Search Results (88)

High-performance resource orchestration and robust data security represent critical, often competing, operational objectives in modern cloud computing architectures. This study presents a unified infrastructure framework designed to reconcile these requirements by integrating a Geographically Aware Placement Algorithm (GAPA) with an Artificial Intelligence-Driven Monitoring system (AIDAM). GAPA dynamically optimizes workload distribution based on regional server capacity and geographic proximity, while AIDAM leverages deep unsupervised autoencoders for real-time anomaly detection and threat mitigation. The framework was evaluated via deterministic simulation using production traces from the Google Cluster Data (2019) corpus under a systematic injection of volumetric Distributed Denial-of-Service (DDoS) anomalies. The empirical results demonstrate a 92% macro-averaged threat detection accuracy rate against low-and-slow traffic variations alongside a minimal cryptographic processing latency overhead of 3–5% relative to an unencrypted baseline scheduling configuration. Furthermore, the integrated pipeline achieved a 25% reduction in end-to-end network latency compared to traditional non-geographically aware heuristic models. These findings demonstrate that cloud infrastructure efficiency and security resilience can be simultaneously enhanced without requiring comprehensive physical re-engineering. Full article

With the rapid growth of data center (DC) energy consumption and the large-scale integration of renewable energy, DCs increasingly face time-varying power upper-bound constraints jointly shaped by grid power supply capability, renewable energy fluctuations, and demand response mechanisms. Meanwhile, DC power consumption exhibits a typical information-load-driven characteristic. The computing tasks hosted by virtual machines affect server-side IT power consumption through resource utilization states such as CPU, memory, disk I/O, and network I/O, and are further coupled with non-IT auxiliary power consumption from cooling, power distribution, and networking equipment. In such cyber–physical operation scenarios, physical-layer sensing data and hypervisor-level virtualization monitoring data jointly provide the state basis for power estimation, power warning, and migration decisions. To address the mismatch between dynamic power upper bounds and time-varying information loads, this paper investigates the information load scheduling problem under constrained power loads and proposes a two-stage virtual machine (VM) migration optimization framework. In the VM selection stage, a Multi-Factor Balanced (MFB) algorithm is designed. By introducing a warning-line trend model based on the arctangent function, MFB comprehensively considers resource utilization, power load variation trends, and service level agreement (SLA) violation levels to dynamically identify candidate VMs for migration. In the VM placement stage, a Multi-Factor Equilibrium Ant Colony Optimization (MFEACO) algorithm incorporating a Random Roulette Wheel (RRW) selection mechanism is proposed. By constructing normalized multi-dimensional equilibrium factors, MFEACO coordinates the trade-off among energy consumption, load balancing, and SLA violations. Simulation experiments are conducted on an improved CloudSim platform using real-world cluster trace data from Google and Alibaba. The results show that, while satisfying dynamic power constraints, the proposed MFB–MFEACO framework achieves a favorable comprehensive trade-off among energy consumption control, SLA violation suppression, and migration reduction. Compared with traditional heuristic methods and a power-constrained genetic algorithm baseline, the proposed framework demonstrates better dynamic adaptability and scheduling stability. Full article

The ongoing evolution of Industry 4.0 technologies necessitates novel and effective modes of human–machine interaction within production environments. This work presents a modular approach to the design and implementation of graphical user interfaces (GUI) in mixed reality, leveraging the Asset Administration Shell (AAS) standard. The proposed method enables the dynamic rendering of GUI elements in a Mixed Reality setting based on structured data retrieved from an AAS server. Developed for the Microsoft HoloLens 2 using the Unity engine and the Microsoft Reality Toolkit 3 (MRTK3), the system allows for the spatial placement of interface components either at predefined coordinates or in relation to specific elements of a production line model. Additionally, it incorporates a real-time distributed architecture utilizing OPC UA PubSub and MQTT protocols for processing and visualising live data. The prototype demonstrates the viability of using AAS as a flexible framework for defining and generating GUI components in immersive environments and lays the groundwork for further research into standardised, easily deployable user interface solutions for industrial applications. Full article

With the rapid growth of edge computing applications, optimizing both edge server placement and task offloading decisions is critical for minimizing system latency in edge–cloud environments. However, these two problems are tightly coupled and jointly form a binary non-linear programming (BNLP) problem that is NP-hard. To address this challenge, this paper proposes a novel hybrid meta-heuristic algorithm with alternating iteration, which decouples the joint optimization into two interdependent subproblems: edge server placement and task offloading. These subproblems are solved alternately using particle swarm optimization (PSO) for placement and a genetic algorithm (GA) for offloading, respectively. PSO efficiently explores the discrete placement space under bound constraints, while GA effectively navigates the high-dimensional binary offloading space. Compact encoding schemes are designed to inherently satisfy problem constraints, reducing search overhead and improving convergence. The overall algorithm exhibits polynomial-time complexity, making it scalable for practical deployments. Extensive experiments comparing the proposed method against ten baseline algorithms demonstrate that it achieves the best latency with the smallest standard deviation. The results validate the effectiveness, robustness, and scalability of the proposed alternating iterative hybrid meta-heuristic approach for joint edge server placement and task offloading optimization. Full article

The fast growth of AI and large-scale industrial compute infrastructures has led to unsustainable increases in energy consumption and greenhouse gas emissions on a global scale, creating serious sustainability issues in today’s modern cloud computing. The proposed hybrid framework called the Hierarchical Clustering Deep Q-Network Carbon-Aware Placement System (HC-DQNCAPS) was developed as a means to combine energy efficient design with carbon-aware deployment strategies to support intelligent, adaptive and environmentally sustainable workload scheduling and resource allocation for industrial computing systems. This framework uses real time metrics of resource utilization (server and network) and information about carbon intensity to improve the distribution of workloads across geographically distributed cloud and hybrid infrastructures through both Hierarchical Agglomerative Clustering (HAC)- and Deep Q-Network (DQN)-based reinforcement learning models. Multi-objective optimization is leveraged to optimize energy usage, carbon emissions and SLA violations while optimizing resource utilization. The HC-DQNCAPS architecture significantly outperformed such work practices as FCFS, Energy-Aware VM Allocation, Carbon-Unaware RL, PPO, DDQN and MADRL Scheduling, with SLA breaches always less than 5%, and with energy utilization consistently reduced by 30–35%, carbon emissions reduced by 25–30% and resource utilization increased by +20%. The model’s significance and stability were demonstrated using both ANOVA and Wilcoxon signed-rank statistical tests to be significant (p < 0.05) at 95% confidence intervals. Overall, the findings show that there is potential for implementing carbon-aware AI methods in order to maintain economic viability for all computing systems involved in the industrial cloud. Full article

In the traditional $(K,L,M_T,M_U,N)$ partially connected linear network, a central server stores a library of N files and connects to $K+L-1$ transmitters, each equipped with a cache of size $M_T$. Each user is connected to L neighboring transmitters and is equipped with a local cache of size $M_U$. Motivated by practical scenarios in which users can access multiple cache nodes, this paper considers a $(K,L,r,M_T,M_C,N)$ multiaccess cache-assisted partially connected linear network, where each user can access r neighboring cache nodes under a cyclic wrap-around topology, and each cache node has a storage capacity of $M_C$. We propose a general construction framework based on placement delivery arrays (PDAs). The analysis shows that, when the Maddah-Ali and Niesen (MN) scheme is employed and r is sufficiently large, the achieved normalized delivery time (NDT) approaches that of existing schemes for the traditional partially connected linear network. Moreover, under the same aggregate cache size accessible to each user, numerical results demonstrate that, as the cache size ratio increases, the gap between the NDT achieved by the proposed scheme and that of the traditional partially connected linear network scheme gradually diminishes, while the proposed scheme requires a smaller subpacketization level. Full article

Edge server placement (ESP) is a critical determinant of service quality in edge–cloud computing systems, yet existing solutions often neglect the inherent collaboration between edge and cloud, leading to suboptimal performance under dynamic workloads. To address this gap, this paper proposes a novel meta-heuristic edge server placement algorithm based on the Coati Optimization Algorithm (COA). We first formulate the ESP problem as a constrained binary nonlinear programming model that explicitly incorporates edge–cloud collaboration, aiming to minimize the average request processing delay. The proposed COA-based solver features a compact one-dimensional encoding scheme that simultaneously represents server placement and request offloading decisions, a tailored boundary correction mechanism to enforce coverage and atomicity constraints, and a balanced exploration–exploitation strategy inspired by coatis’ natural hunting and escape behaviors. Extensive simulations are conducted, comparing the proposed algorithm against ten representative heuristic and meta-heuristic algorithms, including GA, PSO, DE, GWO, and their variants. The experimental results demonstrate that our algorithm significantly outperforms all compared methods in terms of the mean, minimum, and standard deviation of the overall average processing delay. Specifically, it achieves a 98.2% reduction in the mean delay relative to suboptimal algorithms while maintaining near-zero variance, confirming its effectiveness, efficiency, and robustness. The proposed algorithm provides a promising solution for service providers to enhance quality of service through optimal edge server deployment and request offloading under edge–cloud collaboration. Full article

Forest fires are a major concern due to their significant impact on the environment, economy, and wildlife habitats. Efficient early detection systems can significantly mitigate their devastating effects. This paper provides a comprehensive review of forest fire detection (FFD) techniques and traces their evolution from basic lookout-based methods to sophisticated remote sensing technologies, including recent Internet of Things (IoT)- and Unmanned Aerial Vehicle (UAV)-based sensor network systems. Historical methods, characterized primarily by human surveillance and basic electronic sensors, laid the foundation for modern techniques. Recently, there has been a noticeable shift toward ground-based sensors, automated camera systems, aerial surveillance using drones and aircraft, and satellite imaging. Moreover, the rise of Artificial Intelligence (AI), Machine Learning (ML), and the IoT introduces a new era of advanced detection capabilities. These detection systems are being actively deployed in wildfire-prone regions, where early alerts have proven critical in minimizing damage and aiding rapid response. All FFD techniques follow a common path of data collection, pre-processing, data compression, transmission, and post-processing. Providing sufficient power to complete these tasks is also an important area of research. Recent research focuses on image compression techniques, data transmission, the application of ML and AI at edge nodes and servers, and the minimization of energy consumption, among other emerging directions. However, to build a sustainable FFD model, proper sensor deployment is essential. Sensors can be either fixed at specific geographic locations or attached to UAVs. In some cases, a combination of fixed and UAV-mounted sensors may be used. Careful planning of sensor deployment is essential for the success of the model. Moreover, ensuring adequate energy supply for both ground-based and UAV-based sensors is important. Replacing sensor batteries or recharging UAVs in remote areas is highly challenging, particularly in the absence of an operator. Hence, future FFD systems must prioritize not only detection accuracy but also long-term energy autonomy and strategic sensor placement. Integrating renewable energy sources, optimizing data processing, and ensuring minimal human intervention will be key to developing truly sustainable and scalable solutions. This review aims to guide researchers and developers in designing next-generation FFD systems aligned with practical field demands and environmental resilience. Full article

In the traditional $(H,r,M,N)$ combination network, a central server storing N files communicates with $K=(\genfrac{}{}{0pt}{}{H}{r})$ users through H cache-less relays. Each user has a local cache of size M files and is connected to a distinct subset of r relays. This paper studies the $(H,r,L,\mathrm{\Lambda },M,N)$ combination network with multi-access caching, where $\mathrm{\Lambda }$ cache nodes (each of size M files) are available and each user can access L cache nodes. We show that in the regime $H\ge \mathrm{\Lambda }$ and $r\ge L$, an achievable design can be obtained via a group-wise operation, which reduces the scheme design within each group to an effective $(\mathrm{\Lambda },L,L,\mathrm{\Lambda },M,N)$ instance. For the case $\mathrm{\Lambda }=H$ and $L=r$, we further propose an explicit coded caching scheme constructed via two array-based representations (a cache-node placement array and a user-retrieve array) and a derived combinatorial placement delivery array (CPDA) based on the Maddah-Ali–Niesen (MN) placement strategy. Numerical comparisons using the user-retrievable cache ratio as the evaluation metric indicate that the proposed scheme approaches the converse bound of the traditional combination network, and the performance gap diminishes as the cache ratio increases. Full article

Vagococcus species have been isolated from diverse environments, including aquatic, terrestrial, food-associated, and clinical sources; however, plant- and flower-associated representatives remain poorly characterized at the genomic level. In this study, we report the complete genomic sequence and analysis of Vagococcus sp. JNUCC 83, isolated from flowers of Camellia japonica collected on Jeju Island, Republic of Korea. The genome comprises a single circular chromosome of 2,472,896 bp with a GC content of 33.5 mol% and was assembled at high depth (555.43×), resulting in a high-quality complete genome. Genome-based phylogenomic analysis using the Type (Strain) Genome Server (TYGS) showed that strain JNUCC 83 forms a distinct lineage within the genus Vagococcus. Digital DNA–DNA hybridization (dDDH) values were far below the 70% species threshold, and 16S rRNA gene-based phylogeny consistently supported its independent placement, suggesting that JNUCC 83 represents a previously undescribed genomic species. Functional annotation based on EggNOG/COG analysis indicated the enrichment of genes involved in core metabolism and genome maintenance, while antiSMASH analysis identified a terpene-precursor-type biosynthetic locus encoding a polyprenyl synthase. Overall, this study expands the genomic understanding of flower-associated Vagococcus lineages and provides a foundation for future investigations into their ecological roles and potential applications as plant-derived microbial resources. Full article

Dynamic edge-server allocation in mobile edge computing (MEC) networks is a challenging multi-objective optimization problem due to highly dynamic user demands, spatiotemporal traffic variations, and the need to simultaneously minimize service latency and workload imbalance. Existing heuristic and metaheuristic-based approaches for this problem often suffer from premature convergence, limited exploration–exploitation balance, and inadequate adaptability to dynamic network conditions, leading to suboptimal edge-server placement and inefficient resource utilization. Moreover, most existing methods lack memory-aware search mechanisms, which restrict their ability to capture long-term system dynamics. To address these limitations, this paper proposes a Fractional-Order Multi-Objective African Vulture Optimization Algorithm (FO-MO-AVOA) for dynamic edge-server allocation. By integrating fractional-order calculus into the standard multi-objective AVOA framework, the proposed method introduces long-memory effects that enhance convergence stability, search diversity, and adaptability to time-varying workloads. The performance of FO-MO-AVOA is evaluated using realistic MEC network scenarios and benchmarked against several well-established metaheuristic algorithms. Simulation outcomes reveal that FO-MO-AVOA achieves 40–46% lower latency, 38–45% reduction in workload imbalance, and up to 28–35% reduction in maximum workload compared to competing methods. Extensive experiments conducted on real-world telecom network data demonstrate that FO-MO-AVOA consistently outperforms state-of-the-art multi-objective optimization algorithms in terms of convergence behaviour, Pareto-front quality, and overall system performance. Full article

This paper addresses two safety issues regarding smart vehicles: that of intoxicated drivers (one of the most common causes for car accidents) and that of theft. More specifically, it presents the design and implementation of an intelligent system based on the Arduino-Mega2560 board. The issue of intoxicated drivers is addressed by using an MQ3 alcohol sensor that is capable of sensing the driver’s breath and a relay that immobilizes the vehicle if it detects alcohol above the permissible limit. Regarding theft, there are two safety layers: the first layer uses a fingerprint sensor which would not let the vehicle move unless the user is authenticated, while the second layer includes a GPS module that collects the information about the vehicle’s location and, through an incorporated GSM module, transmits the location data to an Internet-of-Things (IoT) server. The main contribution of the proposed system is that it treats two essential safety-security issues (drunk driving and theft) at the same time with the additional merits of low-cost implementation and easy placement and use within a vehicle. Full article

In large-scale IoT environments, two major challenges—limited edge storage resources and complex task dependencies—make efficient management of service placement and task offloading particularly difficult. Existing approaches often optimize these two aspects independently while overlooking their tight interrelationship, resulting in poor performance in dynamic settings. To address this co-optimization challenge, we propose a Hierarchical Deep Q-Network (HDQN) framework that simultaneously manages service placement and task offloading in task-dependent MEC systems. HDQN divides the decision process into two levels: a meta-controller for long-term service placement and resource planning, and a subcontroller that makes real-time task offloading decisions based on the latest system state. This two-layer structure enables the framework to efficiently adapt to changing conditions while meeting both dependency and resource constraints. Evaluation across diverse experimental conditions—including varying numbers of users, MEC servers, communication rates, and service types—demonstrates that our proposed HDQN framework achieves a significant enhancement in task latency optimization compared to mainstream advanced algorithms like DDPG and DQN, underscoring its superior performance. Full article

With the emergence of Industry 5.0 and explosive data growth, replica allocation has become a critical issue in edge computing systems. Current methods often focus on placing replicas on edge servers near terminals, yet this may lead to edge node overload and system performance degradation, especially in large 6G edge computing communities. Meanwhile, existing terminal-based strategies struggle due to their time-varying nature. To address these challenges, we propose the HRCD, a hybrid replica method based on community division. The HRCD first divides time-varying terminals into stable sets using the community division algorithm. Then, it employs fuzzy clustering analysis to select terminals with strong service capabilities for replica placement while utilizing uniform distribution to prioritize geographically local hotspot data as replica data. Extensive experiments demonstrate that the HRCD effectively reduces data access latency and decreases edge server load compared to other replica strategies. Overall, the HRCD offers a promising approach to optimizing replica placement in 6G edge computing environments. Full article

The increasing demand for low-latency, computationally intensive vehicular applications, such as autonomous navigation and real-time perception, has led to the adoption of cloud–edge–vehicle infrastructures. These applications are often modeled as Directed Acyclic Graphs (DAGs) with interdependent subtasks, where precedence constraints enforce causal ordering while allowing concurrency. We propose a task offloading framework that decomposes applications into precedence-constrained subtasks and formulates the joint scheduling and offloading problem as a Markov Decision Process (MDP) to capture the latency–energy trade-off. The system state incorporates vehicle positions, wireless link quality, server load, and task-buffer status. To address the high dimensionality and sequential nature of scheduling, we introduce DepSchedPPO, a dependency-aware sequence-to-sequence policy that processes subtasks in topological order and generates placement decisions using action masking to ensure partial-order feasibility. This policy is trained using Proximal Policy Optimization (PPO) with clipped surrogates, ensuring stable and sample-efficient learning under dynamic task dependencies. Extensive simulations show that our approach consistently reduces task latency, energy consumption and QOS compared to conventional heuristic and DRL-based methods. The proposed solution demonstrates strong applicability to real-time vehicular scenarios such as autonomous navigation, cooperative sensing, and edge-based perception. Full article