Intelligent Portable Edge-Cloud Computing Ar-chitecture for Secure Data Analysis and Adaptive Resource Optimization Using AI-Driven Resource Scheduling
Aug 2026· International Journal for Research in Applied Science and Engineering Technology· 0 citations
TL;DR
This paper introduced an Intelligent Portable Edge – Cloud Computing Architecture (IPECA) that combines the portable computing hardware, AI-based workload prediction, adaptive resource optimization, container-based virtualization and secure edge-cloud collaboration into a single computing architecture.
Abstract
In recent years the growth of cloud computing, Internet of Things (IoT), artificial intelligence (AI) and edge intelligence has been increasing, and with it the need for portable, scalable and secure computing infrastructures that can process vast
amounts of data that is dispersed, and has very low latency. Traditional cloud infrastructures are typically based on central server deployments which can be costly to deploy, immobile, have potentially greater communication latency, and waste resources in
dynamic workload environments. In this paper, we introduced an Intelligent Portable Edge – Cloud Computing Architecture
(IPECA) that combines the portable computing hardware, AI-based workload prediction, adaptive resource optimization, container-based virtualization and secure edge-cloud collaboration into a single computing architecture. In conventional architectures, there is no intelligent resource orchestration mechanism, which can provide flexible allocation of computational resources
according to the property of workload, thermal status, energy consumption, network availability and so on. The architecture also
features an adaptive security layer leveraging multiple layers of authentication, secure communication protocols, blockchain for
integrity verification and on-the-fly system health monitoring to enhance cyber resilience. Simulations are conducted with varying workloads to gauge the effectiveness of the proposed architecture, and compared to traditional cloud and edge-cloud architectures with the metrics of latency, throughput, CPU utilization, response time, energy consumption, thermal efficiency, and
resource utilization. Experiments demonstrate significant energy savings, scalability, responsiveness of the system and efficiency
of computations using secure distributed processing. The suggested architecture is viable for the coming intelligent cloud infrastructures that are essential for smart city, industrial IoT, digital healthcare, education and enterprise computing.
As enterprise data grows across cloud, edge, and geographically distributed environments, traditional Edge computing has emerged as a transformative extension of cloud computing by addressing the limitations of latency, bandwidth, and scalability in real-time applications. With the increasing demand for ultra-low latency in autonomous vehicles, industrial automation, telemedicine, smart cities, and augmented reality, traditional cloud architectures face challenges due to centralized processing and network delays. Edge computing overcomes these issues by processing data closer to end devices, enabling faster decision-making and reduced communication overhead. This paper presents a comprehensive survey of edge computing architectures for ultra-low latency applications, covering key technologies such as 5G, Software-Defined Networking (SDN), Network Function Virtualization (NFV), Artificial Intelligence (AI), microservices, and container orchestration. It also examines major challenges, including resource management, interoperability, security, and energy efficiency. A multi-layer edge computing framework with intelligent task scheduling and dynamic resource allocation is proposed to optimize latency and resource utilization. Experimental findings demonstrate significant improvements over conventional cloud architectures, achieving over 70% reduction in end-to-end latency and 65% improvement in resource efficiency. The study concludes that intelligent edge computing architectures will play a vital role in supporting future real-time applications and next-generation 6G-enabled digital ecosystems.
Alan Bundy· International Journal of Mod...· 0 citations
This paper presented an Intelligent Edge Computing Framework for Secure, Low-Latency, and Energy-Efficient Smart Devices that integrates edge intelligence, adaptive task scheduling, resource-aware computation, secure communication, and cloud-assisted services to address the limitations of conventional cloud-centric architectures. By processing data closer to smart devices, the proposed framework significantly reduces latency, minimizes network bandwidth consumption, improves resource utilization, and enables faster real-time decision-making while ensuring data privacy and security. The experimental results demonstrate superior performance in terms of classification accuracy, ROC-AUC, Average Precision, execution time, and computational efficiency compared with existing cloud-based and edge computing approaches. The proposed framework provides a scalable, reliable, and energy-efficient solution for diverse Internet of Things (IoT) applications, including smart healthcare, industrial automation, intelligent transportation, and smart homes. Future work will focus on integrating federated learning, blockchain-enabled security, and next-generation 6G edge intelligence to further enhance scalability, privacy preservation, and autonomous decision-making in large-scale smart device ecosystems.
Kolipaka Vinay, Valusa Venkat Sai Kumar, D. A. Kumar· International Journal of Sci...· 0 citations
The rapid growth of Internet of Things (IoT) devices has increased the volume of real-time data generated by sensors,
smart devices, industrial controllers, and healthcare monitoring systems. Cloud-centric processing provides scalable
computation, but it often introduces high communication latency, bandwidth overhead, and energy cost for delay-sensitive IoT
streams. Edge and fog computing reduce response time by moving computa-tion closer to data sources, but efficient
workload placement across edge, fog, and cloud resources remains a challenging scheduling problem. This paper proposes an
adaptive cloud-edge scheduler using lightweight artificial intelligence models for real-time IoT stream placement. The
framework dynamically selects edge, fog, or cloud execution based on stream priority, latency deadline, bandwidth availability,
workload complexity, CPU uti-lization, memory utilization, queue length, energy availability, and historical scheduling success.
Lightweight models such as Decision Tree, Logistic Regression, Random Forest, and Tiny Neural Network are considered to
support fast inference under resource constraints. A latency-energy-aware cost function vali-dates model predictions and
improves runtime decision quality. The framework also integrates explainable scheduling decisions and a human-in-the-loop
override mechanism for operational governance. Experimental evaluation using a working prototype demonstrates adaptive
distribution of IoT streams across edge, fog, and cloud layers, with 11 observed scheduling decisions, 55% success rate,
274.796 ms average latency, and 0.111 J average energy consumption. The results indicate that combining lightweight AI
prediction with cost-aware validation improves scheduling flexibility, transparency, and practical applicability in real-time IoT
systems.
Munesula Venkatesh, DP Saravanan· International Journal for Re...· 0 citations
Cloud computing paradigm enables the delivery of computing, storage, and applications as services as subscription-oriented services to end users over the Internet. It reduces the need for highly expensive computing infrastructure, dedicated storage servers, and software applications to be procured, deployed, and managed as in house capabilities. The amount of data generated by enterprise and business applications has increased exponentially. Addressing and dealing with enormous datasets is a challenging and very time-consuming task that necessitates a high level of computing infrastructure to enable proper data processing and analysis. This study aims to compare and analyze the different aspects of ultra-large data storage systems in cloud computing. The description, features, and classification of cloud storage systems are discussed, along with certain cloud computing considerations. Relationships between big data and cloud computing are also explored, as are ultra-large storage systems and Hadoop technology. Furthermore, research concerns such as dynamicity, scalability, availability, data integrity, self-organizing, data quality, data diversity, privacy, and legal and regulatory issues have been investigated. The sole motivation of this study has been thoroughly explained with the help of a mind-mapping diagram of cloud-oriented data storage (CODS) elements. The seven major elements of CODS—the data model, data consistency, data replication, data scalability, data integration, data virtualization, and transaction—have been systematically discussed in this study. This study also highlights some open research issues related to ultra-large storage systems, which summarize sufficient research efforts.
Ajay Kumar, S. Bawa, Neeraj Kumar et al.· ACM Computing Surveys· 0 citations
The increase in the number of firms adopting multi-cloud in their business operations requires progressive approaches to efficient security and resource utilization. This paper provides a new approach where AI is used to improve operational performance and security in multi-cloud environments. The proposed solution produces solutions to important issues related to cloud-network integration through a balance the processing time, energy use, and cost. As suggested, based on the principle of AI and expert rule system the work load of different cloud can be nicely integrated with the help of this framework and it also ensures secure integration of different cloud infrastructure. Probably, one of the most important strengths of the proposed strategy is its capability to make smart real-time decisions with usage of dynamic information and changing service requirements. This is consistent with the logic of the network following the cloud and the cloud responding to the data, and minimizes latency and leverages ER resources. This locks-in operational efficiencies especially within contemporary multicloud landscapes which are rather convoluted and demanding of resources.
S. Vijayanand, T. Ramana, Raju Bandaru· 2026 International Conferenc...· 0 citations
Cloud computing has emerged as a transformative computing paradigm by providing scalable, on-demand, and cost-effective access to computing resources over the Internet. It enables organizations to utilize computing infrastructure, storage, networking, and software services without maintaining extensive physical hardware, thereby improving operational efficiency and reducing infrastructure costs. The widespread adoption of cloud computing has accelerated digital transformation across various domains, including healthcare, education, finance, manufacturing, and government. Despite its numerous advantages, cloud computing continues to face significant challenges related to security, privacy, interoperability, vendor lock-in, latency, resource management, and energy efficiency. Moreover, the rapid growth of emerging technologies such as artificial intelligence, edge computing, fog computing, blockchain, serverless computing, and quantum computing is reshaping cloud environments by enhancing intelligence, scalability, security, and sustainability. This review paper presents a comprehensive overview of cloud computing, critically examines the major challenges affecting its adoption, discusses recent technological advancements, and identifies future research directions for developing intelligent, secure, and sustainable cloud computing systems. The findings presented in this paper provide valuable insights for researchers, practitioners, and policymakers seeking to understand the evolving landscape of cloud computing and its future prospects.
S. Jyothi· International Journal of Res...· 0 citations
Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.
