Implementing Secure and Scalable APIs in Java Spring Boot: RESTful vs. GraphQL Services

The rapid adoption of cloud-native architectures, driven by containerization and orchestration platforms such as Kubernetes, has transformed the scalability and flexibility of modern applications. Yet, achieving true high availability (HA) in such dynamic environments remains challenging, as traditional load balancing approaches—such as Round Robin or Least Connections—are inherently reactive and fail to capture the transient health conditions of containerized workloads. This paper presents an integrated approach to cloud load balancing and container orchestration for high availability through the Predictive and Health-Aware Load Balancing (PHAL) algorithm. PHAL combines two synergistic components: a Predictive Forecasting Module (PFM), which leverages Long Short-Term Memory (LSTM) networks to anticipate workload surges and proactively scale resources, and a Health-Aware Routing Module (HRM), which dynamically distributes traffic across pods based on a composite health score derived from latency, CPU utilization, active connections, and predicted headroom. Through experimental evaluation on a Google Kubernetes Engine cluster running a multi-service e-commerce application, PHAL demonstrates significant improvements over conventional load balancing strategies, including reduced tail latency, enhanced throughput, and resilience against node and pod failures. The study highlights that integrating intelligent cloud load balancing with orchestration frameworks is essential for ensuring robust, fault-tolerant, and highly available cloud-native systems.

The integration of Domain-Driven Design (DDD) with Kubernetes presents a transformative approach to deploying scalable, resilient, and business-aligned microservices. This paper explores the synergies between DDD’s strategic patterns such as bounded contexts, subdomains, and ubiquitous language and Kubernetes’ container orchestration capabilities. By aligning DDD’s domain-centric modeling with Kubernetes’ declarative infrastructure, organizations can achieve architectures that are both technically robust and closely tied to business objectives. Key considerations include the decomposition of business capabilities into microservices, containerization strategies, service discovery, and load balancing. Case studies illustrate practical implementations, emphasizing iterative deployment, resilience, and the alignment of technical and business domains. The findings highlight how Kubernetes and DDD collectively address modern challenges in cloud-native application development.

MDM is a tool that enables the users to define schemes of different data models and to perform translations of schemes from one model to another. These functionalities can be at the basis of a customizable and integrated CASE environment supporting the analysis and design of information systems. MDM has two main components: the Model Manager and the Schema Manager. The Model Manager supports a specialized user, the model engineer, in the definition of a variety of models, on the basis of a limited set of metaconstructs covering almost all known conceptual models. The Schema Manager allows designers to create and modify schemes over the defined models, and to generate at each time a translation of a scheme into any of the data models currently available. Translations between models are automatically derived, at definition time, by combining a predefined set of elementary transformations, which implement the standard translations between simple combinations of constructs.

Microservice architecture is a cloud application design pattern which shifts the complexity away from the traditional monolithic application into the infrastructure. Each microservice is a small containerized application that has a single responsibility in terms of functional requirement, and that can be deployed, scaled and tested independently using automated orchestration systems. We propose a simple swarm-like decentralized load balancing system for microservices running inside OpenVZ containers. It can potentially offer performance improvements with respect to the existing centralized container orchestration systems.

BACKGROUND The purpose of the study was to analyse approaches to improving reliability and recovery from failures in the processes of software development and operation. MATERIALS AND METHODS The research utilised a qualitative methodology, conducting semi-structured interviews with industry experts to investigate fault tolerance and disaster recovery solutions within DevOps processes. Grounded theory coding was applied to analyse the data, identifying key themes that were mapped to the research questions and comparison matrix. RESULTS The results showed that the microservices’ architecture is highly flexible and scalable, which allows for faster adaptation to changes in the workload and reduces the likelihood of system failures. However, the complexity of managing dependencies between services can lead to new problems. Serverless computing can be effective in reducing infrastructure costs and simplifying scaling, but there is a need for detailed monitoring of the functioning of functions and management of their resources. Containerisation and container orchestration are suitable for providing a high level of portability and application isolation. At the same time, the growing complexity of orchestration systems can be a challenge for the team working on their administration. Cloud computing and load balancing should be used to allocate resources and reduce the risk of stress on individual components, but they require constant monitoring to ensure optimal performance and identify potential problems. In addition, data replication and backup provide reliable protection against data loss, but the increase in data volume places new demands on the speed and efficiency of backup processes. Monitoring and alerting can improve timely detection and response to problems but require regular updates and adaptation to new threats. Automated testing and disaster recovery planning have proven to be key to reducing system recovery times, but the complexity of setting up automated scripts and tests remains an essential issue. CONCLUSION Overall, the results indicate the need for continuous improvement of existing methods to ensure increased resilience and adaptability of systems to new threats when working with software.

Over the last two decades, the IT sector has shifted from managing workloads on physical servers to virtualizing them and then further consolidating them into containers in the subsequent iteration. Later, Kubernetes or Open Shift were used to coordinate container workloads built on Docker and Podman. The use of containerization platforms for high-performance computing (HPC) settings, however, has been trailing behind since there is still much to learn. Containers provide flexibility, adaptability, and maintenance advantages, and they often have lower overhead. Additionally, by monitoring their performance and constantly regulating their behaviour, container orchestration platforms—which are being embraced by companies more and more—are crucial for efficiently managing the life-cycle of multi-container informatics systems. Specifically, the Kubernetes platform, a new open standard for cloud services, is helping to achieve service agnostic deployment by helping to isolate individual cloud providers via its Cloud Controller Manager mechanism. Finding the best container strategy for typical use scenarios and evaluating each solution's advantages and disadvantages were the goals. Basic performance measurements, such as average service recovery time, average transfer rate, and total number of unsuccessful calls, were gathered via a series of organized tests. Docker, Kubernetes, and Proxmox were among the container systems that were examined. Based on a thorough analysis, it can be said that the most efficient high-availability solution for widely used Linux containers is often Docker with Docker Swarm. However, there are other situations where Proxmox excels, such as when load balancing is not a crucial need or where quick data transmission is a top concern.

MDM is a tool that enables the users to define schemes of different data models and to perform translations of schemes from one model to another. These functionalities can be at the basis of a customizable and integrated CASE environment supporting the analysis and design of information systems. MDM has two main components: the Model Manager and the Schema Manager . The Model Manager supports a specialized user, the model engineer , in the definition of a variety of models, on the basis of a limited set of metaconstructs covering almost all known conceptual models. The Schema Manager allows designers to create and modify schemes over the defined models, and to generate at each time a translation of a scheme into any of the data models currently available. Translations between models are automatically derived, at definition time, by combining a predefined set of elementary transformations, which implement the standard translations between simple combinations of constructs.

The convergence of machine learning (ML) and web application frameworks has the potential to revolutionize real-time analytics in backend systems.Java Spring Boot, a widely adopted framework for building scalable microservices, can be effectively integrated with machine learning models to enhance real-time decisionmaking processes.This paper explores the integration of ML models developed using frameworks such as TensorFlow and PyTorch within Java Spring Boot services.It delves into such integration's architectural considerations, implementation challenges, and performance impacts.The research presents a comprehensive methodology for embedding ML models into Spring Boot applications, focusing on achieving low latency and high throughput in real-time analytics.The evaluation demonstrates the feasibility and effectiveness of the proposed approach, providing valuable insights for developers and organizations seeking to leverage machine learning in their backend systems.

Parallel computational scientific applications have been described by their computation and communication patterns. From a storage and I/O perspective, these applications can also be grouped into separate data models based on the way data is organized and accessed during simulation, analysis, and visualization. Parallel netCDF is a popular library used in many scientific applications to store scientific datasets and provides high-performance parallel I/O. Although the metadata-rich netCDF file format can effectively store and describe regular multi-dimensional array datasets, it does not address the full range of current and future computational science data models. In this paper, we present a new storage scheme in Parallel netCDF to represent a broad variety of data models used in modern computational scientific applications. This scheme also allows concurrent metadata construction for different data objects from multiple groups of application processes, an important feature in obtaining a high degree of I/O parallelism for data models exhibiting irregular data distribution. Furthermore, we employ non-blocking I/O functions to aggregate irregularly distributed data requests into large, contiguous data requests, to achieve high-performance I/O. Using an example of adaptive mesh refinement data model, we demonstrate the proposed scheme can produce scalable performance results for both data and metadata creation and access.

As machine learning (ML) models are used more and more for important business tasks, it is important to make sure that ML operations are safe. This makes it easier to automate tasks, make decisions, and come up with fresh ideas in a number of fields. These procedures generally comprise sensitive datasets, proprietary algorithms, and complex pipelines that go through many phases, such as taking in data, preparing it, training it, validating it, and deploying it. These pipelines are open to data corruption, model changes, and illegal access since they don't have strong security measures in place. All of these things might make the models substantially less accurate and reliable. Kubernetes is the best platform for building and scaling machine learning operations because it has strong container orchestration features, declarative configuration, and works with the latest MLOps tools. It is always changing and falling apart, however, which means it has its own security problems that need to be dealt with carefully and with careful preparation. This article talks about how to use Kubernetes and follow the best practices and principles of the Certified Kubernetes Security Specialist (CKS) framework. This post's purpose is to provide you knowledge on how to make machine learning pipelines safer. We will talk about how to set up strong Role-Based Access Control (RBAC) based on the principle of least privilege, how to create detailed network policies to keep workloads separate, how to safely manage secrets using both native Kubernetes tools and third-party tools, and how to use runtime security measures like container image scanning and admission controllers to make sure that everything stays safe at all times. In the context of building machine learning systems that can solve problems, we want to stress how important DevSecOps concepts are. This list includes things like putting security first, automating the process of finding vulnerabilities, and always keeping an eye on pipelines. In this case, the security rules that come with Kubernetes are applied. Companies may build machine learning systems that are scalable, auditable, and safe utilizing methods that CKS has approved. This lowers risks and encourages new ideas. There is a case study at the end of the post that shows how a tiered security policy may make a real-world machine learning pipeline better. This is solid advice for anybody who is working with Kubernetes-based machine learning systems when it comes to putting security first

High-end computing is suffering a data deluge from experiments, simulations, and apparatus that creates overwhelming application dataset sizes. This has led to the proliferation of high-end mass storage systems, storage area clusters, and data centers. These storage facilities offer a large range of choices in terms of capacity and access rate, as well as strong data availability and consistency support. However, for most end-users, the “last mile” in their analysis pipeline often requires data processing and visualization at local computers, typically local desktop workstations. End-user workstations---despite having more processing power than ever before---are ill-equipped to cope with such data demands due to insufficient secondary storage space and I/O rates. Meanwhile, a large portion of desktop storage is unused.We propose the FreeLoader framework, which aggregates unused desktop storage space and I/O bandwidth into a shared cache/scratch space, for hosting large, immutable datasets and exploiting data access locality. This article presents the FreeLoader architecture, component design, and performance results based on our proof-of-concept prototype. Its architecture comprises contributing benefactor nodes, steered by a management layer, providing services such as data integrity, high performance, load balancing, and impact control. Our experiments show that FreeLoader is an appealing low-cost solution to storing massive datasets by delivering higher data access rates than traditional storage facilities, namely, local or remote shared file systems, storage systems, and Internet data repositories. In particular, we present novel data striping techniques that allow FreeLoader to efficiently aggregate a workstation's network communication bandwidth and local I/O bandwidth. In addition, the performance impact on the native workload of donor machines is small and can be effectively controlled. Further, we show that security features such as data encryptions and integrity checks can be easily added as filters for interested clients. Finally, we demonstrate how legacy applications can use the FreeLoader API to store and retrieve datasets.

With the increasing adoption of RESTful APIs as the backbone of modern web and mobile applications, ensuring their security has become a critical concern. Traditional security mechanisms such as rule-based firewalls and static rate-limiting policies are often ineffective against sophisticated, evolving threats like zero-day attacks, automated bot traffic, and API abuse patterns. This research proposes an intelligent, machine learning-based framework to detect and prevent malicious activity in REST API traffic. The approach involves collecting and preprocessing real-time API request logs to extract behavioural and contextual features. Supervised and unsupervised machine learning models such as isolation forests, LSTM-based anomaly detectors, and decision trees are evaluated for their effectiveness in detecting anomalies, injection attacks, and abnormal usage behavior. Furthermore, the study incorporates reinforcement learning to dynamically adjust security policies (e.g., rate limits, IP bans) in response to detected threats without impacting legitimate users. A proof-of-concept prototype will be developed and deployed in a controlled environment to evaluate performance in terms of detection accuracy, false positive rates, and system latency. The outcome of this research aims to advance the state-of-the-art in API security by introducing adaptive, self-learning mechanisms capable of protecting APIs from modern security threats while maintaining usability and performance.

SummaryThe block chain is the emerging domain that offers the features like shared, immutable, and decentralized dataset that saves the asset's history. Security and storage are the most challenging aspect. The classical techniques' issues and challenges are an encouragement for developing a fresh block chain‐assisted IPFS model with access control policies. Hence, this research paper introduces an effective access control and authentication based on an interplanetary file system (IPFS)‐based block chain and access control system for attaining secured transactions. The developed method comprises five entities, that is smart contract, data owner, data requester, block chain and IPFS along with eight phases, to be exact setup, user registration, initialization, storage and data encryption, authentication, testing, access control, and decryption phase, to provide better access control and storage capacity. The proposed IPFS‐based block chain and access control system offered effective storage and secure transactions in the block chain network. The highest detection rate (0.870), lowest communication overhead (0.766 MB), and highest privacy rate (0.719) are all achieved by the proposed IPFS‐based block chain and access control system.

Deploying Java Spring Boot applications on Google Cloud Platform (GCP) involves choosing cost-effective solutions. While Kubernetes provides powerful orchestration, its high costs make it impractical for smaller-scale applications. This article proposes Docker as an alternative for containerizing Spring Boot applications on a single virtual machine (VM). Docker ensures consistent environments, simplifies management, and reduces infrastructure costs. This approach enhances deployment reliability and streamlines operations, making it ideal for budget-conscious teams.

A Context-Aware Framework for Secure Fintech APIs: Leveraging Java Spring Boot and OAuth 2.0 with Dynamic Token Adaptation