Designing and implementing scalable microservices with spring boot on AWS

Enterprise IT infrastructures experience a transformation because of rapid cloud computing adoption, especially Amazon Web Services (AWS), which delivers scalability, flexibility, and cost efficiency. AWS users encounter difficulties optimizing their performance because they face problematic situations combining workload variability with complex resource distribution systems and delay issues. The implementation of Artificial Intelligence constructs an evaluation process to boost performance optimization in Amazon Web Services (AWS) cloud systems. AI-powered insights implemented through machine learning algorithms with predictive analytics capabilities and automation functionality improve workload distribution and enhance auto-scaling efficiency while reducing operational expenses. Processor analysis of historical data combined with real-time metrics allows AI algorithms to make proactive decisions about resource administration, detect anomalies, and perform innovative management. The research evaluates performance-altering AI-based approaches that enhance AWS services, including EC2, Lambda, and Sage Maker. Implementing AI-based optimizations generates copious improvements in computational efficiency and cost performance based on experimental data. Thanks to AI, cloud performance management strategies will fundamentally transform since they endorse efficient AWS environments that align with business requirements.

Cloud computing has revolutionized information technology (IT) infrastructure by enabling on-demand access to scalable resources. However, the elasticity and complexity of cloud billing models introduce significant challenges for effective resource cost management. This paper proposes a hybrid framework integrating statistical models auto regressive integrated moving average (ARIMA), machine learning techniques long short-term memory (LSTM), and optimization methods deep deterministic policy gradient (DDPG) to forecast and manage cloud costs with enhanced accuracy and adaptability. The framework is empirically validated using synthetic billing datasets and real-world cloud provider data, with performance evaluated via root mean square error (RMSE) and mean absolute percentage error (MAPE) metrics. Results demonstrate 15-25% improvement in cost prediction accuracy over baseline models and up to 20% cost savings through dynamic resource allocation. The framework extends beyond traditional VM-based workloads to support serverless computing (amazon web services (AWS) Lambda and Azure Functions) and container-based applications (Docker and Kubernetes), addressing the growing adoption of microservices architectures. Comparative analysis with existing tools (AWS Cost Explorer and Azure Advisor) reveals superior adaptability in multi-cloud environments. The paper concludes with discussions of emerging paradigms including FinOps practices, AIOps automation, and sustainability-aware resource allocation, outlining future research directions toward explainable AI-driven cost governance.

The Internet of Things (IoT) has emerged as one of the most transformative paradigms in modern computing, enabling billions of connected devices to sense, process, and transmit data. This growth poses challenges in scalability, security, and real-time data management. Cloud platforms, particularly Amazon Web Services (AWS), have become central enablers of large-scale IoT deployments due to their comprehensive service ecosystem. This paper presents a review of AWS IoT, focusing on its architecture, applications, and open research directions. We analyze core components such as AWS IoT Core, Lambda, Greengrass, and data services, highlighting their roles in enabling cloud-edge integration and secure device management. A survey of academic literature and industrial case studies demonstrates AWS IoT's practical adoption across healthcare, agriculture, and smart city domains. Comparative analysis with Microsoft Azure IoT and Google Cloud IoT positions AWS as a scalable and secure platform, while identifying limitations in latency, cost, and provisioning complexity. Finally, we outline future research opportunities in federated learning, energy-efficient edge inference, protocol interoperability, and blockchain-based provisioning. This review provides a consolidated reference for researchers and practitioners aiming to leverage AWS IoT for next-generation intelligent systems.

In view of the fact that the limited wireless resources and the asymmetric traffic between uplink and downlink in the trunking communication system based on TD-SCDMA network, a design scheme of dynamic resource allocation (DCA) is given. DCA is a very effective technology to solve those problems and enhance the utilization of resource. Based on the characteristics of the trunking communication system, we combine with the DCA algorithms of TD-SCDMA network in this paper, and give an optimal DCA strategy for the truking communication system in TD-SCDMA network. Finally, the simulation result shows the DCA strategy markedly improve the system performance and gain higher resource utilization.

This article investigates resource allocation in ATM networks and emphasizes bandwidth allocation. Resource allocation methods are categorized, and static and dynamic allocation strategies are compared. Dynamic allocation uses actual traffic behavior, while static allocation uses only the reference traffic condition given a priori. Examples of dynamic allocation strategies are available bit rate flow control, dynamic connection admission control, and dynamic VP bandwidth control. Dynamic resource allocation is shown to be promising for situations where the a priori reference model is unclear.

With the advent of gaining popularity in cloud computing, there is an increase in demand of resources among the heterogeneous workload types on cloud. Resource management is a key challenging problem that is faced by the cloud service providers, by achieving business goals and agreed level of service with the subscribers. This paper focuses on dynamic resource allocation with risk analysis by meeting service level agreements. Further proposed framework handles heterogeneous workload types by dynamic capacity planning with risk assessment to maximise the profit and resource utilisation on clouds. In addition to advanced resource reservation, SLA-based scheduling/rescheduling with risks involved in resource allocation is considered in the proposed model. The experimental results demonstrate that proposed framework maximises the resource utilisation and profit gain of the cloud service provider when evaluated against widely used static configuration strategy.

We highlight the advantages of dynamic wavelength allocation and path rerouting in hybrid optical flow-switched data center networks compared with static electronic packet-switched data center networks. Current electronic packet-switched networks perform routing based on lookup tables and shortest path routing over high speed optical connections. However, load balancing and path rerouting are not designed in coordination with the optical data plane. In this work, we design and analyze the performance of load balancing and resource allocation in hybrid flow data center architectures. In the hybrid flow architecture, the transport layer and control plane are responsible for load balancing, congestion control and reliable data delivery in wavelength division multiplexed networks. The transport layer abstracts the underlying network and data link layers from user applications. Applications are unaware of whether flows are transmitted via the electronic data plane, optical data plane, or both, where the relevant metric for applications is transaction delay. We show that the data center architecture that minimizes delay in the presence of elephant flows and dynamic loads is the hybrid flow architecture with dynamic resource allocation.

This paper discusses the issue of dynamic resource allocation (DRA) in the context of cognitive radio (CR) networks. We present a general framework adopting generalized transmitter and receiver signal-expansion functions, which allow us to join DRA with waveform adaptation, two procedures that are currently carried out separately. Moreover, the proposed DRA can handle many types of expansion functions or even combinations of different types of functions. An iterative game approach is adopted to perform multi-player DRA, and the best-response strategies of players are derived and characterized using convex optimization. To reduce the implementation costs of having too many active expansion functions after optimization, we also propose to combine DRA with sparsity constraints for dynamic function selection. Generally, it incurs little rate-performance loss since the effective resources required by a CR are in fact sparse.

The DVB-RCS standard provides a framework for Multi-Frequency TDMA (MF-TDMA) and dynamic resource (frequencies and time slots) allocation over satellite links. Nevertheless, MF-TDMA and dynamic resource allocation alone are not sufficient to achieve satisfactory throughput for TCP, the dominant transport protocol in IP-based networks such as the Global Information Grid (GIG) system, due to the inherent large delay-bandwidth product of the satellite links. Various mechanisms called TCP Performance Enhancement Proxies (PEP) are proposed to improve the performance of TCP over large delay-bandwidth product networks. However, typical TCP PEPs do not support GIGpsilas layer-3 security standard HAIPE (High Assurance Internet Protocol Encryption). In this paper, we identify eXplicit Control Protocol (XCP) as a desired performance enhancement mechanism, and propose a novel system architecture that integrates DVB-RCS, XCP and HAIPE. We implement XCP in the network simulator QualNet and evaluate its performance against that of TCP. Our simulation results show that XCP achieves higher throughput and fairer bandwidth allocation among competing traffic flows in DVB-RCS systems than TCP.

Traditional algorithms for dynamic OFDMA resource allocation have relatively deterministic system capacity and user fairness. Thus, in this letter, an efficient scheme is proposed to flexibly adjust quality-of-service for users, which is achieved by appropriately setting minimum data-rate of each user.

The rapid proliferation of internet-connected devices and the consequent evolution of cloud-computing infrastructure have created an unprecedented opportunity to redesign the way healthcare services are delivered. Traditional healthcare systems are burdened by geographical barriers, administrative overheads, fragmented record-keeping, and constrained access to specialist physicians—challenges that have been accentuated in the post-pandemic era. This paper presents TeleMed, a comprehensive, full-stack telemedicine web platform that integrates live video consultations, electronic health record (EHR) management, and artificial intelligence (AI)-based diagnostic assistance into a single, unified ecosystem. The platform is architected as a decoupled client-server application powered by a React.js single-page frontend, a Java 17 / Spring Boot 3.0 backend secured with JSON Web Token (JWT) authentication, and a hybrid database model comprising PostgreSQL for relational transactional data and MongoDB for unstructured medical records. The AI diagnostic module leverages the Meta Llama 3 large language model (LLM), accessed through the HuggingFace Inference Interface, employing advanced zero-shot and few-shot prompt engineering with dynamic patient-context injection to deliver highly personalized symptom analysis without the overhead of fine-tuning custom datasets. Real-time video consultations are facilitated through WebRTC standards via the Jitsi Meet API, enabling secure, low-latency virtual rooms. Experimental validation confirms that TeleMed successfully unifies intelligent triage, doctor discovery, appointment scheduling, and live consultation into a scalable platform, offering a practical blueprint for next-generation remote healthcare delivery

By using the time-varying channel responses of individual users, suitable dynamic resource allocation (DRA) strategies can improve resource utilization to provide broadband multimedia services of different QoS requirements over OFDM downlink in mobile cellular systems (Damji, N. and Le-Ngoc, T., ICC04, 2004). For non-realtime traffic, queueing can be used in conjunction with DRA to enhance further the system performance in terms of system spectral efficiency (b/s/Hz/cell) and system throughput. Performance of the proposed DRA schemes with queueing in various traffic and propagation conditions is evaluated and compared.

In a shared virtual computing environment, dynamic load changes as well as different quality requirements of applications in their lifetime give rise to dynamic and various capacity demands, which results in lower resource utilization and application quality using the existing static resource allocation. Furthermore, the total required capacities of all the hosted applications in current enterprise data centers, for example, Google, may surpass the capacities of the platform. In this paper, we argue that the existing techniques by turning on or off servers with the help of virtual machine (VM) migration is not enough. Instead, finding an optimized dynamic resource allocation method to solve the problem of on-demand resource provision for VMs is the key to improve the efficiency of data centers. However, the existing dynamic resource allocation methods only focus on either the local optimization within a server or central global optimization, limiting the efficiency of data centers. We propose a two-tiered on-demand resource allocation mechanism consisting of the local and global resource allocation with feedback to provide on-demand capacities to the concurrent applications. We model the on-demand resource allocation using optimization theory. Based on the proposed dynamic resource allocation mechanism and model, we propose a set of on-demand resource allocation algorithms. Our algorithms preferentially ensure performance of critical applications named by the data center manager when resource competition arises according to the time-varying capacity demands and the quality of applications. Using Rainbow, a Xen-based prototype we implemented, we evaluate the VM-based shared platform as well as the two-tiered on-demand resource allocation mechanism and algorithms. The experimental results show that Rainbow without dynamic resource allocation (Rainbow-NDA) provides 26 to 324 percent improvements in the application performance, as well as 26 percent higher average CPU utilization than traditional service computing framework, in which applications use exclusive servers. The two-tiered on-demand resource allocation further improves performance by 9 to 16 percent for those critical applications, 75 percent of the maximum performance improvement, introducing up to 5 percent performance degradations to others, with 1 to 5 percent improvements in the resource utilization in comparison with Rainbow-NDA.

Cloud computing is emerging as an increasingly popular computing paradigm, allowing dynamic scaling of resources available to users as needed. This requires a highly accurate demand prediction and a resource allocation methodology. The existing methodologies for dynamic resource allocation do not provide effective performance isolation between the VM and Artificial Demand Analysis machines since it gets affected by interferences. To overcome these issues, this paper proposes a conceptual model and an effective algorithm to achieve dynamic resource allocation by migrating tasks or requests in VMs. At first, task demands from the multiple users go to the feature extraction process. In feature extraction, features of the user's tasks and cloud server are extracted. Next both features are reduced by using Modified PCA algorithm to reduce the dynamic resource allocation processing time. Finally, both the features are combined and resource allocation is performed using Hybrid Particle Swarm Optimization and Modified Genetic Algorithm (HPSO-MGA). Then the optimized task has been scheduled to particular VM for allocating the resources. The experimental result of the proposed resource allocation methodology indicates better performance when compared with the existing methods Firefly and Krill herd Load Balancing (LB). For 100 VMs the reliability of HPSO-MGA is 0.87 but the exiting krill herd LB and IDSA gives 0.78 and 0.85, which is lower than the proposed one.

Dynamic resource allocation combined with multiple antennas (MIMO) can farther increase the system capacity and the quality of service (QoS) of conventional OFDM systems. Selection diversity is the simplest way to realize spatial diversity, but the performance improvement of this form of diversity is limited. Adaptive antenna arrays promise to achieve significant increases in system capacity and performance in wireless communications, but they are characterized by a relatively higher implementation complexity than that of selection diversity. In this paper, we propose a dynamic resource allocation scheme with selective beamforming for MIMO/OFDM systems. By applying eigenvalue decomposition on the subset of all available channel correlation matrices, the best beamforming, which corresponds to the largest eigenvalue in the subset, is adaptively selected. Then the proposed dynamic resource allocation algorithm adaptively assigns the bit and power distribution according to the channel gain corresponding to the selected best beamforming, under the constraint of fixed power and overall bit rate. By doing so, the maximal SNR on each sub-carrier obtained by selective beamforming is further maximized. Numerical results show that under frequency-selective multipath fading, the proposed system yields significant performance improvements over the conventional OFDM transmission.