2025· International Journal of Applied Data Science & Modern Computing· 0 citations
TL;DR
This work proposes a scalable, intelligent, and resilient foundation for next-generation high-performance analytics and data-intensive applications that integrates adaptive resource management, intelligent workload scheduling, dynamic task migration, predictive analytics, and machine learning-based optimization to improve computational efficiency and responsiveness.
Abstract
The rapid growth of Big Data, IoT, cloud computing, edge intelligence, and AI has increased the demand for scalable and efficient analytical infrastructures. Traditional distributed computing systems often rely on fixed resource allocation and execution strategies, leading to performance issues, resource underutilization, higher latency, and limited scalability under dynamic workloads. To address these challenges, self-adaptive distributed computing models enable systems to autonomously monitor, analyze, and optimize operations in real time. The proposed framework integrates adaptive resource management, intelligent workload scheduling, dynamic task migration, predictive analytics, and machine learning-based optimization to improve computational efficiency and responsiveness. The architecture includes monitoring layers, decision engines, adaptation controllers, distributed resource managers, and analytics execution frameworks. Machine learning techniques such as reinforcement learning, deep neural networks, and predictive modeling support proactive adaptation by forecasting workload demands and optimizing scheduling decisions. Experimental results demonstrate significant improvements in resource utilization, throughput, scalability, fault tolerance, and execution time compared to traditional approaches. Self-healing capabilities further enhance resilience against node failures and network disruptions. Overall, self-adaptive distributed computing provides a scalable, intelligent, and resilient foundation for next-generation high-performance analytics and data-intensive applications.
This research proposes an AI-driven resource scheduling framework that integrates workload prediction, resource classification, intelligent scheduling, and continuous feedback mechanisms that aims to optimize multiple objectives, including cost reduction, execution efficiency, energy consumption, and SLA compliance.
Michael Anderson· International Journal of App...· 0 citations
Modern large-scale data pipelines support analytics, AI, ML, and real-time applications but face challenges related to scalability, resource utilization, reliability, and changing workloads. This paper proposes a reinforcement learning (RL)-based autonomous optimization framework that integrates RL agents with data orchestration platforms to continuously monitor pipeline states and optimize operations. The framework uses system metrics such as workload patterns, queue lengths, execution delays, resource consumption, and failure rates to make intelligent decisions on task scheduling, resource allocation, workload balancing, and fault recovery. Three RL algorithms—Q-learning, Deep Q-Networks (DQN), and Proximal Policy Optimization (PPO)—are evaluated. Experimental results demonstrate improved throughput, reduced latency, enhanced fault tolerance, and better resource efficiency compared to traditional rule-based approaches. The proposed framework enables adaptive, self-managing data pipelines that improve scalability, resilience, and operational efficiency across enterprise, cloud, and edge environments.
Rahul Mehta· International Journal of App...· 0 citations
Reinforcement learning-based adaptive resource management framework is proposed that enables cloud systems to autonomously learn optimal resource allocation policies through continuous interaction with the environment and significantly outperforms static and reactive baseline strategies in terms of resource utilization efficiency and response time stability.
Rajesh Sharma, Priya Natarajan· International Journal of Mac...· 0 citations
This study investigates energy-efficient distributed machine learning techniques, including federated learning, model compression, adaptive resource management, dynamic task offloading, and communication-efficient optimization, and proposes a distributed learning framework that integrates local model training, adaptive communication scheduling, gradient compression, and workload balancing to minimize energy consumption while maintaining learning accuracy.
Venkatesh Iyer· International Journal of App...· 0 citations
The rapid growth of data-intensive applications in scientific computing, enterprise analytics, and cloud services has increased the demand for efficient distributed data processing systems. Traditional scheduling methods like FCFS, Round Robin, and heuristic approaches often fail to meet the dynamic and heterogeneous requirements of modern environments. This paper proposes an intelligent workflow scheduling framework that improves performance through adaptive decision-making, predictive analytics, and machine learning. The system dynamically allocates tasks based on resource availability, workflow dependencies, and historical execution data, enabling it to anticipate bottlenecks and reassign tasks proactively. It also incorporates resource heterogeneity modeling and dependency-aware scheduling to reduce idle time and optimize execution. Performance is evaluated using metrics such as makespan, throughput, resource utilization, and fault tolerance, showing significant improvements over traditional methods. The framework also addresses key challenges like load balancing, scalability, energy efficiency, and fault tolerance. Overall, the proposed approach enhances system efficiency and scalability while supporting integration with emerging technologies such as edge computing and hybrid cloud environments, paving the way for more autonomous and resilient distributed scheduling systems.
D. Parnas· International Journal of Dat...· 0 citations
This study proposes a Context-Aware AI framework for dynamic cloud resource management that incorporates workload patterns, user behavior, network conditions, infrastructure health, and business objectives and provides a foundation for future technologies such as edge computing, IoT, 6G networks, and intelligent enterprise applications.
Richard Evans, Karen Lewis· International Journal of App...· 0 citations