Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Task scheduling in cloud computing environment becomes difficult when complexity level of dispute, including task count and computing resources, rises with user growth. Solving this, an enhanced task scheduling (ETS) model with optimised artificial intelligence driven by swarm is proposed in paper. In proposed method, supervised machine learning algorithm, artificial neural networks (ANN) with swarm-based moth flame optimisation (MFO) is used to balance scheduling. MFO optimises by separating out virtual machines (VMs) considering basic properties like CPU utilisation, memory and bandwidth. ETS model is optimised based on resource allocation and balancing issues using back-propagation algorithm (BPA) with ANN (ANN-BPA) to analyse scheduling and problem identification mechanism. Efficiency of ETS model is assessed, focusing on aspects such as task allocation, task completion, execution time and energy consumption. The ANN-BPA-based task scheduling model outperforms by present technique and ANN-based model, which enhances resource utilisation by 7.54% and decreases completion time by 0.6 s.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

The research focuses on the main issues that arise in cloud computing regarding how to schedule tasks and pick the right virtual machines due to growing need for virtual resources. Therefore, an innovative task scheduling model is proposed by integrating multi-criteria decision making (MCDM) and the shortest job first (SJF) technique. An approach using VIKOR in MCDM is followed to highlight tasks by their queuing, task and resource importance, thereby cutting down waiting time. If priorities are equal, SJF algorithm prefers to select shorter tasks to ensure no conflicts occur. An original metaheuristic algorithm called chaotic artificial bee colony with quantum (CABCQ) is proposed to ensure best choice of VMs by considering the time needed to execute, transfer and process tasks. Cloud task assignment and VM usage improve due to the models, which boosts Python CloudSim's performance in terms of makespan, latency, user priority, energy consumption, computation time and cost.

The exponential growth of streaming data from diverse sources including Internet of Things devices, web applications, and database change data capture systems has created unprecedented challenges in data management, analytics, and governance. Traditional batch-oriented data architectures struggle to meet the demands of real-time analytics while maintaining data quality, security, and compliance requirements. This research presents a comprehensive cloud-native data analytics platform that integratesApache Kafka for distributed messaging, Apache Flink for stream processing, Delta Lake for medallion architecture storage, and Feast feature store for machine learning operationalization, all unified under a robust governance framework leveraging Great Expectations, AWS security services, and enterprise observability tools.The proposed architecture processes over 340,000 events per second across multiple data sources, implements a three-tier medallion storage pattern with automated quality validation, and achieves sub-10-millisecond latency for online feature serving while maintaining Ramesh Pandipati https://iaeme.com/Home/