Intelligent Resource Management Research Articles

Blockchain Based Intelligent Resource Management in Distributed Digital Twins Cloud

Blockchain Based Intelligent Resource Management in Distributed Digital Twins Cloud

Sustainable Irrigation Systems in Vineyards: A Literature Review on the Contribution of Renewable Energy Generation and Intelligent Resource Management Models

Sustainable Irrigation Systems in Vineyards: A Literature Review on the Contribution of Renewable Energy Generation and Intelligent Resource Management Models

Smart Energy Systems Based on Next-Generation Power Electronic Devices

Power electronics plays a key role in the management and conversion of electrical energy in a variety of applications, including the use of renewable energy sources such as solar, wind and hydrogen energy, as well as in electric vehicles, industrial technologies, homes and smart grids. These technologies are essential for the successful implementation of the green transition, as they help reduce carbon emissions and promote the production and consumption of cleaner and more sustainable energy. The present work presents a new generation of power electronic devices and systems, which includes the following main aspects: advances in semiconductor technologies, such as the use of silicon carbide (SiC) and gallium nitride (GaN); nanomaterials for the realization of magnetic components; using a modular principle to construct power electronic devices; applying artificial intelligence techniques to device lifecycle design; and the environmental aspects of design. The new materials allow the devices to operate at higher voltages, temperatures and frequencies, making them ideal for high-power applications and high-frequency operation. In addition, the development of integrated and modular power electronic systems that combine energy management, diagnostics and communication capabilities contributes to the more intelligent and efficient management of energy resources. This includes integration with the Internet of Things (IoT) and artificial intelligence (AI) for automated task solving and work optimization.

Application of VIC-WUR model for assessing the spatiotemporal distribution of water availability in anthropogenically-impacted basins

Climate change, rapid increase in population, and urbanization have given rise to over-exploitation and shortage of freshwater resources. Acquiring accurate representations of fresh water availability is therefore crucial to sustainable development. Based on the underlying hydrological processes and storage types, fresh water can be divided into blue and green water components. Hence, quantification of the spatio-temporal distribution of blue and green water requires estimating the components of terrestrial water cycle. In this study, a framework is proposed to evaluate the water scarcity using the blue and green water concept. In this framework, the Wageningen Institute and Research (WUR) version of the Variable Infiltration Capacity Model (VIC) is employed to simulate terrestrial water cycle. Compared to VIC model, VIC-WUR model employs additional modules to account for sectoral water demands, return flows, and groundwater withdrawals, however, very limited number of its applications have been reported so far. The Divandareh-Bijar (DBJ) basin in west of Iran is selected because it has experienced severe anthropogenic alterations in the past three decades. To address precipitation and parameter uncertainty, the VIC-WUR model is separately calibrated for three precipitation datasets. As a part of this framework, the calibrated model is used to simulate blue and green water components and their corresponding scarcity metrics for the first time. According to the results, blue and green water resources in the DBJ are relatively scarce. The results also highlight the importance of considering anthropogenic impacts on the spatiotemporal dynamics of green and blue water resources. Additionally, quantification of the uncertainty in blue and green water components can help produce more reliable estimations for intelligent management of water resources. The ANalysis Of VAriance (ANOVA) method is therefore used to quantify the contribution of three major factors, including precipitation inputs, model parameters, and the interaction of these two, on the uncertainty of calculated water scarcity metrics. Structural uncertainty is implicitly addressed through the use of the modified version of the VIC model. The results indicate seasonality of these contributions for the two metrics under investigation and the differences in relative contribution of different sources to the overall uncertainty. The proposed framework could be employed to identify water stress in various spatiotemporal scales across the globe.

BIO-SD: A Blockchain-Empowered Intelligent Resource Management for Symbiotic Devices

BIO-SD: A Blockchain-Empowered Intelligent Resource Management for Symbiotic Devices

Design of an Iterative Method for Hybrid Device-to-Device and Network Integration via Dynamic Spectrum Sharing and Intelligent Resource Management

In the evolving landscape of wireless communication, the integration of Device-to-Device (D2D) communication within traditional network infrastructures emerges as a pivotal solution to meet the burgeoning demand for efficient, scalable, and reliable communication. Despite the promise of D2D to augment network capabilities, existing frameworks often falter in optimizing resource utilization, ensuring privacy and security, and maintaining high-quality service amidst dynamic network conditions and escalating data traffic. Addressing these limitations, this work introduces a comprehensive hybrid model that synergizes D2D communication with network integration through the innovative application of Dynamic Spectrum Sharing (DSS), Reinforcement Learning (RL)-based Resource Allocation, Mobile Edge Computing (MEC), Differential Privacy Techniques, Swarm Intelligence-based Routing, Cognitive Radio Networks, Self-Healing Network Protocols, and Low-Power Communication Protocols. DSS is employed to dynamically allocate spectrum, enhancing communication flexibility between D2D and network modes. RL algorithms adaptively manage resources, optimizing network performance in real-time scenarios. MEC decentralizes data processing, slashing latency and bandwidth demands. Differential privacy fortifies data security within D2D interactions in different communication scenarios. Swarm intelligence and cognitive radios bolster network resilience and spectral efficiency through self-organized routing and adaptive spectrum access. Self-healing protocols and energy-efficient designs ensure robust, sustainable network operations. The proposed model markedly elevates network efficiency, slashes latency, and augments scalability by intelligently offloading traffic, distributing computing tasks, and preserving user privacy. Its implementation fosters a resilient, self-organizing network fabric capable of meeting contemporary demands. The impacts of this work are profound, offering a blueprint for next-generation wireless networks that are more adaptive, secure, and sustainable, thereby significantly enhancing user experience and trust in D2D communication systems.

Intelligent resource management in 5G/6G network by adopting edge intelligence for higher education systems

The Internet of Things (IoT) is expected to have a positive impact on various sectors, such as manufacturing, users, and operators. Due to the capabilities of smart objects to share their contents and interact with their social networks, the combined Social Internet of Educational Things (SIoET) for smart education is expected to provide users with more efficient services. The primary objective of this paper is to provide an efficient resource management by adopting edge intelligence technique for higher education system workloads on next-generation networks. As the number of tasks handled by these interconnected devices continues to rise, there is an escalating concern regarding resource competition and power consumption within the network. To mitigate these challenges, we propose an innovative edge intelligence-based resource allocation approach to intelligently allocate resources while minimizing latency and optimizing power consumption with increasing task volumes. The problem is dissected into two core sub-problems: computation resource optimization and power optimization. Leveraging the concept of Lagrangian and Bisection methods, our proposed algorithm, implemented within a Device-to-Device (D2D) -assisted edge intelligence network, demonstrates remarkable performance improvements. Network latency and energy consumption is reduced by up to 57.9 % and 82.3 % compared to the benchmark schemes. These findings hold promising implications for the integration of this work into higher education systems driven by SIoET. Furthermore, simulations indicate that the system maintains robust performance even in dynamic urban environments characterized by random device movement.

>Water quality prediction of artificial intelligence model: a case of Huaihe River Basin, China.

Accurate prediction of water quality contributes to the intelligent management of water resources. Water quality indices have time series characteristics and nonlinearity, but the existing models only focus on the forward time series when long short-term memory (LSTM) is introduced and do not consider the parallel computation on the model. Owing to this, a new neural network called LSTM-multihead attention (LMA) was constructed to predict water quality, using long short-term memory to process time series data and multihead attention for parallel computing and extracting feature information. Additionally, water quality indices have the issues of multiple data types and complex data correlations, as well as missing data and abnormal data problems in water quality data. In order to solve these problems, this study proposes a water quality prediction model called GRA-LMA-based linear interpolation, gray relational analysis and LMA. Two experiments are carried out to verify the predictive performance of the GRA-LMA with the water quality data of the Huaihe River Basin as a case study sample. The first experiment focuses on data processing, including the processing of missing data and abnormal data of water quality data, and the correlation analysis of water quality indices. Linear interpolation is adapted to process the missing data, while a combination of boxplot and histogram is adopted to analyze and eliminate the abnormal data, which is then repaired the abnormal data with linear interpolation. The gray relational analysis is adopted to calculate the correlation between different water quality indices, and water quality indices with high correlation are retained to determine the input variables of the water quality prediction model. The data processing results demonstrate that repairs can be made using linear interpolation without altering the pattern of data change and the model by using the gray relational analysis to reduce the quantity of data it needs as input. In the second experiment, the predictive capacity of GRA-LMA and existing models such as backpropagation neural network (BP), recurrent neural network (RNN), long short-term memory (LSTM), and gate recurrent unit (GRU) was evaluated and compared using different numerical and graphical performance evaluation metrics.Comparative experimental results show that the mean square error of pH, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, electrical conductivity, turbidity, total phosphorus, and total nitrogen of GRA-LMA is reduced to 0.05890, 0.40196, 0.32454, 0.04368, 14.71003, 8.13252, 0.01558, and 0.14345. The results indicate that GRA-LMA has superior adaptability for predicting various water quality indices and can significantly lower the induced prediction error.

Performance assessment and impact of electric vehicles penetration in active distribution grids

Because of the ongoing discussion about global warming, many nations have developed several approaches to address this problem. Some strategies are: renewable energy integration, agricultural robotic solutions, and electric vehicle penetration. The last strategy, the electric vehicle (EV), has gained considerable attention due to the paradigm shift in the transport sector driven by internal combustion engines and EV penetration can also achieve efficient operation of power grids. However, there are numerous challenges associated with the penetration of these technologies within active distribution networks. It is necessary to analyze the increased amount of electricity consumption in these grids, the degradation in the voltage profile in these networks and the hosting capacity assessment of electric vehicle integration in these grids. This paper analyzes the influence of EV penetration on a 34-bus active distribution system through five EV stations and proposes two alternatives for improving the introduction of these technological elements. Specifically, it analyzes the voltage levels at the system nodes and proposes an intelligent management of resources through capacitor banks and transformer taps. From the results, the effects on the network were voltage drop on some bus bars and an increase or decrease of the loading on lines depending on the EV penetration. It is also evident that 9% of the bus bars were not working under acceptable voltage ranges in the worst-case scenario.

Asynchronous Deep Reinforcement Learning for Collaborative Task Computing and On-Demand Resource Allocation in Vehicular Edge Computing

Vehicular Edge Computing (VEC) is enjoying a surge in research interest due to the remarkable potential to reduce response delay and alleviate bandwidth pressure. Facing the ever-growing service applications in VEC, how to effectively aggregate and flexibly schedule ubiquitous network resources for implementing diverse tasks and meeting differentiated demands from numerous vehicular users remains haunting. Toward this end, we investigate collaborative task computing and on-demand resource allocation. The collaborative computing framework in VEC is provided to support deep collaboration and intelligent management of heterogeneous resources widely distributed in vehicles, edge servers and cloud. Based on this framework, the joint optimization problem of distributed task offloading and multi-resource management is formulated with the aim to maximize the system utility by making the optimal task and resource scheduling policy, the novelty of which lies in the exploration of available vehicle resources and the consideration of service migration. In view of the dynamics, randomness and time-variant of vehicular networks, the asynchronous deep reinforcement algorithm is leveraged to find the optimal solution. Extensive simulation experiments are implemented to demonstrate the superiority of our proposed algorithm in terms of response latency compared with full offloading and random offloading.

Research on the Influence of Electric Vehicle Integration in Island Microgrid, Vietnam

Vietnam's economy is developing strongly, and the demand for energy use will increase rapidly. The development of smart grids contributes significantly to the transition and sustainable development of energy from renewable energy sources to improve the quality of the national power supply and promote the sustainable use of electricity economically and efficiently. Thus, this is highly beneficial in reducing carbon emissions and other types of pollution. Besides, electrification in the transportation industry is developing rapidly, such as Electric Vehicles (EVs) and Metros in recent years. Integrating electric vehicles into the grid will enable two-way energy exchange, reactive power compensation and load balancing. However, the number of EVs participating in charging at a time will cause some conflicts, such as voltage and power loss at the nodes. Therefore, the balancing problem between load demand and generation source is a difficult task in planning operations. This paper presents a method to optimize island Microgrid (MG) operation with the participation of electric vehicles based on renewable energy sources. Optimization techniques in intelligent resource forecasting and management algorithms are built in MATLAB to achieve different requirements. The proposed Microgrid manages energy efficiency that adapts to the variability of Renewable Energy with improved efficiency.

UAV-Assisted Internet of Vehicles Over Licensed and Unlicensed Spectrum: Architecture, Intelligent Resource Management, and Challenges

UAV-Assisted Internet of Vehicles Over Licensed and Unlicensed Spectrum: Architecture, Intelligent Resource Management, and Challenges

Conceptual Model of Key Aspects of Security and Privacy Protection in a Smart City in Slovakia

The output of this work is a comprehensive overview of a wide range of key aspects of security and privacy relevant for the development of smart cities in Slovakia. The work incorporates heterogeneous and complex findings into a corpus of simplified evidence. By employing a systematic review method, this study first outlines key characteristics of a smart city, and then proceeds to summarise opportunities and challenges for conceptualising a model of a smart city in Slovakia. The development of a classification with respect to the different smart city domains, systems and potential threats aims to highlight universally applicable aspects. In order to provide an overview, the paper also presents specific requirements, options, problems, and factors taking into account Slovak policies. This work is based on the proposition that a sustainable and prosperous conceptual model of a smart city is not only linked with technological artefacts and communication infrastructure that enable intelligent management of various governance resources, but is especially tied to the norms, policies, and standards that ensure security and privacy for smart city residents, as their presence and trust in the whole ecosystem is essential for the generation, collection, processing, storage, dissemination, and use of data by respectful technologies. A secure smart city is a cross-disciplinary dilemma, a universal technological challenge built upon context-based policies, standards and procedures. The output of this work is an identification of smart city domains that can become subject to attacks and a stipulation of security requirements that are needed to assure domain functionality. Maintaining meaningful human control as a requirement to mitigate influence activities as well as protect and ensure residential engagement in a smart city was identified and added to the results of the review. Simple communication was highlighted as an effective countermeasure. Applicability of the smart city concept in Slovakia is particularly vulnerable due to the slow pace of implementation and fragmentation of relevant legislation, short development cycle of new techniques of attack, and the lack of expertise and low level of user awareness.

An intelligent resource management method in SDN based fog computing using reinforcement learning

An intelligent resource management method in SDN based fog computing using reinforcement learning

Modeling and Characterizing Service Interference in Dynamic Infrastructures

Performance interference can occur when various services are executed over the same physical infrastructure in a cloud system. This can lead to performance degradation compared to the execution of services in isolation. This work proposes a Confirmatory Factor Analysis (CFA)-based model to estimate performance interference across containers, caused by the use of CPU, memory and IO across a number of co-hosted applications. The approach provides resource characterization through human comprehensible indices expressed as time series, so the interference in the entire execution lifetime of a service can be analyzed. Our experiments, based on the combination of real services with different profiles executed in Docker containers, suggest that our model can accurately predict the overall execution time, for different service combinations. The approach can be used by a service designer to identify <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">phases</i> , during the execution life-cycle of a service, that are likely to lead to a greater degree of interference, and to ensure that only complementary services are hosted on the same physical machine. Interference-awareness of this kind will enable more intelligent resource management and scheduling for cloud systems, and may be used to dynamically modify scheduling decisions.

OPTIMAL ENERGY CONSUMPTION AND COST PERFORMANCE SOLUTION WITH DELAY CONSTRAINTS ON FOG COMPUTING

Cloud computing plays an essential role in development of the Internet of Things, which provides data processing and storage services. Fog computing is the evolution of cloud computing, which helps provide solutions to cloud computing challenges such as latency, location awareness, and real-time mobility support. Fog computing fills the gap between the cloud and IoT devices within the close vicinity of IoT devices. So, computation, networking, storage, data management, and decision making occur along the path between the cloud and IoT devices. The automatic and intelligent management of fog node resources and achieving an effective scheduling policy in the computing model is a necessary requirement and will lead to the improvement of the overall performance of fog computing. Some optimization problems are modeled by mixed-integer nonlinear programming (MINLP). In this paper, a model, i.e. an MINLP optimization problem on fog computing, is designed. Our model has two goals: to increase Cost Performance as well as to reduce energy consumption. Cost Performance is the price, users are charged as benefit/revenue. In other words Cost Performance is defined as the ratio of the average data rate of each user to its cost. Then the exact mathematical method with the GAMS program was used to prove its logical process. In the next step, we solved the model with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing+GA (SA+GA), Teaching–Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), and random method. According to the TOPSIS comparison, the SA+GA method with a value of 0.23 is the best compared to other methods. Then GWO, GA, TLBO, PSO, and GOA methods are better, respectively.

Two Time-Scale Energy and Spectrum Allocation for MEC Networks with Hybrid Energy Supplies

Integrating power grid and renewable energy to charge mobile-edge computing (MEC) server is a promising solution for green networks, which can reduce the energy consumption of power grid and carbon emission. Intelligent management of energy and bandwidth resources in the two time-scale framework can increase the energy efficiency and network revenue in MEC networks with heterogeneous energy supplies and multiple mobile users (MUs). In this paper, we investigate the joint energy management (for workloads offloading and execution) and bandwidth allocation (for workloads transmission) problem that maximizes the network revenue in the MEC network with hybrid energy supplies. We formulate it into a challenging nonlinear optimization problem because of the data randomness and the temporal coupling effect. Based on the Lyapunov optimization approach with quick convergence speed and low complexity, we propose an online algorithm to overcome the obstacles by relaxing the battery constraints, allowing us to achieve close-to-optimal maximum network revenue with the stability of battery level and different throughput requirements of MUs. Extensive numeral simulations verify the theoretical analysis and demonstrate the superior performance of the proposed algorithm in different algorithms and scenarios.

Deep-Deterministic Policy Gradient Based Multi-Resource Allocation in Edge-Cloud System: A Distributed Approach

Edge Cloud (EC) empowers the beyond 5G (B5G) wireless networks to cope with large-scale and real-time traffics of Internet-of-Things (IoT) by minimizing the latency and providing compute power at the edge of the network. Due to a limited amount of resources at the EC compared to the back-end cloud (BC), intelligent resource management techniques become imperative. This paper studies the problem of multi-resource allocation (MRA) in terms of compute and wireless resources in an integrated EC and BC environment. Machine learning-based approaches are emerging to solve such optimization problems. However, it is challenging to adopt traditional discrete action space-based methods due to their high dimensionality issue. To this end, we propose a deep-deterministic policy gradient (DDPG) based temporal feature learning attentional network (TFLAN) model to address the MRA problem. TFLAN combines convolution, gated recurrent unit and attention layers together to mine local and long term temporal information from the task sequences for excellent function approximation. A novel heuristic-based priority experience replay (hPER) method is formulated to accelerate the convergence speed. Further, a pruning principle helps the TFLAN agent to significantly reduce the computational complexity and balance the load among base stations and servers to minimize the rejection-rate. Lastly, data parallelism technique is adopted for distributed training to meet the needs of a high-volume of IoT traffic in the EC environment. Experimental results demonstrate that the distributed training approach suites well to the problem scale and can magnify the speed of the learning process. We validate the proposed framework by comparing with five state-of-the-art RL agents. Our proposed agent converges fast and achieves up to 28% and 72% reduction in operational cost and rejection-rate, and achieves up to 32% gain in the quality of experience on average, compared to the most advanced DDPG agent.

Intelligent Resource Management for eMBB and URLLC in 5G and Beyond Wireless Networks

Intelligent Resource Management for eMBB and URLLC in 5G and Beyond Wireless Networks

Toward intelligent resource management in dynamic Fog Computing‐based Internet of Things environment with Deep Reinforcement Learning: A survey

SummaryFog computing has already started to gain a lot of momentum in the industry for its ability to turn scattered computing resources into a large‐scale, virtualized, and elastic computing environment. Resource management (RM) is one of the key challenges in fog computing which is also related to the success of fog computing. Deep learning has been applied to the fog computing field for some time, and it is widely used in large‐scale network RM. Reinforcement learning (RL) is a type of machine learning algorithms, and it can be used to learn and make decisions based on reward signals that are obtained from interactions with the environment. We examine current research in this area, comparing RL and deep reinforcement learning (DRL) approaches with traditional algorithmic methods such as graph theory, heuristics, and greedy for managing resources in fog computing environments (published between 2013 and 2022) illustrating how RL and DRL algorithms can be more effective than conventional techniques. Various algorithms based on DRL has been shown to be applicable to RM problem and proved that it has a lot of potential in fog computing. A new microservice model based on the DRL framework is proposed to achieve the goal of efficient fog computing RM. The positive impact of this work is that it can successfully provide a resource manager to efficiently schedule resources and maximize the overall performance.