МНОГОУРОВНЕВАЯ АРХИТЕКТУРА СИСТЕМЫ МОНИТОРИНГА И РЕАГИРОВАНИЯ НА ВОЗДЕЙСТВИЯ В ЭРГАТИЧЕСКИХ СИСТЕМАХ

: Project goals were to develop techniques of continual reallocation of resources to maintain application performance despite statically unpredictable change in resource demands. Research was targeted to multiple application systems executing on HPC (High Performance Computing) platforms. This project built on the results of a previous program, called Adaptive Resource Allocation (ARA). In ARA, Honeywell developed techniques for dynamic reallocation of resources to single parallel applications, structured as multi-pipelines, executing on a high performance parallel machine. They extended ARA results to systems with multiple applications and multiple machines connected over a network. In October 1997 DARPA merged the technical effort on this project with the RTARM project funded under Quorum. This did not affect the core statement of work for ARM, but led to extension of its completion date. ARM focused on developing an approach based on adaption models, and addressed best-effort resource allocation in an environment with partitionable rather than shared resources. Parallel HPG platforms were de-emphasized in favor of general distributed computing platforms. Results from ARM are being integrated into RTARM. The layered architecture of ARM has given way to a hierarchical architecture characterized by uniformity across different levels. The MPI-based communication infrastructure in ARM has given way to a CORBA ORB infrastructure. While ARM implementation was targeted to Unix machine connected over Ethernet, the target platform for PTARM consists of Windows NT machines networked over ATM.

Multi-access edge computing (MEC) is a promising solution to the computation-intensive, low-latency rendering tasks of the metaverse. However, how to optimally allocate limited communication and computation resources at the edge to a large number of users in the metaverse is quite challenging. In this paper, we propose an adaptive edge resource allocation method based on multi-agent soft actor-critic with graph convolutional networks (SAC-GCN). Specifically, SAC-GCN models the multi-user metaverse environment as a graph where each agent is denoted by a node. Each agent learns the interplay between agents by graph convolutional networks with self-attention mechanism to further determine the resource usage for one user in the metaverse. The effectiveness of SAC-GCN is demonstrated through the analysis of user experience, balance of resource allocation, and resource utilization rate by taking a virtual city park metaverse as an example. Experimental results indicate that SAC-GCN outperforms other resource allocation methods in improving overall user experience, balancing resource allocation, and increasing resource utilization rate by at least 27%, 11%, and 8%, respectively.

This paper presents a number of key radio resource management strategies across physical (PHY) and medium access control (MAC) layers for reducing base station energy consumption including power efficient link adaptation (LA), exploitation of multi-user diversity and trading bandwidth for energy efficiency. By collectively taking advantage of these radio resource management strategies, a multi-user adaptive power and resource allocation algorithm is proposed to ease the power requirements of a base station, whilst maintaining the same levels of service. The scheduling algorithm is evaluated within a LTE downlink simulator for various traffic load conditions. The results show that the proposed algorithm achieves a significant energy saving over a conventional non-energy aware resource allocation scheme.

Edge video analytics enables agile responses of machine-centric applications by streaming videos from end devices to edge servers (ESs) for resource-intensive deep neural network (DNN) inference. Quality of Inference (QoI), reflected by end-to-end analytics delay and inference accuracy, is crucial for the high-quality real-time decision-making. Due to the limited ES resources and dynamic network conditions, video configuration adaptation and fine-grained resource allocation are necessary to enhance the QoI of video streams, which involves a delicate balance between delay and accuracy. In this article, we propose an edge-enabled multivideo analytics framework, in which performing DNN inference relies on heterogeneous resources, including CPU, GPU, and memory. Considering the different impacts of heterogeneous resources on QoI and double-queue backlogs, we formulate the joint video configuration adaptation, CPU-GPU resource allocation, and batch size selection (JCCGB) problem based on semi-Markov decision process to maximize the long-term average QoI. Then, Lyapunov optimization is applied to transform the original problem into one that minimizes the Lyapunov drift-plus-penalty upper bound, thereby ensuring the stability of both the transmission and computation queues. To tackle the potential multimodality of the optimal configuration adaptation and resource allocation policy, we propose the diffusion-deep-reinforcement-learning-based JCCGB (DD-JCCGB) scheme. This approach effectively enhances the policy performance and accelerates the training process. Experiments driven by real-world network traces demonstrate that the DD-JCCGB algorithm improves the long-term average QoI and outperforms baseline schemes.

Adaptive computation offloading and resource allocation strategy in a mobile edge computing environment

In wide-area networks (WANs), efficiently allocating distributed resources presents a significant challenge due to increasing node numbers and complex network conditions. Traditional algorithms, which heavily depend on global information, encounter scalability limitations. This study proposes an adaptive distributed optimal resource allocation consistency algorithm designed for WANs. The approach dynamically adjusts resource allocation based on real-time network conditions and user requirements, reducing dependence on global data. A time window distribution model and an information coding model are employed to optimize the resource allocation process. By integrating a Q-learning algorithm and extending the Paxos algorithm to WANs, global consistency among network nodes is ensured. The experimental results demonstrate the algorithm’s superior performance, particularly in low-bandwidth WAN scenarios (below 2 Mb/s), where it significantly enhances throughput. An average resource utilization rate exceeding 97% is achieved, along with rapid convergence and high parallelism. This research underscores the algorithm’s effectiveness and scalability, providing a robust solution to distributed resource allocation challenges in complex WAN environments.

A new cross-layer adaptive resource allocation scheme for real-time services is proposed in this paper. Not only the characters such as the random service arrival, limited buffers and QoS requirements are considered at MAC layer, but also the time-varying wireless channel effect. By adaptive processing procedures, it can improve end-to-end system performance effectively. Simulation results show that the proposed algorithm can improve the system throughput, spectrum efficiency as well as user satisfying level

Network virtualization technology is generally envisaged as a promising technology to consequently satisfy various types of service requirements. On the other hand, non-orthogonal multiple access (NOMA) technology has the potential to significantly increase the spectral efficiency of the system. However, previous works that jointly address these two issues have not considered the dynamic resource allocation issue in this context. In this paper, we propose a slice-based virtual resources scheduling scheme with NOMA technology to enhance the quality-of-service (QoS) of the system. We formulate the power granularity allocation and subcarrier allocation strategies into a constrained Markov decision process problem, aiming at the maximization of the total user rate. In order to further avoid the curse of dimensionality and the expectation calculation in the optimal value function, we develop an adaptive resource allocation algorithm based on approximate dynamic programming to solve the problem. Extensive simulation works have been conducted under various system settings, and the results demonstrate that the proposed algorithm can significantly reduce the outage probability and increase the user data rate.

Gears are integral components of industrial machines that are used for converting power and motion in different industrial processes. Effective monitoring of gear health and early detection of their faults is essential. It assists in averting sudden shutdowns and, in addition, reduces the costs of maintenance while at the same time increasing the efficiency of the production process. This paper proposes an approach for gear fault prognostics and condition monitoring under variable speed and flexible load conditions using vibration data. The approach involves the utilization of Artificial Neural Network (ANN) in combination with signal processing techniques to analyze the gear vibrations in induction motors (IMs) with the aim of establishing the efficiency of the gear system in different conditions of variable loads and speeds. This real-time data is recorded from the two sensors that are fixed on the exterior of the induction motor horizontally and vertically. In addition, this methodology consists of data collection, data cleaning, and data preprocessing, enhancing the ability of deep learning models, leading to accurate predictions and classification of gear faults. The models are tested and evaluated for different evaluation metrics and prove to be very effective in monitoring the overall health of gears and detecting their faults. The variable load and variable speed models were able to achieve accuracies of 96.44% and 93.82% respectively. The precision, recall, and F1 score are 0.97, 0.96, and 0.96 for the variable load model, while 0.96, 0.94, and 0.94 for the variable speed models.

This paper deals with the creation of planetary gear trains including sensor for torque and rpm and the useful way to measure the torque and RPM of the geared motor. For this we have made the planetary geared reduction motor with Hall sensors and with the monitoring system. The monitoring system displays the sensing data (torque, rpm) and calculated data (power) and also has the network capability with Bluetooth protocol. We show that our solution is much more inexpensive and simple method to measure torque and rpm than before.

This paper presents the design and development of a high power mock-up RF system used in Tokamak for matching of variable load. Developed mock-up is rated for 2kW power handling capability at the desired frequency, 182.5MHz. It comprises various components such as coaxial transmission lines, coupler, stub tuners, phase shifter, RF antennae, etc. A moveable water tank has been utilized to emulate the plasma load variation as in real Tokamak. The moveable water tank provides a continuously variable load in front of the RF antenna to test the matching capability of the developed mock-up. The developed system has been tested using the Vector Network Analyzer (VNA). The test results are presented in the paper, where the importance of each component used in the system has been highlighted. The work presented here is useful for the development of a load resilient ICRF system of the Tokamak. The developed components in the mock-up can also be useful in various fields such as radar, satellite, radio communication and broadcasting etc.

Power system dynamic state estimation (DSE) remains an active research area. This is driven by the absence of accurate models, the increasing availability of fast-sampled, time-synchronized measurements, and the advances in the capability, scalability, and affordability of computing and communications. This paper discusses the advantages of DSE as compared to static state estimation, and the implementation differences between the two, including the measurement configuration, modeling framework and support software features. The important roles of DSE are discussed from modeling, monitoring and operation aspects for today's synchronous machine dominated systems and the future power electronics-interfaced generation systems. Several examples are presented to demonstrate the benefits of DSE on enhancing the operational robustness and resilience of 21st century power system through time critical applications. Future research directions are identified and discussed, paving the way for developing the next generation of energy management systems and novel system monitoring, control and protection tools to achieve better reliability and resiliency.

Water distribution networks (WDNs) are a critical component of modern society, responsible for delivering water to local communities and industries while maintaining continuous, stable flow within the system, which carries low-grade hydropower potential. These systems are vulnerable to disruptive events, such as pipeline failures and water contamination. To mitigate the risk of disruption events, recent EU policies have focused on the digitalisation of critical infrastructure, including WDNs and wastewater treatment plants (WWTPs), through the integration of Internet of Things (IoT) technologies. Digitalisation enables system operators to collect real-time data and perform predictive maintenance, thereby improving resilience and operational efficiency. Thus, system electricity requirements will increase to support IoT and digitalisation retrofitting.This creates an opportunity to deploy energy-harvesting (EH) devices within WDNs and WWTP infrastructure that harness and utilise the resonant and kinetic energy inherent in water flow. Although the benefits of EH devices are often considered minimal and their energy outputs are low, EH devices are considered a viable power source for low-power sensors, such as pressure, contamination, and temperature sensors.This study proposes and applies a feasibility assessment framework to evaluate EH devices as an alternative, decentralised power source for IoT-enabled monitoring in WDNs. The framework includes technical performance analysis, economic viability, and environmental impact assessments, combined with probability-based resilience modelling. It uses case-specific hydraulic, operational, design, and economic data to quantify EH power outputs, assess the costs of design and deployment, evaluate the environmental footprint of the devices, determine sensor energy requirements, and assess system resilience across different deployment scenarios.Results demonstrate that the EH device has a mechanical power-generation capacity potential ranging from 0.049 to 36 watts, depending on the study location, flow conditions, and design characteristics. This EH generation capacity is sufficient to power pressure, contamination, and temperature sensors. The resilience modelling indicates that the detection probability of WDNs exhibits the most significant gains from adding sensors at low deployment levels. Thus, most of the improvement in detection levels occurs between deploying the first five to ten sensors; beyond this threshold, adding more sensors exhibits diminishing marginal returns to detection probability and system resilience. In addition, adding more sensors can significantly reduce system resilience by increasing power requirements, thereby placing excessive load on the power supply of the EH devices. Thus, it increases the risk of failure rather than enhancing resilience.Overall, the results underscore the importance of strategic sensor allocation over high-density deployment and of balancing monitoring coverage with energy availability. Furthermore, the results show that EH devices are suitable power-generation technologies for supporting the digitalisation of WDNs and informing the design of new monitoring systems and sensor placement to enhance system reliability, enable cost-effective monitoring, and maximise mitigation benefits. In addition, the proposed framework provides decision-makers with a structured approach to assessing EH integration applications for digitalised WDNs, focusing on enhancing resilience through monitoring, conducting technical performance and cost-benefit analyses, assessing environmental trade-offs, and designing monitoring strategies aligned with sustainability and resilience objectives.

This paper studies the problem of adaptive resource allocation in OFDMA system. An ant colony algorithm based adaptive resource allocation is proposed in this paper. The problem is modeled as finding a minimum cost path in a graph. The ants' solution construction is guided by pheromone trail and heuristic information. Due to the unique heuristic searching mechanism of the ant colony algorithm, the proposed algorithm is guaranteed to converge quickly to an approximately optimal solution. Simulation results showed that the performance of the proposed algorithm is close to the optimal solution, and gains 1-2 dB in comparison with the fixed allocation scheme.

In this paper, a new adaptive resource allocation and grouping scheme is proposed to enhance the capacity of device-to-device (D2D) systems underlaying cellular networks. In order to increase resource utilization, we propose to divide the outer region of the cell into multiple parts and the corresponding D2D pairs located in the symmetrical parts are designed to reuse the same cellular user equipment's (CUE's) resources. To further improve spectrum efficiency, three grouping modes for D2D pairs reusing the same resources are devised and these three modes are proposed to be employed adaptively according to the values of the channel gains between CUEs and base station (BS). Furthermore, by considering interference powers from CUEs to D2D UEs (DUEs) and channel gains of D2D pairs together, an equivalent channel gain is proposed to be exploited to select D2D pairs in symmetrical parts to reuse the same resources of CUEs. Simulation results show that the proposed scheme can increase the capacity of D2D systems up to 3.5 times compared with random resource allocation one.