely, Pikpak ensures your data is just a few clicks away across all your devices. 2. Protect Against Data Loss Hard drive failures, accidental deletions, and device thefts are common risks. By storing your data in the cloud, you create a secure backup that protects against unexpected loss. 3. Simplify Collaboration Cloud storage makes it easy to share files with others, work on collaborative projects, and provide access without sending bulky attachments via email. 4. 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Business and Team Plans For teams and businesses, Pikpak provides scalable plans that include: Shared storage across team members Administrative controls Collaborative workspace features Integration support with third-party services The 10% discount with the invitation code also applies to business plans, helping teams reduce costs while maintaining powerful cloud storage capabilities. Key Features of Pikpak in 2026 Pikpak offers an impressive suite of features designed to simplify your digital life, improve productivity, and protect your data. Here are the most noteworthy features available in 2026: 1. Cross-Platform Syncing Pikpak seamlessly syncs your files across multiple devices — including smartphones, tablets, and computers — ensuring that your data is always up to date no matter where you access it from. 2. Smart File Management Gone are the days of manually organizing folders. Pikpak’s intelligent system helps you categorize and locate files quickly with advanced search tools and automatic sorting options. 3. Secure Sharing Sharing files with others has never been easier or safer. With link sharing, password protection, and customizable permissions, you remain in control of who accesses your data. 4. Offline Access Need to work while offline? Pikpak allows you to mark files for offline access so you can view and edit documents without internet connectivity. 5. Automatic Backup Never worry about forgetting to save your newest photos or important documents again. Pikpak automatically backs up selected folders from your device. 6. Large File Support From high-resolution videos to massive project files, Pikpak handles large uploads and downloads with ease, making it ideal for creators, professionals, and media-heavy workflows. 7. Enhanced Security With encryption at rest and in transit, two-factor authentication, and secure access controls, Pikpak prioritizes the safety and privacy of your data. How to Redeem the Pikpak Invitation Code 2026 "39002515" Using the Pikpak Invitation Code 2026 "39002515" is quick and straightforward. Follow these steps to unlock your 10% discount: Step 1: Create or Log In to Your Pikpak Account If you’re new to Pikpak, sign up with your email address or preferred login method. Existing users can simply log in to their account. Step 2: Choose a Plan Navigate to the subscription section and select the plan that fits your storage and feature needs — whether it’s an individual premium plan or a business plan. Step 3: Enter the Invitation Code During checkout, look for the promo or invitation code field. Enter 39002515 exactly as shown. Step 4: Apply the Code and Complete Payment Once the code is entered, you’ll see your total updated with a 10% discount. Complete the payment process using your preferred method. Step 5: Enjoy Your Upgraded Storage You’re now ready to enjoy enhanced cloud storage with extra space, features, and peace of mind — all at a better price thanks to the Pikpak invitation code. Benefits of Applying the Invitation Code That extra 10% off might seem modest compared to some offers, but in 2026, every bit of savings counts — especially when you’re investing in tools you’ll use every day. Here are how you benefit: Reduce Long-Term Costs Cloud storage is often an ongoing need. That’s why saving on your subscription now means long-term financial benefits, especially if you opt for annual plans. Get More Value for Premium Features Premium plans come with advanced options such as enhanced security, collaboration tools, and larger storage — all at a discount with the invitation code. 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Abstract: This survey paper explores the secure management of cloud-based electronic health records (EHRs) through centralized models, focusing on data security, access control mechanisms and algorithmic performance. It examines various encryption techniques and access strategies designed to protect sensitive health data against unauthorized access and breaches. The study also evaluates the efficiency and reliability of current security algorithms within cloud environments. This work focuses on developing robust and secure cloud solutions to effectively manage healthcare records. It addresses key challenges and outlines future research directions aimed at enhancing privacy, compliance, and performance within cloud-based healthcare systems.

Abstract: CloudnetPy is Python software designed for producing vertical profiles of cloud properties from ground-based remote sensing measurements.

Abstract: viral-pipelines A set of scripts and tools for the analysis of viral NGS data. Workflows are written in WDL format. This is a portable workflow language that allows for easy execution on a wide variety of platforms: on individual machines (using miniWDL or Cromwell to execute) on commercial cloud platforms like GCP, AWS, or Azure (using Cromwell or CromwellOnAzure) on institutional HPC systems (using Cromwell) on commercial platform as a service vendors (like DNAnexus) on academic cloud platforms (like Terra) Obtaining the latest WDL workflows Workflows from this repository are continuously deployed to Dockstore, a GA4GH Tool Registry Service. They can then be easily imported to any bioinformatic compute platform that utilizes the TRS API and understands WDL (this includes Terra, DNAnexus, DNAstack, etc). Workflows are also available in the Terra featured workspace. Workflows are continuously deployed to a DNAnexus CI project. Basic execution The easiest way to get started is on a single, Python & Docker-capable machine (your laptop, shared workstation, or virtual machine) using miniWDL as shown above. MiniWDL can be installed either via pip or conda (via conda-forge). After confirming that it works (miniwdl run_self_test, you can use miniwdl run to invoke WDL workflows from this repository. For example, to list the inputs for the assemble_refbased workflow: miniwdl run https://raw.githubusercontent.com/broadinstitute/viral-pipelines/v2.1.8.0/pipes/WDL/workflows/assemble_refbased.wdl This will emit: missing required inputs for assemble_refbased: reads_unmapped_bams, reference_fasta required inputs: Array[File]+ reads_unmapped_bams File reference_fasta optional inputs: outputs: To then execute this workflow on your local machine, invoke it with like this: miniwdl run \ https://raw.githubusercontent.com/broadinstitute/viral-ngs-staging/master/pipes/WDL/workflows/assemble_refbased.wdl \ reads_unmapped_bams=PatientA_library1.bam \ reads_unmapped_bams=PatientA_library2.bam \ reference_fasta=/refs/NC_045512.2.fasta \ trim_coords_bed=/refs/NC_045512.2-artic_primers-3.bed \ sample_name=PatientA In the above example, reads from two sequencing runs are aligned and merged together before consensus calling. The optional bed file provided turns on primer trimming at the given coordinates. Available workflows The workflows provided here are more fully documented at our ReadTheDocs page.

Abstract: The Quantum Computing as a Service (QCaaS) Market is revolutionizing how organizations access advanced computational power by offering cloud-based quantum resources on demand. This emerging model enables enterprises to tackle complex optimization, simulation, and data-intensive challenges without the burden of expensive quantum hardware. Driven by growing cloud adoption, increasing R&D investments, and cross-industry demand, QCaaS is poised to reshape sectors ranging from healthcare and finance to logistics and energy. Despite challenges such as high costs and talent shortages, the market is expanding globally, with North America leading current adoption and Asia-Pacific emerging as a high-growth region. Strategic collaborations, innovative service offerings, and continued technological advancements position QCaaS as a critical enabler of the next generation of enterprise computing. Access Full Report- https://www.nextmsc.com/report/quantum-computing-as-a-service-qcaas-market

Abstract: Modern data-management frameworks promise a flexible and efficient management of data and metadata across storage backends. However, such claims need to be put to a meaningful test in daily practice. We conjecture that such frameworks should be fit to construct a data backend for workflows which use geographically distributed high-performance and cloud computing systems. Cross-site data transfers within such a backend should largely saturate network bandwidth, in particular when parameters such as buffer sizes are optimized. To explore this further, we evaluate the "integrated Rule-Oriented Data System" iRODS with EUDAT's B2SAFE module as data backend for the "Distributed Data Infrastructure" within the LEXIS Platform for complex computing workflow orchestration and distributed data management. The focus of our study is on testing our conjectures-i.e., on construction and assessment of the data infrastructure and on measurements of data-transfer performance over the wide-area network between two selected supercomputing sites connected to LEXIS. We analyze limitations and identify optimization opportunities. Efficient utilization of the available network bandwidth is possible and depends on suitable client configuration and file size. Our work shows that systems such as iRODS nowadays fit the requirements for integration in federated computing infrastructures involving web-based authentication flows with OpenID Connect and rich on-line services. We are continuing to exploit these properties in the EXA4MIND project, where we aim at optimizing data-heavy workflows, integrating various systems for managing structured and unstructured data.

Abstract: Hybrid and multi-cloud environments have been more popular due to the meteoric rise of cloud computing. In these setups, businesses utilize a combination of cloud services from various providers to enhance their adaptability, efficiency, and reliability. Despite these advantages, such environments introduce considerable challenges in interoperability, scalability, and security. Middleware architectures play a central role in addressing these challenges by acting as a unifying layer that ensures seamless integration across heterogeneous platforms, efficient resource orchestration, and secure communication. This study examines a range of middleware approaches, including service-oriented, event-driven, microservices-based, and agent-based architectures, highlighting their design principles and applicability in hybrid and multi-cloud environments. Particular attention is given to scalability techniques such as containerization, dynamic resource provisioning, and AI-driven optimization, which enable adaptive performance under fluctuating workloads. Equally important are the security mechanisms embedded in middleware, including authentication, authorization, data encryption, and integrity verification, which safeguard sensitive information and maintain trust across distributed systems. Through comparative evaluation of existing models, the study demonstrates how middleware not only mitigates technical complexities but also enhances the efficiency, resilience, and security of multi-cloud operations, making it a vital component of modern distributed computing environments. Future research should focus on intelligent middleware architectures powered by AI and ML, enabling automated fault management, predictive scalability, and real-time decision-making. Data processing efficiency and latency can be further enhanced through integration with fog and edge computing.

Abstract: The Open All Photonics Network Market is gaining strong momentum as global communication systems evolve toward fully optical, end-to-end architectures capable of handling unprecedented data volumes with minimal latency and improved energy efficiency. By eliminating repeated optical-electrical-optical conversions, these networks enable deterministic, high-capacity connectivity that aligns with the growing demands of cloud computing, artificial intelligence, and next-generation digital services. Access Full Report-https://www.nextmsc.com/report/open-all-photonics-network-market-ic3790

Abstract: viral-pipelines A set of scripts and tools for the analysis of viral NGS data. Workflows are written in WDL format. This is a portable workflow language that allows for easy execution on a wide variety of platforms: on individual machines (using miniWDL or Cromwell to execute) on commercial cloud platforms like GCP, AWS, or Azure (using Cromwell or CromwellOnAzure) on institutional HPC systems (using Cromwell) on commercial platform as a service vendors (like DNAnexus) on academic cloud platforms (like Terra) Obtaining the latest WDL workflows Workflows from this repository are continuously deployed to Dockstore, a GA4GH Tool Registry Service. They can then be easily imported to any bioinformatic compute platform that utilizes the TRS API and understands WDL (this includes Terra, DNAnexus, DNAstack, etc). Workflows are also available in the Terra featured workspace. Workflows are continuously deployed to a DNAnexus CI project. Basic execution The easiest way to get started is on a single, Python & Docker-capable machine (your laptop, shared workstation, or virtual machine) using miniWDL as shown above. MiniWDL can be installed either via pip or conda (via conda-forge). After confirming that it works (miniwdl run_self_test, you can use miniwdl run to invoke WDL workflows from this repository. For example, to list the inputs for the assemble_refbased workflow: miniwdl run https://raw.githubusercontent.com/broadinstitute/viral-pipelines/v2.1.8.0/pipes/WDL/workflows/assemble_refbased.wdl This will emit: missing required inputs for assemble_refbased: reads_unmapped_bams, reference_fasta required inputs: Array[File]+ reads_unmapped_bams File reference_fasta optional inputs: outputs: To then execute this workflow on your local machine, invoke it with like this: miniwdl run \ https://raw.githubusercontent.com/broadinstitute/viral-ngs-staging/master/pipes/WDL/workflows/assemble_refbased.wdl \ reads_unmapped_bams=PatientA_library1.bam \ reads_unmapped_bams=PatientA_library2.bam \ reference_fasta=/refs/NC_045512.2.fasta \ trim_coords_bed=/refs/NC_045512.2-artic_primers-3.bed \ sample_name=PatientA In the above example, reads from two sequencing runs are aligned and merged together before consensus calling. The optional bed file provided turns on primer trimming at the given coordinates. Available workflows The workflows provided here are more fully documented at our ReadTheDocs page.

Abstract: This paper reviews an easily expandable plan for smart document handling across multiple cloud systems, aiming to make work easier to manage, more resilient to issues, and improve the total cost of ownership. The importance of this task stems from two factors: first, Intelligent Document Processing (IDP) tools are experiencing growth; second, multi-cloud use is expanding more widely. This increases the primary fight between wanting top-notch help for every step and the dangers of being stuck with one provider, having messy operations, and uneven safety rules. The study aims to create and support a complete design that can hide both setup and software links while offering complete control and standard protection in a mixed environment. The innovation is in the coherent four-layer model, which merges a general control plane atop Kubernetes and Crossplane with portable application runtime Dapr, exposing standard APIs for statelessness, messaging, and service invocation, decomposed IDP microservices, and an overlay layer for management and security. The key findings validate that only the combination of Crossplane at the level of the control plane with GitOps and OPA policies together with Dapr at the level of the application-API can provide real portability, elastic scaling, governed security, while maintaining freedom of choice between cloud services. It proves that workflows crossing provider boundaries can be orchestrated, thus reducing vendor lock-in. The article will be helpful to cloud-platform architects, IT executives, data and MLOps engineers, IDP product teams, and researchers in distributed systems and enterprise AI.

Abstract: The widespread adoption of cloud storage services has strengthened challenges associated with data redundancy, excessive storage consumption, and the protection of sensitive information. A considerable share of cloud storage space is consumed by repeated copies of textual data, including documents, reports, emails, and system logs uploaded by multiple users. Although data deduplication is widely recognized as an effective approach for reducing redundancy and improving storage efficiency, conventional deduplication techniques typically rely on plaintext data comparison. This reliance exposes confidential user information to cloud service providers, who may not always be fully trusted. While secure deduplication methods have been proposed to mitigate these risks, many existing solutions remain vulnerable to brute-force attacks, metadata leakage, and scalability limitations, particularly when handling large volumes of textual data. This paper presents DEDUCT, a secure and efficient deduplication framework specifically designed for textual data in cloud environments. The proposed framework allows the cloud server to detect and eliminate duplicated data without gaining access to the original file content. DEDUCT combines text preprocessing, chunk-based fingerprint generation, cryptographic hashing, and secure encryption techniques to preserve data confidentiality while enabling reliable duplicate detection. In addition, a proof-of-ownership mechanism is employed to prevent unauthorized users from exploiting deduplication advantages. The proposed approach effectively balances storage optimization with strong security guarantees by ensuring that only encrypted data and protected metadata are processed by the cloud. Experimental observations indicate that DEDUCT substantially reduces storage redundancy while strengthening resistance to inference, guessing, and confirmation attacks, making it well suited for privacy-aware cloud storage applications.

Abstract: The rapid growth of cloud gaming and game streaming has led to a substantial increase in the volume of game content data. To ensure real-time delivery of cloud game content, a common strategy is to downsample and compress the game content before transmission, reducing both data size and bandwidth requirements. However, this approach presents considerable obstacles for super-resolution (SR) networks at the receiver side. In particular, the degraded quality of compressed video streams, combined with the stringent demand for real-time processing, poses major challenges for practical SR applications. In this paper, we propose a novel real-time super-resolution framework that works directly in the compressed domain by exploiting coding-domain priors. Specifically, we propose an extremely lightweight U-Net architecture that leverages prediction maps and residuals as its primary guidance signals. Furthermore, we incorporate the partition map into a Pixel Adaptive Convolution (PAC) module, allowing the convolution kernels to adapt to different regions in the decoded frame. The resulting deep features are then fused with those from the U-Net backbone through an attention block. Finally, we present an enhanced re-parameterization block designed to better model edge features, leading to notable gains in both the objective metrics and subjective visual quality of the reconstructions. Extensive experiments demonstrate that the proposed method consistently outperforms existing real-time approaches on compressed game video content, achieving superior performance in both quality and efficiency.

Abstract: In this paper, we study multi-resource maximin share fair allocation with time discount in a cloud computing system with heterogeneous servers. It primarily focuses on the allocation of computing resources in cloud computing systems when users are dealing with time-sensitive tasks. If users delay handling these tasks, their utility will decrease over time. In addition, users do not always stay in the computing system in this problem. For this problem, we propose a mechanism called maximin share fairness with time discount (MMS-TD) in a heterogeneous cloud computing system. We prove theoretically that the allocation returned by the mechanism is lexicographically max-min optimal, that the allocation satisfies the maximin share fairness, and that the mechanism is Pareto efficiency, proportionality, and strategy-proofness. In addition, we designed an algorithm to realize this mechanism and conducted simulation experiments with Alibaba cluster traces. The experimental results show that regardless of resource utilization or user utility, the MMS-TD mechanism consistently outperforms the other three similar mechanisms.

Abstract: AI batch jobs such as model training, inference pipelines, and data analytics require substantial GPU resources and often need to finish before a deadline. Spot instances offer 3-10x lower cost than on-demand instances, but their unpredictable availability makes meeting deadlines difficult. Existing systems either rely solely on spot instances and risk deadline violations, or operate in simplified single-region settings. These approaches overlook substantial spatial and temporal heterogeneity in spot availability, lifetimes, and prices. We show that exploiting such heterogeneity to access more spot capacity is the key to reduce the job execution cost. We present SkyNomad, a multi-region scheduling system that maximizes spot usage and minimizes cost while guaranteeing deadlines. SkyNomad uses lightweight probing to estimate availability, predicts spot lifetimes, accounts for migration cost, and unifies regional characteristics and deadline pressure into a monetary cost model that guides scheduling decisions. Our evaluation shows that SkyNomad achieves 1.25-3.96x cost savings in real cloud deployments and performs within 10% cost differences of an optimal policy in simulation, while consistently meeting deadlines.

Abstract: This paper explores modern cybersecurity practices and threat mitigation strategies in cloud and web applications, focusing on practical techniques for penetration testing, vulnerability assessment, and secure system design. The work also includes hands-on experimentation and analysis of real world scenarios

Abstract: Cloud computing has been shown to be an essential enabling technology for public sector organizations PSOs and offers numerous potential benefits, including reduced information technology infrastructure costs, increased innovation potential, and improved resource resilience and scalability. Despite governments' intensifying efforts to realize the benefits of this technology, cloud computing adoption and usage proves to be challenging, posing a variety of organizational and operational issues for PSOs. This systematic analysis constitutes the initial phase of a larger research effort that involves forthcoming case studies of specific public sector cloud stakeholders; it aims to identify and synthesize the available knowledge on organizational cloud computing adoption and utilization in the public sector to provide public sector decision makers and stakeholders with reliable, evidence-based, actionable insights that inform and improve public sector IT practice and policy.

Abstract: Lightweight pose-aware Convolutional Neural Networks (CNNs), integrated with edge computing technologies, have created new opportunities in the field of real-time health emergency detection. Such systems use visual information about human body posture and behavior to detect critical medical events such as falls or immobility and to provide immediate and localized responses without relying on cloud infrastructure. This review examines the state-of-the-art in pose-aware CNNs optimized for deployment on edge devices, emphasizing their design, optimization techniques, deployment challenges, and application use cases. It draws insights from adjacent domains, including education and animal health monitoring, and illustrates how these cross-disciplinary advancements can inform healthcare solutions. The discussion covers an in-depth examination of hardware specifications, environmental limitations, energy efficiency, data privacy, and scalability of the system. By analyzing the current architectures and future opportunities, this review shows the potential of lightweight pose-aware CNNs to transform health monitoring to be both proactive and accessible.

Abstract: The Identity-as-a-Service Market is gaining strong momentum as organizations worldwide prioritize secure, scalable, and user-centric identity and access management solutions. As enterprises continue to migrate workloads to cloud environments and adopt remote and hybrid work models, the need for centralized identity control, seamless authentication, and regulatory compliance has become critical. IDaaS solutions address these demands by enabling secure access to applications and data while reducing operational complexity and infrastructure costs. Access full Report-https://www.nextmsc.com/report/identity-as-a-service-market