56 research outputs found
A multi-core makespan model for parallel scientific workflow execution in cloud computational framework
Get PDFResearchers have shown a lot of potential in optimizing cloud-based workload-scheduling over the past few years. However, executing scientific workloads inside the cloud is time-consuming and costly, making it inefficient from both a financial and productivity standpoint. As a result, there are many investigations conducted, with the general trend being to speed up the rate of processing and establish a cost-effective system, whereby customers are billed according to their actual use. In addition, energy-consumption is capable of being reduced, especially if the available resources are heterogeneous; however, few investigations have optimized multi-core with analyzing makespan parameters collectively to fulfill the quality of service (QoS) and service level agreement (SLA) of the workload task. In this research, we introduce an optimal scheduling for a heterogeneous distributed cloud computing environment called task aware makespan optimized scheduler (TAMOS) that guarantees requirements across the task levels of scientific workflows. The energy and time required to carry out specific workflows are significantly reduced by using this TAMOS strategy. The TAMOS framework was studied using the scientific workflows namely, inspiral and sipht. When compared to the conventional method of scheduling work, our methodology used less energy and makespan
Digital Marketing ROI in the Era of Experience Economy: Evolving Methodologies and Unified Measurement Strategies
No full textThis research examines the evolving landscape of digital marketing ROI measurement within the context of the Experience Economy, driven by increasing channel complexity and technological advancements. It traces the historical progression from basic metrics to sophisticated multi-touch attribution (MTA) and marketing mix modeling (MMM). Employing a multi-method approach, including literature review and expert interviews, the study identifies key challenges like privacy regulations and data integration, and explores emerging trends like AI-driven analytics. A hierarchical ROI framework (tactical, operational, strategic) and temporal considerations, with a focus on measuring experiential value, are developed. Methodologies such as probabilistic path analysis and Bayesian models are evaluated for effectiveness. Ethical considerations regarding data privacy are also addressed. The findings provide actionable recommendations for optimizing digital marketing ROI, including the measurement of customer experiences, through comprehensive and ethical measurement practices
Input/output optimization scheduler for cloud-based map reduce framework
Get PDFHadoop MapReduce (HMR) provides the most common MapReduce (MR) framework, and it is available as open source. MR is a famous computational framework for evaluating unstructured, and semi-structured big data and executing applications in the past ten years. Memory and input/output (I/O) overhead are just two of the many problems affecting the current HMR scheduler system. This study aims to improve systems resource use including the processing of data in real-time by creating a memory I/O optimized scheduler (MIOOS) for HMR. The disk I/O seek can be reduced by using MIOOS, which analyzes the entire memory management. Additionally, the MIOOS makespan approach is used to reduce the occurrence of problems in intermediary tasks. Both the MIOOS approach and the current approach are assessed by using complex scientific workflow applications with extreme task inter-dependencies. Further, the comparison study demonstrates that the MIOOS framework outdoes the current approach regarding makespan and overall memory usage
Advancing Cybersecurity in Smart Factories Through Autonomous Robotic Defenses
No full textAdvancing Cybersecurity in Smart Factories Through Autonomous Robotic Defenses bridges the gap between the technical aspects of AI, industrial automation, and the evolving landscape of cybersecurity. This book provides readers with insight into the most recent advancements in AI-powered security tools, explore ethical and regulatory considerations, and learn practical strategies to protect complex systems from cyberattacks. Covering topics such as smart factories, wearable devices, and drone systems, this book is an excellent resource for cybersecurity professionals, computer engineers, industrial engineers, policymakers, policy regulators, professionals, researchers, scholars, academicians, and more
Use of Deep Learning in Personalized Medicine: Current Trends and the Future Perspective
No full textPrediction of Influenza-like Illness from Twitter Data and Its Comparison with Integrated Disease Surveillance Program Data
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