An OptiAssign-PSO Based Optimisation for Multi-Objective Multi-Level Multi-Task Scheduling in Cloud Computing Environment
Advanced Analytics for Disease Tracking & Remote Intravenous Injection Monitoring
DevKraft - Fueling Collaboration in Coding Challenges
Comparative Security and Compliance Analysis of Serverless Computing Platforms: AWS Lambda, Azure Functions, and Google Cloud Functions
Blockchain Healthcare Management using Patients
A Comprehensive Review of Security Issues in Cloud Computing
An Extended Min-Min Scheduling Algorithm in Cloud Computing
Data Quality Evaluation Framework for Big Data
An Architectural Framework for Ant Lion Optimization-based Feature Selection Technique for Cloud Intrusion Detection System using Bayesian Classifier
Be Mindful of the Move: A Swot Analysis of Cloud Computing Towards the Democratization of Technology
GridSim Installation and Implementation Process
A Survey on Energy Aware Job Scheduling Algorithms in Cloud Environment
Genetic Algorithm Using MapReduce - A Critical Review
Clustering based Cost Optimized Resource Scheduling Technique in Cloud Computing
Encroachment of Cloud Education for the Present Educational Institutions
Cloud computing is a prominent and evolving distributed computing paradigm that provides users with on-demand services through a network of diverse autonomous systems with flexible computational structures. The significance of task scheduling becomes evident, serving as a vital component to elevating cloud computing's overall performance. Streamlining cost-effective execution and optimizing resource utilization is a key objective, given the NP-hard nature of the task scheduling problem. Although numerous meta-heuristic techniques have been explored to address task allocation challenges, ample opportunities remain for the development of optimal strategies. This paper presents a state-of-the-art task assignment model that revolves around OptiAssign particle swarm optimization (PSO), with a strong emphasis on the crucial role played by efficient dependency handling and multi-level task scheduling. The primary aim of this model is to optimize the utilization of virtual machine capacities, simultaneously minimizing execution time, makespan, wait time, and overall execution costs within a variety of distributed computing systems. This novel algorithm showcases outstanding performance when compared to traditional approaches in task scheduling, highlighting the importance of skillful dependency management and the implementation of multi-level task scheduling strategies. The results of this study further affirm the effectiveness of the model in addressing the inherent complexities of scenarios involving intricate task dependencies and diverse scheduling priorities.
This system is proposed to address the challenges healthcare professionals face in monitoring patients who require intravenous infusion. This treatment method is commonly used for patients with dehydration, malnutrition, or those unable to take oral medications. The system enables remote monitoring of a patient's health and reduces the workload of healthcare professionals. The process begins with a doctor prescribing the necessary dose of drip bottles, which are then configured by the nurse accordingly. Once set for automatic operation, the system diligently calculates and monitors the fluid level and infusion rate using a load cell sensor. Additionally, the patient's vital signs, including heart rate, body temperature, and oxygen levels, are continuously monitored and stored in a database using cloud computing technology, enabling robust data analysis. The system provides real-time feedback on infusion parameters through an LCD screen. In the event of a patient's health deteriorating below a threshold level, a message is sent to the doctor, accompanied by a buzzer sounding and a red LED flashing at the patient's bedside. A supervisor continuously monitors the patient's health report, promptly informing the physician of any rapid changes and facilitating immediate treatment. This comprehensive system enhances patient care, streamlines medical monitoring processes, and enables swift responses to emergent health situations.
DevKraft is a cutting-edge cloud platform designed for collaborative coding experiences, leveraging AWS infrastructure. It offers specialized pages tailored for various activities: DevKraft for hackathons, DevKode for coding challenges, and QuizDeck for quizzes. DevKraft boasts streamlined deployment processes. It utilizes a robust DevOps stack, including Jenkins, Docker, Prometheus, Grafana, and Kubernetes. Developed with TypeScript and Node.js, this platform ensures seamless integration and deployment of projects, setting a new standard for collaborative coding platforms in the cloud environment. This paper explores DevKraft's architecture, highlighting its innovative integration with AWS and Firebase to deliver a robust user experience. By comparing DevKraft with traditional hackathon platforms, the paper demonstrates its potential to transform collaborative coding practices, foster innovation, and drive skill development within the developer community. Furthermore, it discusses the platform's impact on future hackathon frameworks and collaborative coding methodologies, emphasizing its role in advancing the field. The results and performance evaluations underscore DevKraft's effectiveness in enhancing security, scalability, and user engagement.
Serverless computing has revolutionized cloud services by abstracting infrastructure management, enabling developers to focus on application logic. This paper examines the security and compliance features of three major serverless platforms: AWS Lambda, Azure Functions, and Google Cloud Functions. By evaluating authentication mechanisms, data encryption practices, vulnerability management, and compliance certifications, this paper aims to provide a comparative analysis that informs businesses and developers on the most secure and compliant platform for their needs.
The secure exchange of medical information significantly benefits people's life equality, improving healthcare treatment. The interoperability of the entire healthcare ecosystem is constantly challenging with all the risk security of alternatives when it comes to balancing the alternative ecosystem. However, with the constant development of new blockchain technology, the evolution of the healthcare system is difficult to establish. Disruptions with the biomedical and clinical studies fields have brought about dramatic modifications in healthcare statistics control systems. This leads to a step forward in scientific statistics protection. This ability of blockchain to maintain an incorruptible, decentralized, and obvious log of all affected person statistics makes it a generation rife for protection applications. In this paper, blockchain technology is used for its major gain of being transparent. Since it's far private, concealing the identification of any people with complicated and secure codes, which could shield the sensitivity of scientific statistics, is achieved. The decentralized nature of this generation additionally lets in patients, medical doctors, and healthcare vendors to share equal records fast and safely. Extracting the applicable records from the statistics is feasible with the aid of making use of the KNN machine learning algorithm.
