Time-sensitive Applications Research Articles

Soft Actor-critic-based Distributed Routing Scheme for Edge Computing Integrated with Dynamic IoT Networks

The rapid expansion of the Internet of Things (IoT) necessitates advanced routing schemes capable of meeting the stringent demands for low latency and high accuracy, which are critical for applications such as autonomous vehicles and telemedicine. Traditional edge computing methods often struggle with elevated latency, rendering them unsuitable for time-sensitive applications. Additionally, many reinforcement learning (RL) algorithms require action space discretization, which can introduce biases and dimensionality challenges. This paper introduces a novel Soft Actor-Critic (SAC)-based distributed routing scheme for edge computing, specifically designed to address these limitations. By integrating RL with Maximum Entropy principles and employing a decentralized approach, the proposed model enhances network performance, reduces delays, and effectively manages multi-optimality criteria. The distributed routing scheme operates independently of a centralized controller, allowing routers to make autonomous decisions and adapt seamlessly to changes in the network. This is accomplished through a Markov Decision Process (MDP) that optimizes routing paths based on various factors, including node depth, energy consumption, and transmission probability. The methodology encompasses local training phases for individual nodes, followed by federated training to refine the model across the network. Experimental results conducted on topologies of varying scales demonstrate the model’s efficacy in achieving high accuracy and efficient convergence, particularly in dynamic IoT environments. These findings underscore the potential of the proposed SAC-based distributed routing scheme as a robust solution for enhancing routing efficiency and reliability in the evolving landscape of IoT applications. IMPACT STATEMENT The rapid expansion of Internet of Things (IoT) applications demands advanced routing solutions to ensure low latency and high accuracy, crucial for sectors like autonomous vehicles and telemedicine. Traditional edge computing methods struggle with elevated latency, while many reinforcement learning (RL) algorithms face challenges with action space discretization, leading to biases and dimensionality issues. This study introduces a novel Soft Actor-Critic (SAC)-based distributed routing scheme to address these limitations. Integrating Maximum Entropy principles with RL enhances exploration and decision-making stability. The decentralized approach allows routers to make autonomous, real-time decisions based on local network conditions, optimizing routing paths through a Markov Decision Process (MDP). Experimental results from various simulated IoT network topologies show the model’s superior performance in reducing delays and maintaining bandwidth. This research paves the way for more reliable, low-latency IoT applications, significantly enhancing routing efficiency and network adaptability in dynamic IoT environments.

Optimised Deep Learning for Time-Critical Load Forecasting Using LSTM and Modified Particle Swarm Optimisation

Short-term electric load forecasting is critical for power system planning and operations due to demand fluctuations driven by variable energy resources. While deep learning-based forecasting models have shown strong performance, time-sensitive applications require improvements in both accuracy and convergence speed. To address this, we propose a hybrid model that combines long short-term memory (LSTM) with a modified particle swarm optimisation (mPSO) algorithm. Although LSTM is effective for nonlinear time-series predictions, its computational complexity increases with parameter variations. To overcome this, mPSO is used for parameter tuning, ensuring accurate forecasting while avoiding local optima. Additionally, XGBoost and decision tree filtering algorithms are incorporated to reduce dimensionality and prevent overfitting. Unlike existing models that focus mainly on accuracy, our framework optimises accuracy, stability, and convergence rate simultaneously. The model was tested on real hourly load data from New South Wales and Victoria, significantly outperforming benchmark models such as ENN, LSTM, GA-LSTM, and PSO-LSTM. For NSW, the proposed model reduced MSE by 91.91%, RMSE by 94.89%, and MAPE by 74.29%. In VIC, MSE decreased by 91.33%, RMSE by 95.73%, and MAPE by 72.06%, showcasing superior performance across all metrics.

Improving the performance of IoT devices that use Wi-Fi

<p>Providing quality service to users of the internet of things (IoT) entails addressing two crucial aspects: one related to security and the other concerning the limited resources of IoT devices. We will face a challenge while using timesensitive applications within a network that utilizes a high-performance Wi-Fi technology with exceeding energy consumption. Due to this research challenge, we propose a new algorithm, IoT-quality of service (QoS), designed to achieve a true balance between enhancing the security aspects of IoT devices and improving network-hardware performance. Thus, the algorithm efficiently manages the limited energy resources by monitoring energy levels, communication quality, and queuing delay at access points. This is accomplished by utilizing a streamlined identity management system capable of achieving authentication and access authorization with reduced loading for IoT devices. The research hypothesis underwent validation through a comparative analysis of its performance against the conventional model of a Wi-Fi-based IoT device. This evaluation was conducted utilizing the NS3 simulator and was based on a predetermined set of parameters influencing the examined performance metrics, including power consumption, throughput, delay, and response time. The findings exposed the superiority of the proposed algorithm.</p>

Enhanced Acoustic Mixing in Silicon-Based Chips with Sharp-Edged Micro-Structures

The small dimensions of microfluidic channels allow for fast diffusive or passive mixing, which is beneficial for time-sensitive applications such as chemical reactions, biological assays, and the transport of to-be-detected species to sensors. In microfluidics, the need for fast mixing within milliseconds arises primarily because these devices are often used in fields where rapid and efficient mixing significantly impacts the performance and outcome of the processes. Active mixing with acoustics in microfluidic devices involves using acoustic waves to enhance the mixing of fluids within microchannels. Using sharp corners and wall patterns in acoustofluidic devices significantly enhances the mixing by acoustic streaming around these features. The streaming patterns around the sharp edges are particularly effective for the mixing because they can produce strong lateral flows that rapidly homogenize liquids. This work presents extensive characterizations of the effect of sharp-edged structures on acoustic mixing in bulk acoustic wave (BAW) mode in a silicon microdevice. The effect of side wall patterns in different angles and shapes, their positions, the type of piezoelectric transducer, and its amplitude and frequency have been studied. Following the patterning of the channel walls, a mixing time of 25 times faster was reached, compared to channels with smooth side walls exhibiting conventional BAW behavior. The average locally determined acoustic streaming velocity inside the channel becomes 14 times faster if sharp corners of 10° are added to the wall.

Continuous GPS PPP-AR frequency transfer links

Abstract In this paper, we show that the SPARK software of the Natural Resources Canada (NRCan) with their DCR (Decoupled Clock Rapid) products can be used to generate the PPP-AR continuous batch clock solutions of multiple days, which we use to build frequency transfer links between two remotely located GPS receivers. The reliability and confidence in forming the long-term frequency transfer links have been improved compared to reference~\cite{jian2023gps}. We compare the SPARK PPP-AR links to an optical fiber and TWSTFT links for hundreds of days. The ∽500-day-long comparisons with TWSTFT show no frequency bias for continental and cross-continental links. Frequency transfer links formed using the SPARK solutions have uncertainty of 1×10-15/T, where T is in days, without reaching the noise floor, a critical requirement for comparing optical frequency standards and for the redefinition of the SI second. The short latency of the NRCan DCR products enables the quick availability of the PPP-AR links presented in this paper marking it particularly relevant for time sensitive applications.

Multi-agent reinforcement learning for task offloading with hybrid decision space in multi-access edge computing

Multi-access Edge Computing (MEC) has become a significant technology for supporting the computation-intensive and time-sensitive applications on the Internet of Things (IoT) devices. However, it is challenging to jointly optimize task offloading and resource allocation in the dynamic wireless environment with constrained edge resource. In this paper, we investigate a multi-user and multi-MEC servers system with varying task request and stochastic channel condition. Our purpose is to minimize the total energy consumption and time delay by optimizing the offloading decision, offloading ratio and computing resource allocation simultaneously. As the users are geographically distributed within an area, we formulate the problem of task offloading and resource allocation in MEC system as a partially observable Markov decision process (POMDP) and propose a novel multi-agent deep reinforcement learning (MADRL) -based algorithm to solve it. In particular, two aspects have been modified for performance enhancement: (1) To make fine-grained control, we design a novel neural network structure to effectively handle the hybrid action space arisen by the heterogeneous variables. (2) An adaptive reward mechanism is proposed to reasonably evaluate the infeasible actions and to mitigate the instability caused by manual configuration. Simulation results show the proposed method can achieve 7.12%−20.97% performance enhancements compared with the existing approaches.

A Comprehensive Review of Energy-Efficient Techniques for UAV-Assisted Industrial Wireless Networks

The rapid expansion of the Industrial Internet-of-Things (IIoT) has spurred significant research interest due to the growth of security-aware, vehicular, and time-sensitive applications. Unmanned aerial vehicles (UAVs) are widely deployed within wireless communication systems to establish rapid and reliable links between users and devices, attributed to their high flexibility and maneuverability. Leveraging UAVs provides a promising solution to enhance communication system performance and effectiveness while overcoming the unprecedented challenges of stringent spectrum limitations and demanding data traffic. However, due to the dramatic increase in the number of vehicles and devices in the industrial wireless networks and limitations on UAVs’ battery storage and computing resources, the adoption of energy-efficient techniques is essential to ensure sustainable system implementation and to prolong the lifetime of the network. This paper provides a comprehensive review of various disruptive methodologies for addressing energy-efficient issues in UAV-assisted industrial wireless networks. We begin by introducing the background of recent research areas from different aspects, including security-enhanced industrial networks, industrial vehicular networks, machine learning for industrial communications, and time-sensitive networks. Our review identifies key challenges from an energy efficiency perspective and evaluates relevant techniques, including resource allocation, UAV trajectory design and wireless power transfer (WPT), across various applications and scenarios. This paper thoroughly discusses the features, strengths, weaknesses, and potential of existing works. Finally, we highlight open research issues and gaps and present promising potential directions for future investigation.

DBDAA: A real-time approach to Dynamic Banker’s Deadlock Avoidance Algorithm with optimized time complexity

Effective resource allocation is crucial in operating systems to prevent deadlocks, especially when resources are limited and non-shareable. Traditional methods like the Banker’s algorithm provide solutions but suffer from limitations such as static process handling, high time complexity, and a lack of real-time adaptability. To address these challenges, we propose the Dynamic Banker’s Deadlock Avoidance Algorithm (DBDAA). The DBDAA introduces real-time processing for safety checks, significantly improving system efficiency and reducing the risk of deadlocks. Unlike conventional methods, the DBDAA dynamically includes processes in safety checks, considerably decreasing the number of comparisons required to determine safe states. This optimization reduces the time complexity to O(n) in the best-case and O(nd) in the average and worst-case scenarios, compared to the O(n2d) complexity of the original Banker’s algorithm. The integration of real-time processing ensures that all processes can immediately engage in safety checks, improving system responsiveness and making the DBDAA suitable for dynamic and time-sensitive applications. Additionally, the DBDAA introduces a primary unsafe sequence mechanism that enhances the acceptability and efficiency of the algorithm by allowing processes to participate in safety checks repeatedly after a predetermined amount of system-defined time. Experimental comparisons with existing algorithms demonstrate the superiority of the DBDAA in terms of reduced safe state prediction time and increased efficiency, making it a robust solution for deadlock avoidance in real-time systems.

ShaderNN: A lightweight and efficient inference engine for real-time applications on mobile GPUs

Inference using deep neural networks on mobile devices has been an active area of research in recent years. The design of a deep learning inference framework targeted for mobile devices needs to consider various factors, such as the limited computational capacity of the devices, low power budget, varied memory access methods, and I/O bus bandwidth governed by the underlying processor’s architecture. Furthermore, integrating an inference framework with time-sensitive applications — such as games and video-based software to perform tasks like ray tracing denoising and video processing — introduces the need to minimize data movement between processors and increase data locality in the target processor. In this paper, we propose Shader Neural Network (ShaderNN), an OpenGL-based, fast, and power-efficient inference framework designed for mobile devices to address these challenges. Our contributions include the following: (1) the texture-based input/output provides an efficient, zero-copy integration with real-time graphics pipelines or image processing applications, thereby saving expensive data transfers between CPU and GPU, which are unavoidable in most existing inference engines; (2) we are the first to leverage fragment shaders based on the OpenGL backend in neural network inference operators, which has an advantage in deploying parametrically small neural network models; (3) a hybrid implementation of the compute shader and fragment shader is proposed that enables layer-level shader selection to boost performance; and (4) we utilize OpenGL features — such as normalization, interpolation and texture padding — to improve performance. Experiments illustrate the favorable performance of ShaderNN over other popular on-device deep learning frameworks such as TensorFlow-Lite on the latest mobile devices powered by Qualcomm and MediaTek chips. A case study further demonstrates the usability and integration of the ShaderNN framework with a media processing Android application seamlessly. ShaderNN is available open source at Github (https://github.com/inferenceengine/shadernn).

A Localization Method of High Energy Transients for All-sky Gamma-ray Monitor

Fast and reliable localization of high-energy transients is crucial for characterizing the burst properties and guiding the follow-up observations. Localization based on the relative counts of different detectors has been widely used for all-sky gamma-ray monitors. There are two major methods for this count distribution localization: χ 2 minimization method and the Bayesian method. Here we propose a modified Bayesian method that could take advantage of both the accuracy of the Bayesian method and the simplicity of the χ 2 method. With comprehensive simulations, we find that our Bayesian method with Poisson likelihood is generally more applicable for various bursts than the χ 2 method, especially for weak bursts. We further proposed a location-spectrum iteration approach based on the Bayesian inference, which could alleviate the problems caused by the spectral difference between the burst and location templates. Our method is very suitable for scenarios with limited computation resources or time-sensitive applications, such as in-flight localization software, and low-latency localization for rapidly follow-up observations.

Exploiting stream scheduling in QUIC: Performance assessment over wireless connectivity scenarios

The advent of wireless technologies has led to the development of novel services for end-users, with stringent needs and requirements. High availability, very high throughput, low latency, and reliability are all of them crucial performance parameters. To address these demands, emerging technologies, such as non-terrestrial networks or millimeter wave (mmWave), are being included in 5G and Beyond 5G (B5G) specifications. mmWave enables massive data transmissions, at the expense of a more hostile propagation, typical for high frequency bands. Consequently, the inherent instability of the physical channel significantly affects the upper layers of the protocol stack, resulting in congestion and data losses, which might strongly hinder the overall communication performance. These challenges can be addressed not only at the link layer, but at any affected layer. QUIC is a new transport protocol designed to reduce communications latency in many ways. Among other features, it enables the use of multiple streams to effectively manage data flows sent through its underlying UDP socket. This paper introduces an implementation of priority-based stream schedulers along with the design of a flexible interface. Exploiting the proposed approach, applications are able to set the required scheduling scheme, as well as the stream priorities. The feasibility of the proposed approach is validated through an extensive experiment campaign, which combines Docker containers, the ns-3 simulator and the Mahimahi framework, which is exploited to introduce realistic mmWave channel traces. The results evince that an appropriate stream scheduler can indeed yield lower delays for time-sensitive applications by up to 36% under unreliable conditions.

A Ranking-Based Cross-Entropy Loss for Early Classification of Time Series.

Early classification tasks aim to classify time series before observing full data. It is critical in time-sensitive applications such as early sepsis diagnosis in the intensive care unit (ICU). Early diagnosis can provide more opportunities for doctors to rescue lives. However, there are two conflicting goals in the early classification task-accuracy and earliness. Most existing methods try to find a balance between them by weighing one goal against the other. But we argue that a powerful early classifier should always make highly accurate predictions at any moment. The main obstacle is that the key features suitable for classification are not obvious in the early stage, resulting in the excessive overlap of time series distributions in different time stages. The indistinguishable distributions make it difficult for classifiers to recognize. To solve this problem, this article proposes a novel ranking-based cross-entropy () loss to jointly learn the feature of classes and the order of earliness from time series data. In this way, can help classifier to generate probability distributions of time series in different stages with more distinguishable boundary. Thus, the classification accuracy at each time step is finally improved. Besides, for the applicability of the method, we also accelerate the training process by focusing the learning process on high-ranking samples. Experiments on three real-world datasets show that our method can perform classification more accurately than all baselines at all moments.

Coordinated SR and Restricted TWT for Time Sensitive Applications in WiFi 7 Networks

Coordinated SR and Restricted TWT for Time Sensitive Applications in WiFi 7 Networks

Real-time data analytics in distributed systems

Real-time data analytics involves the processing and analysis of data as it arrives, delivering immediate insights that are crucial for time-sensitive applications. This research explores the platforms and techniques necessary for supporting real-time analytics, extending beyond traditional Event Processing Systems (EPS) to include broader big data contexts that integrate both 'data at rest' and 'data in motion' solutions. A detailed case study is presented, showcasing the application of the Event Swarm complex event processing engine in addressing financial analytics challenges. The study identifies key research challenges in the field, emphasizing the need for innovative approaches and algorithms to enhance real-time data filtering, exploration, statistical analysis, and machine learning.

Optimal Piecewise Polynomial Approximation for Minimum Computing Cost by Using Constrained Least Squares.

In this paper, the optimal approximation algorithm is proposed to simplify non-linear functions and/or discrete data as piecewise polynomials by using the constrained least squares. In time-sensitive applications or in embedded systems with limited resources, the runtime of the approximate function is as crucial as its accuracy. The proposed algorithm searches for the optimal piecewise polynomial (OPP) with the minimum computational cost while ensuring that the error is below a specified threshold. This was accomplished by using smooth piecewise polynomials with optimal order and numbers of intervals. The computational cost only depended on polynomial complexity, i.e., the order and the number of intervals at runtime function call. In previous studies, the user had to decide one or all of the orders and the number of intervals. In contrast, the OPP approximation algorithm determines both of them. For the optimal approximation, computational costs for all the possible combinations of piecewise polynomials were calculated and tabulated in ascending order for the specific target CPU off-line. Each combination was optimized through constrained least squares and the random selection method for the given sample points. Afterward, whether the approximation error was below the predetermined value was examined. When the error was permissible, the combination was selected as the optimal approximation, or the next combination was examined. To verify the performance, several representative functions were examined and analyzed.

Advanced method for Image detection using OpenCV

Abstract: In computer vision, object detection is an essential job with many real-world applications, such as robots, autonomous cars, and surveillance. Because of its great accuracy and speed, the YOLO (You Only Look Once) method is a well-liked realtime object recognition technique that has attracted a lot of attention. This technique is perfect for time-sensitive applications since it examines the full image at once and predicts bounding boxes and class probabilities for recognized items. YOLO has undergone many iterations, with YOLO v5 being the most recent and sophisticated version. It utilizes anchor boxes and a feature pyramid network (FPN) to increase the accuracy of object recognition. Our goal in this research is to apply YOLO v5 to realtime picture and object identification applications stage. The model will be trained using an appropriate dataset, and its performance will be assessed against a range of benchmarks and advanced object detection methods. The project's output will offer a reliable and effective real-time object detection system that will facilitate prompt decision-making in the identification of item types and their corresponding placements. It has useful uses in robotics, autonomous driving, and surveillance.

Gas engine CCHP system optimization: An energy, exergy, economic, and environment analysis and optimization based on developed northern goshawk optimization algorithm

This paper aims to enhance the design and operation of a Combined Cooling, Heating, and Power (CCHP) system utilizing a gas engine as the primary energy source for a residential building in China. An Energy, Exergy, Economic, and Environment (4E) analysis is employed to assess the system's performance and impact based on energy, exergy, economic, and environmental criteria. The effectiveness of the DNGO algorithm is evaluated on a case study site and compared with Northern Goshawk Optimization (NGO) and Genetic Algorithm (GA). The findings demonstrate that the DNGO algorithm identifies the optimal gas engine size of 130 kW. The algorithm's search capabilities are greatly enhanced by this unique blend, surpassing what traditional methods can offer. The DNGO algorithm brings several advantages, including unparalleled energy efficiency, reduced exergy destruction, and a substantial decrease in CO2 emissions. This not only supports environmental sustainability but also aligns with global standards. Economically, the algorithm enhances the performance of the CCHP system, evident through a reduced payback period and increased annual profit. Additionally, the algorithm's rapid convergence rate allows it to reach the optimal solution faster than its counterparts, making it advantageous for time-sensitive applications. Incorporating innovative methods like chaos theory, the DNGO algorithm effectively avoids local optima, enabling a broader search for the best solution. The utilization of Lévy flight further enhances the algorithm's ability to escape local optima and navigate the search space more efficiently. Additionally, swarm intelligence is employed to simulate the collective behavior of decentralized systems, aiding in problem-solving. This research represents a significant advancement in optimization techniques for CCHP systems and offers a fresh perspective to the field of swarm-based optimization algorithms.

Bi-directional adaptive enhanced A* algorithm for mobile robot navigation

PurposeThe present paper aims to address challenges associated with path planning and obstacle avoidance in mobile robotics. It introduces a pioneering solution called the Bi-directional Adaptive Enhanced A* (BAEA*) algorithm, which uses a new bidirectional search strategy. This approach facilitates simultaneous exploration from both the starting and target nodes and improves the efficiency and effectiveness of the algorithm in navigation environments. By using the heuristic knowledge A*, the algorithm avoids unproductive blind exploration, helps to obtain more efficient data for identifying optimal solutions. The simulation results demonstrate the superior performance of the BAEA* algorithm in achieving rapid convergence towards an optimal action strategy compared to existing methods.Design/methodology/approachThe paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bidirectional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.FindingsThe paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bi-directional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm.Research limitations/implicationsThe rigorous evaluation of our proposed BAEA* algorithm with the BAA* algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.Originality/valueThe originality of this paper lies in the introduction of the bidirectional adaptive enhancing A* algorithm (BAEA*) as a novel solution for path planning for mobile robots. This algorithm is characterized by its unique characteristics that distinguish it from others in this field. First, BAEA* uses a unique bidirectional search strategy, allowing to explore the same path from both the initial node and the target node. This approach significantly improves efficiency by quickly converging to the best paths and using A* heuristic knowledge. In particular, the algorithm shows remarkable capabilities to quickly recognize shorter and more stable paths while ensuring higher success rates, which is an important feature for time-sensitive applications. In addition, BAEA* shows adaptability and robustness in dynamically changing environments, not only avoiding obstacles but also respecting various constraints, ensuring safe path selection. Its scale further increases its versatility by seamlessly applying it to extensive and complex environments, making it a versatile solution for a wide range of practical applications. The rigorous assessment against established algorithms such as BAA* consistently shows the superior performance of BAEA* in planning shorter paths, achieving higher success rates in different environments and cementing its importance in complex and challenging environments. This originality marks BAEA* as a pioneering contribution, increasing the efficiency, adaptability and applicability of mobile robot path planning methods.

Hybrid metaheuristic schemes with different configurations and feedback mechanisms for optimal clustering applications

This paper addresses the critical gap in the understanding of the effects of various configurations and feedback mechanisms on the performance of hybrid metaheuristics (HMs) in unsupervised clustering applications. Despite the widespread use of HMs due to their ability to leverage multiple optimization methods, the lack of comprehensive studies on their configuration and feedback mechanisms effects often results in sub-optimal clustering performances and premature convergence. To tackle these issues, we introduce two algorithms for implementing eight distinct HM schemes, focusing on the impacts of parallel and serial processing models along with different feedback mechanisms. Our approach involves selecting candidate metaheuristics based on a mix of evolutionary and swarm-based methods, including the k-means algorithm, to form various HM-based clustering schemes. These schemes were then rigorously evaluated across a range of datasets and feedback mechanisms, further assessing their efficiency in the deployment of smart grid base stations. Performance analysis was based on total fitness evaluations, timing capabilities, and clustering accuracy. The results revealed that parallel HMs with decoupled feedback mechanisms performed best in terms of accuracy but at the cost of slower convergence rates as compared to serial HMs. Our findings further suggest that serial HMs will be best suited for time-sensitive applications where a compromise between speed and accuracy is acceptable, while parallel HMs with decoupled feedback mechanisms are preferable for scenarios where precision is paramount. This research significantly contributes to the field by providing a detailed analysis of HM performance in varying conditions, thereby guiding the selection of appropriate HM schemes for specific clustering tasks.

Distributed Systems for Real-Time Computing in Cloud Environment: A Review of Low-Latency and Time Sensitive Applications

As a result of its many benefits, including cost-efficiency, speed, effectiveness, greater performance, and increased security, cloud computing has seen a boom in popularity in recent years. This trend has attracted both consumers and businesses. Being able to process and provide data or services in a quick and effective manner while adhering to low latency and time limits is the hallmark of an efficient distributed system that is designed particularly for real-time computing in cloud environments. It is essential to place a high priority on low latency and time sensitivity while developing and putting into action a distributed system for real-time computing in a cloud environment. In order to fulfil the particular requirements of the application or service, consideration must be given to a number of different aspects. In particular, the topic of load balancing will be discussed in this paper. It is possible to ensure a more effective distribution of workload and reduce latency by using load balancers, which distribute incoming traffic over many servers or instances. The throttled algorithm is believed to be the most efficient load balancing strategy for reducing service delivery delay in cloud computing. This research investigates a hybrid method known as Equally Spread Current Execution (ESCE), which is known for its combination with the throttled algorithm.