Scalable and Intelligent Centralized Alerting Frameworks for Multi-Region Cloud Environments

The rapid evolution of Amazon Web Services (AWS) cloud technologies continues to reshape enterprise computing environments across global industries. This technical review examines recent innovations in AWS services that are transforming organizational capabilities and competitive positioning. Beginning with computational performance advancements through AWS Graviton processors and specialized High-Performance Computing offerings, the article explores how these ARM-based architectures deliver enhanced efficiency across diverse workloads. The expanding AI and Machine Learning ecosystem, particularly through AWS Bedrock's foundation model integration and SageMaker's democratized ML tools, enables organizations to implement sophisticated intelligence capabilities without extensive expertise. Serverless computing developments, including Lambda Function URLs and SnapStart features, alongside visual workflow orchestration through Step Functions, have simplified application development while improving performance characteristics. Container orchestration enhancements facilitate consistent hybrid deployments across on-premises and cloud environments. Looking forward, AWS continues strategic investments in quantum computing initiatives, sustainability practices, and multi-cloud compatibility tools that position the platform at the forefront of cloud innovation. These advancements collectively enable organizations to achieve greater operational agility, cost efficiency, and innovation capacity while addressing evolving challenges in security, compliance, and technical workforce development.

ObjectiveThe BACTEC MGIT 960 system is currently the most widely used rapid culture system for Mycobacterium tuberculosis. However, its reagents have complex compositions, expensive, and rely on import. This study aimed to develop a self-formulated culture with simple composition, low cost, and performance comparable to or even better than that of the BACTEC MGIT 960 system.MethodsFive experimental media were formulated by supplementing Sauton medium with carrot extract, potato extract, oleic acid, or choline chloride. These were compared against commercial MGIT 960 medium using M. tuberculosis H37Rv suspensions (10-³-10-6 mg/mL, triplicate cultures). Time to detection (TTD) and growth index (GI) were monitored automatically using the BACTEC MGIT 960 system.ResultsFor medium-high inocula (10-³-10-5 mg/mL), carrot- and potato-supplemented Sauton media showed equivalent performance to MGIT 960 (1-day longer TTD, P>0.05; comparable GI values). At low inoculum (10-6 mg/mL), MGIT 960 failed to detect growth within 42 days, whereas supplemented Sauton medium achieved positivity within 30 days. The carrot extract medium performed best (mean TTD = 20.2 days), followed by oleic acid and oleic acid + choline chloride formulations.ConclusionCarrot- and potato-supplemented Sauton medium demonstrated comparable performance to the MGIT 960 system for specimens with medium-to-high bacterial loads. Carrot extract, oleic acid, and choline chloride significantly enhanced the detection rate of low-inoculum, with carrot extract exhibiting the most pronounced growth-promoting effects. This study validates an accessible alternative to expensive imported media, particularly beneficial for resource-limited settings.

The increasing migration of enterprise operations to Amazon Web Services (AWS) has amplified the need for robust, scalable, and intelligent cybersecurity solutions. Security Information and Event Management (SIEM) systems have become vital for detecting threats, correlating logs, and maintaining compliance in cloud environments. This systematic review explores the integration of SIEM tools within AWS-based infrastructures, focusing on their effectiveness in threat detection and log correlation. It examines leading SIEM solutions such as Splunk, IBM QRadar, Sumo Logic, and AWS-native services like Amazon GuardDuty, AWS CloudTrail, and AWS Security Hub. Emphasis is placed on key integration approaches, including API-based ingestion, agentless data capture, and real-time event streaming through AWS services like Kinesis and S3. The review critically analyzes studies published between 2018 and 2023, highlighting trends in the automation of log management, enrichment of security alerts through machine learning, and orchestration via Security Orchestration, Automation, and Response (SOAR) platforms. Challenges such as data normalization, scalability limitations, cross-service visibility, and compliance adherence in multi-account AWS architectures are discussed. The findings indicate that SIEM integration enhances threat detection efficiency by enabling proactive anomaly detection, facilitating rapid incident response, and improving forensic investigation capabilities. However, the review identifies gaps, particularly in cost optimization, handling high-velocity log streams, and adapting traditional SIEM models to dynamic, serverless AWS architectures. Best practices for successful SIEM deployment include leveraging AWS-native integrations, prioritizing event prioritization algorithms, applying continuous tuning, and aligning with security frameworks like NIST and CIS AWS Foundations Benchmark. Future research directions propose the development of AI-driven adaptive SIEM systems tailored for cloud-native environments, advanced correlation engines for serverless and containerized workloads, and strategies to optimize licensing and resource utilization. This systematic review provides cybersecurity practitioners, cloud architects, and researchers with a comprehensive understanding of SIEM integration complexities and evolving practices in AWS infrastructures, ultimately contributing to improved cloud security postures and operational resilience in the face of sophisticated cyber threats.

This study delves into the optimization strategies of Amazon Web Services (AWS) cloud computing solutions tailored specifically for businesses. It provides an in-depth exploration of the transformative impact that cloud computing has had on modern business operations, emphasizing the pivotal role played by AWS in delivering scalable, flexible, and cost-effective resources. With projections indicating the global public cloud services market's growth to $332.3 billion in 2021, it becomes evident that cloud solutions are increasingly relied upon, with AWS commanding a significant 32% market share. This paper highlights the importance of optimizing AWS solutions, considering its extensive range of services designed to meet diverse business needs. However, transitioning to cloud environments brings challenges related to data security, compliance, integration, and migration complexities, as evidenced by pertinent studies and literature reviews. This research synthesizes insights gleaned from various studies, emphasizing the adaptability and cost-effectiveness of AWS while stressing the critical role of robust security measures in its implementation across industries and geographic regions. Understanding prevalent business requisites and challenges in cloud adoption, including scalability, cost efficiency, reliability, availability, and compliance, is essential for organizations seeking to harness cloud technology effectively. Furthermore, this paper provides an overview of AWS's services encompassing computing, storage, databases, machine learning, and AI, showcasing how these empower businesses to streamline operations, foster innovation, and scale dynamically. It elucidates successful instances where AWS was strategically implemented in Netflix, Airbnb, Jollibee Group, and Capital One, demonstrating its impact on scalability, innovation, and service enhancement. The paper outlines anticipated trends in AWS and cloud computing, focusing on user-friendly tools, advancements in machine learning, infrastructure improvements, increased automation, and deeper integration of AI and advanced analytics. Ultimately, this research emphasizes business's need to optimize AWS solutions, navigate complexities, foster innovation, and achieve operational excellence in an increasingly competitive market.

This research aims at examining the strategies of performance tuning in cloud computing with emphasis on the optimization of applications, minimized response time, and optimal, affordable resource utilization. The research therefore includes a systematic literature review together with quantitative findings through empirical testing of the proposed model on Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), in addition to qualitative insights of experts. Auto-scaling, load balancing caching, database optimizing, integration with edge computing and predictive workloads using Artificial Intelligence are the aspects that are also studied as key performance tuning latter. Soon, the investigation, which was made based on the results from applying the four techniques on different applications and two clouds, demonstrates the strength of each technique in achieving different goals. While auto-scale and load balance feature is very helpful in control of workload fluctuations, the caching and database optimization helps in the efficient retrieval of the data. Edge computing reduces latency in response to real-time applications, and the application of artificial intelligence in workload forecast smoothes resource utilization in environments with a rapidly changing workload. Accordingly, the research has shown need for careful choosing of suitable performance-oriented interventions to enhance the application’s interactions, decrease CPU utilization, and cut costs in a cloud environment. Lastly, this work offers practical knowledge about the methods of cloud performance tuning to support better application deployment in the cloud environments.

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.

Micro frontend architecture represents a transformative approach to building large-scale web applications by extending microservices principles to the frontend layer, enabling independent development, testing, and deployment of discrete application modules. This architectural pattern addresses the critical challenges faced by enterprise organizations managing monolithic frontend applications, including lengthy build times, complex deployment processes, and coordination bottlenecks among multiple development teams. Through comprehensive analysis of real-world implementations across various organizational contexts, this article examines the architectural foundations, implementation strategies, benefits, challenges, and organizational impacts of micro frontend adoption in enterprise environments. The article reveals that while micro frontends deliver significant advantages in team autonomy, scalability across multiple dimensions, technology flexibility, and fault isolation, they simultaneously introduce substantial complexity in system architecture, performance optimization, consistency maintenance, and testing strategies. The successful implementation of micro frontends extends beyond technical considerations to encompass profound organizational transformations, requiring evolution from traditional feature teams to autonomous product teams, establishment of lightweight governance models, adoption of DevOps practices, and cultivation of new cultural values emphasizing modularity and explicit communication. Drawing from experiences in customer relationship management systems, monolithic-to-micro frontend conversions, and comparative analyses between startups and established enterprises, this article provides practical insights and lessons learned to guide organizations considering or currently implementing micro frontend architectures in their enterprise applications.

Background Organizations achieve agility, scalability, and enhanced resource utilization in multi-cloud environments, but face challenges in ensuring uniform and robust security across diverse cloud platforms. Variations in configuration and security mechanisms among providers hinder consistent policy enforcement and expose systems to data breaches and evasive threats. Additionally, the dynamic and distributed nature of multi-cloud operations broadens the attack surface, making real-time threat mitigation more complex. Methods To address these challenges, we introduced a groundbreaking software-defined networking (SDN)-enabled framework that incorporates deep learning for attack detection and adaptive security policy management. The framework consists of two primary components: the Software-Defined Multicloud Defense Controller (SDMDC), which enables centralized, real-time security policy enforcement (control plane), and the Multicloud Intrusion Detection System Gateways (MCIDS-G), which facilitate distributed threat detection across cloud platforms (data plane). The SDMDC’s integrated IDS is built using the Cross-Cloud Threat Transformer (CCTT) model, while the MCIDS-G’s regional IDS is based on the Long Short-Term Memory (LSTM) model. Additionally, the Lemurs Optimizer (LO) is employed in the SDMDC for cost-efficient policy management. SDMDC enforces security standards across all cloud environments where applications operate. Results The proposed solution addresses long-standing cloud security issues by combining coordinated global security strategies with automated threat detection and centralized policy management. SDMDC ensures consistent policy enforcement across cloud environments and manages ingress/egress and east–west traffic between cloud domains, including Amazon Web Service (AWS) and other service providers. The architecture supports dynamic resource orchestration and horizontal scalability, enabling adaptive performance under varying load conditions. The system also enables automatic policy implementation across platforms and facilitates real-time threat response to maintain consistent security. Conclusion The presented framework represents a significant advancement over existing multi-cloud protection solutions. It introduces new research directions, particularly in traffic management, firewall integration, and fully qualified domain name (FQDN) policy enforcement with proxy management.

Cloud Database Management System (CDBMS) is one of the potential services provided by various Cloud Service Providers. Cloud providers cope with different users, different data and processing or analysis of different data. Traditional Database Management Systems are insufficient to handle such variety of data, users and their requirements. Hence, at the conceptual layer of CDBMS, traditional SQL, Oracle and many more Database Languages are insufficient to provide proper services to their users. HIVE and Pig are the different types of languages which are suitable for the cloud environment which can handle such huge amount of data. In this paper, performance comparison of 3-Node cluster and Cloud Based Cluster provided by the Amazon Web Services is being done. We have compared the processing of structured data with the help of different queries provided by HIVE tool on 3-Node cluster and Amazon Web Service (AWS) cluster. It has been concluded that HIVE queries on AWS cluster gives better results as compared to 3-Node cluster.

Cloud infrastructure refers to the dynamic provisioning of computing resources over the internet, enabling scalable and flexible enterprise operations. However, such environments face significant security challenges, particularly in access management. Cloud infrastructure delivers scalable computing resources, yet traditional Identity and Access Management (IAM) mechanisms face challenges such as centralized control, misconfigurations, and limited auditability. This study addresses these challenges by proposing a Hyperledger Fabric-based decentralized access control framework integrated with Amazon Web Services (AWS) for healthcare data security. The framework employs Zero Knowledge Proof (ZKP) for identity validation, Ciphertext Policy Attribute Based Encryption (CP- ABE) with Proxy Re-Encryption (PRE) for fine-grained data access, and machine learning driven anomaly detection for continuous monitoring. Experimental evaluation achieved throughput of 15000 transactions per second, latency of 350 milliseconds, privacy preservation up to 99.1 percent, and anomaly detection accuracy of 99.63 percent, surpassing prior models significantly. Storage analysis demonstrated predictable scalability with encrypted medical records up to 20 MB, while token revocation time remained within 1.9 seconds under network stress. The results confirm that blockchain-based access control enhances security, privacy, and auditability while maintaining operational efficiency. This research establishes a scalable and tamper-resistant model for secure healthcare data management in cloud environments.

Nowadays every big and small IT industry has at least one application using the cloud technology. In 2016, Amazon Web Services, a leading provider of public cloud, generated $ 12.2 billion in net sales and its increase each year. The proposed cloud framework is an assistance wherein information is distantly overseen, oversaw and upheld up. The administration is accessible to clients on a system, which is commonly the Internet. The proposed cloud framework offers two alternatives for the client to oversee records, one is Cloudinary, the other is in the application. Clients have a choice to back up their data and records. Individuals can transfer and make records web based, setting them in the favored area. Specifically, reinforcements become increasingly versatile and helpful for clients when the record is available on the Internet. Moreover, clients can get to their data through an assortment of gadgets. In our application, we implemented chatting system, which will help users to chat with other user. User can chat in group as well as personal chat they can do. This application is accessible by all digital devices, which have web browsers and internet connectivity. There are many other different features, which can be performed by users they are Rename of files, deletion of files from their account, Export files.

Modern Software-as-a-Service (SaaS) platforms require a strongand reliable infrastructure that can function seamlessly across various cloud providers, without losing consistency and efficiency in operations. The multi-cloud architecture, however, brings in a lot of complexity due to the differences in provider APIs, resource models, and operational characteristics. When infrastructure management is handled manually, configuration drift becomes a significant issue. The conventional deployment methods are not efficient enough to work at a large scale across different cloud environments. The piece lays out an intricate automation framework that combines Infrastructure as Code (IaC) provisioning, multi-level validation testing, and centralized observability in a flawless manner. Using Terraform, one can carry out declarative infrastructure specifications that can be spread over Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Puppet ensures that the standard configurations are kept in the various node populations. Unit testing is employed to confirm the correctness of the individual modules before their integration. Acceptance testing simulates the end-to-end workflows in temporary environments that are exact replicas of production topologies. Deployment of catalog comparison strategies uncovers unintentional configuration changes prior to implementation. Centralized logging collects the events from distributed systems that are open for cross-cutting analysis. Monitoring dashboards draw the time-series metrics from the infrastructure components and the applications. Automated alerting pinpoints the operational issues facing the threshold conditions and anomaly detection. The combination of the framework fills in the critical gaps of the existing single-cloud automation approaches. Declarative specifications are there to remove the complexities of provider-specific. Automated validation is there to stop the propagation of faulty configurations across distributed systems. Unified observability is the consistency of visibility across fragmented multi-cloud landscapes. The outline exhibits the solutions for the realization of enterprise-scale cloud operations by the integration of strategic automation.

Decentralized and distributed blockchain technology has superior potential. Quorum, as a permissioned blockchain platform, provides enhanced privacy and performance that are suitable for enterprise needs. Cloud, as the infrastructure for Quorum-based blockchain, is vulnerable to failures. Failures originating from errors need to be tolerated through improved reliability and fault-tolerance procedures. Fault-tolerance and reliability are still challenging to implement in cloud infrastructure for Quorum-based blockchain networks. This research focuses on implementing fault-tolerance mechanisms in the provisioning of Quorum-based blockchain in a cloud environment. The research explores the use of hybrid fault tolerance by adopting a combination of reactive protocol, namely replication, and proactive protocol, namely monitoring. The replication protocol creates data redundancy with reactive treatment performed in case of failures, ensuring data availability even in failure conditions. The monitoring protocol provides real-time monitoring of the blockchain network's condition and performance, enabling proactive detection and mitigation of system failures. Overall, this research utilizes automation in its operational processes. It employs tools such as Amazon Web Services, Quorum, Docker, Terraform, Ansible, and Truffle. The research design and implementation are divided into cloud infrastructure, blockchain network, and interface. The research testing results demonstrate that the system infrastructure is capable of achieving fault tolerance using replication and monitoring protocols, as well as making transactions within the blockchain network.

ABSTRACTThe demand for fast, precise, and sensitive food safety methods is growing as consumers increasingly rely on online food delivery services. Food products shipped from South America to Europe can spend more than 21 days in transit, often resulting in deterioration, mould, or pathogen growth. Similar risks apply in the food service industry, where food may have been stored or handled improperly, leading to foodborne illness. This paper presents the Edispotter, an IoT‐based food quality monitoring system designed to address these and similar issues. Using Raspberry Pi and ESP32, the Edispotter collects essential food quality data through various sensors. The data are processed and stored in a Redis database within an Amazon Web Services (AWS) cloud environment, providing real‐time food‐based status updates via an Android application.

Elastic, low latency, and cost-effective systems are becoming increasingly important as both business decisioning systems (real and batch time) as well as processing analytic models and policies, and business rules across volatile workloads. This paper describes this type of serverless architecture for decisioning implemented on Amazon Web Services (AWS) and evaluates the performance of the solution on throughput, latency, cost, maintainability and operational resiliency, by comparison with traditional deployments on containers or virtual machines. We have proposed an architecture which is a combination of AWS Lambda for stateless policy evaluation, Step Functions for orchestration of multi-step decision flows, API Gateway for secure ingestion, DynamoDB for low-latency storage of state and features and Event Bridge/SQS for asynchronous eventing. We bring together model inference with Amazon sage Maker endpoints/lambda hosted lightweight models, and share best practices around cache/ cold- start mitigation, concurrency control, and transactional consistency. An end-to-end decision latency metric, scalability under burst traffic, cost/decision metric, and operational complexity metric (deployment and monitoring) are also used to evaluate the performance of the decision support system for synthetic and natural workloads. Results show that a properly architected serverless decisioning platform is capable of providing sub-100ms median latency for typical decision profiles, near-linear cost reduction at low to moderate utilization levels and simplified operations through managed services, but with trade-offs relating to cold starts, stateful workflows and deterministic performance at very high sustained throughput. As a conclusion, we provide best practice suggestions, design patterns for hybrid serverless/stateful components, as well as suggestions for future work (adaptive provisioning, distributed feature stores, and privacy preserving decisioning).