Task Completion Time Research Articles

The Impact of Multimedia Feedback in Blended Learning Environments on University Students’ Programming Skills

This study investigates the impact of multimedia feedback on university students’ programming skills within a blended learning environment in the United Kingdom. A quasi-experimental design was employed, involving 120 undergraduate students divided into experimental and control groups. The experimental group received multimedia feedback, including screencast videos, annotated coding examples, and interactive quizzes, while the control group received traditional written feedback. Quantitative data from pre-tests and post-tests showed that the experimental group demonstrated a 33.3% increase in programming skills, compared to a 22.5% increase in the control group. The experimental group also exhibited a 35% reduction in errors and a 20% reduction in task completion time. Qualitative data from student interviews and open-ended survey responses revealed increased engagement, understanding, clarity, motivation, and confidence among students who received multimedia feedback. The study highlights the transformative potential of multimedia feedback in enhancing programming education and suggests practical implications for educators and policymakers. Recommendations for future research include exploring the long-term impact, scalability, and applicability of multimedia feedback across different educational contexts and disciplines.

A novel mixture model for characterizing human aiming performance data

Fitts’ law is often employed as a predictive model for human movement, especially in the field of human-computer interaction. Models with an assumed Gaussian error structure are usually adequate when applied to data collected from controlled studies. However, observational data (often referred to as data gathered ‘in the wild’) typically display noticeable positive skewness relative to a mean trend as users do not routinely try to minimize their task completion time. As such, the exponentially modified Gaussian (EMG) regression model has been applied to aimed movements data. However, it is also of interest to reasonably characterize those regions where a user likely was not trying to minimize their task completion time. In this article, we propose a novel model with a two-component mixture structure—one Gaussian and one exponential—on the errors to identify such a region. An expectation-conditional-maximization (ECM) algorithm is developed for estimation of such a model and some properties of the algorithm are established. The efficacy of the proposed model, as well as its ability to inform model-based clustering, are addressed in this work through extensive simulations and an insightful analysis of a human aiming performance study.

Usability Analysis of Smart Speakers from a Learnability Perspective for Novel Users

Although commercial smart speakers are becoming increasingly popular, there is still much potential for investigation into their usability. In this study, we analyzed the usability of commercial smart speakers by focusing on the learnability of young users who are not yet familiar with voice user interface (VUI) operation. In the experiment, we conducted a task in which users repeatedly operated a smart speaker 10 times under four conditions, combining two experimental factors: the presence or absence of a screen on the smart speaker and the operation method (voice control only or in conjunction with remote-control operation). The usability of the smart speaker was analyzed in terms of task-completion time, task-completion rate, number of errors, subjective evaluation, and retrospective protocol analysis. In particular, we confirmed and compared the learning curves for each condition in terms of the performance metrics. The experimental results showed that there were no substantial differences in the learning curves between the presence and absence of a screen. In addition, the “lack of feedback” and “system response error” were identified as usability problems, and it was suggested that these problems led to “distrust of the system”.

Learning Autonomous Viewpoint Adjustment from Human Demonstrations for Telemanipulation

Teleoperation systems find many applications from earlier search-and-rescue to more recent daily tasks. It is widely acknowledged that using external sensors can decouple the view of the remote scene from the motion of the robot arm during manipulation, facilitating the control task. However, this design requires the coordination of multiple operators or may exhaust a single operator as s/he needs to control both the manipulator arm and the external sensors. To address this challenge, our work introduces a viewpoint prediction model, the first data-driven approach that autonomously adjusts the viewpoint of a dynamic camera to assist in telemanipulation tasks. This model is parameterized by a deep neural network and trained on a set of human demonstrations. We propose a contrastive learning scheme that leverages viewpoints in a camera trajectory as contrastive data for network training. We demonstrated the effectiveness of the proposed viewpoint prediction model by integrating it into a real-world robotic system for telemanipulation. User studies reveal that our model outperforms several camera control methods in terms of control experience and reduces the perceived task load compared to manual camera control. As an assistive module of a telemanipulation system, our method significantly reduces task completion time for users who choose to adopt its recommendation.

Conflict-free coordination planning for multiple automated guided vehicles in an intelligent warehousing system

With the rapid development of intelligent manufacturing, the application of automated guided vehicles (AGVs) in intelligent warehousing systems has become increasingly common. Efficiently planning the conflict-free paths of multiple AGVs while minimizing the total task completion time is crucial for evaluating the performance of the system. Distinguishing itself from recent approaches where conflict avoidance strategy and path planning algorithm are executed independently or separately, this paper proposes an improved conflict-free A* algorithm by integrating the conflict avoidance strategy into the initial path planning process. Based on the heuristic A* algorithm, we use the instruction time consumption as the key evaluation indicator of the cost function and add the turning consumption in the future path cost evaluation. Moreover, the expansion mode of child nodes is optimized where a five-element search set containing the “zero movement” is proposed to implement a proactive pause-wait strategy. Then the prediction rules are designed to add constraints to three types of instructions based on the timeline map, guiding the heuristic planning to search for conflict-free child nodes. Extensive simulations show that the coordination planning based on the improved conflict-free A* algorithm not only effectively achieves advanced conflict avoidance at the algorithmic level, but also exhibits lower computational complexity and higher task completion efficiency compared to other coordination planning methods.

Usability of an Automated System for Real-Time Monitoring of Shared Decision-Making for Surgery: Mixed Methods Evaluation.

Improving shared decision-making (SDM) for patients has become a health policy priority in many countries. Achieving high-quality SDM is particularly important for approximately 313 million surgical treatment decisions patients make globally every year. Large-scale monitoring of surgical patients' experience of SDM in real time is needed to identify the failings of SDM before surgery is performed. We developed a novel approach to automating real-time data collection using an electronic measurement system to address this. Examining usability will facilitate its optimization and wider implementation to inform interventions aimed at improving SDM. This study examined the usability of an electronic real-time measurement system to monitor surgical patients' experience of SDM. We aimed to evaluate the metrics and indicators relevant to system effectiveness, system efficiency, and user satisfaction. We performed a mixed methods usability evaluation using multiple participant cohorts. The measurement system was implemented in a large UK hospital to measure patients' experience of SDM electronically before surgery using 2 validated measures (CollaboRATE and SDM-Q-9). Quantitative data (collected between April 1 and December 31, 2021) provided measurement system metrics to assess system effectiveness and efficiency. We included adult patients booked for urgent and elective surgery across 7 specialties and excluded patients without the capacity to consent for medical procedures, those without access to an internet-enabled device, and those undergoing emergency or endoscopic procedures. Additional groups of service users (group 1: public members who had not engaged with the system; group 2: a subset of patients who completed the measurement system) completed user-testing sessions and semistructured interviews to assess system effectiveness and user satisfaction. We conducted quantitative data analysis using descriptive statistics and calculated the task completion rate and survey response rate (system effectiveness) as well as the task completion time, task efficiency, and relative efficiency (system efficiency). Qualitative thematic analysis identified indicators of and barriers to good usability (user satisfaction). A total of 2254 completed surveys were returned to the measurement system. A total of 25 service users (group 1: n=9; group 2: n=16) participated in user-testing sessions and interviews. The task completion rate was high (169/171, 98.8%) and the survey response rate was good (2254/5794, 38.9%). The median task completion time was 3 (IQR 2-13) minutes, suggesting good system efficiency and effectiveness. The qualitative findings emphasized good user satisfaction. The identified themes suggested that the measurement system is acceptable, easy to use, and easy to access. Service users identified potential barriers and solutions to acceptability and ease of access. A mixed methods evaluation of an electronic measurement system for automated, real-time monitoring of patients' experience of SDM showed that usability among patients was high. Future pilot work will optimize the system for wider implementation to ultimately inform intervention development to improve SDM. RR2-10.1136/bmjopen-2023-079155.

Decentralized computation offloading via multi-agent deep reinforcement learning for NOMA-assisted mobile edge computing with energy harvesting devices

Supporting latency-sensitive and computation-intensive applications is hardly possible for mobile devices (MDs) with limited battery capacity and low computing resources. Therefore, mobile edge computing (MEC) and wireless power transfer (WPT) have emerged as promising technologies that allow MDs to offload part or all of their workloads to MEC servers and harvest energy for prolonging their battery lifetime. However, the MEC server’s limited computing resources, available communication channel quality, and time-limited energy harvesting (EH) challenge the computation offloading. In this paper, we study the joint problem of decentralized computation offloading and resource allocation (JDCORA) in the environment of non-orthogonal multiple access (NOMA)-assisted MEC with multiple EH-enabled MDs. To learn decentralized offloading policies, we propose a multi-agent deep reinforcement learning (MADRL)-based scheme towards minimizing energy consumption and task completion time, which considers the cooperation between MDs to adjust their strategies. In particular, we improve the multi-agent deep deterministic policy gradient (MADDPG) by applying the features of double actors, double centralized critics, soft value estimation, critic regularization and proportional-based prioritized experience replay (pPER) and propose an algorithm called multi-agent twin actors regularized critics (MATARC). Simulation results demonstrate that the proposed MATARC has a better convergence performance compared to other baseline methods and also reduces the average energy consumption, task completion time and task drop rate.

Usability Evaluation and Interface Design Improvement for the Maxim Application with User-Centered Design Approach

The research focused on the user interface of the Maxim application, a ride-hailing service in Indonesia facing usability challenges compared to its competitors, such as Gojek, Grab, and inDrive. The study aimed to identify user issues and propose alternative interface designs to enhance usability. The User-Centered Design (UCD) approach was adopted, involving two iterative phases that yielded high-fidelity designs. Through online surveys and interviews, 77 problem categories were identified, adhering to Usability Heuristic principles, and 93 design solutions were crafted based on Shneiderman's Eight Golden Rules of Interface Design. Subsequently, the design solutions were evaluated through moderated usability testing (UT) and the System Usability Scale (SUS) questionnaire distribution. The second iteration's UT results indicated improved success rates, reduced task completion times, higher SUS scores, and positive user feedback on ease of use. In conclusion, this research improved the application's usability and offered valuable recommendations for further enhancements and future investigations.

Copyright Content User Licensing Model for Collective Management Organizations in Uganda

Collective Management Organizations (CMOs) negotiate and collect royalties for intellectual works on behalf of its owners. These royalties fund Collective Management Organizations (CMO) activities, with portions going to copyright holders and the government (VAT). Higher education institutions make substantial contributions to copyright as makers and users of copyrighted works. CMOs attempt to collect royalties from these creators in order to preserve their confidence. Throughout the royalty collection process, Collective Management Organizations (CMO) engage with and gather a large amount of user data. This paper suggests a copyright user licensing approach to help Ugandan CMOs manage their licensing operations. The model includes gathering user information, determining requirements, and granting licenses. Data security in the central database is a major concern. Object-oriented programming (OOAD) is used to develop a trial system for user evaluation. The project involved collaboration with CMO staff, copyright users, and IP practitioners across several African countries. Feedback was gathered through interviews, focus groups, and user testing of a system prototype. The tested prototype was assessed for user-friendliness, navigation ease, consistency, usability, task completion time, and information access speed. The study concludes that a copyright user licensing model can improve licensing processes for CMOs and reduce copyright infringement. It further suggests that this model can be adapted for use by other licensing firms and organizations. Keywords: Collective Management Organization (CMO), Copyright user licensing, Copyright royalties, Higher education institutions, IP, Data security, OOAD, Copyright infringement Journal Reference Format: Lyada E , Egho-Promise E. I.,John,K.A. & Mustapha, A.B. (2024): Copyright Content User Licensing Model for Collective management Organizations in Uganda. Advances in Multidisciplinary Research Journal. Vol. 2.No. 4, Pp1-16. Vol. 10. No. 1. Pp 17-40 www.isteams.net/aimsjournal. dx.doi.org/10.22624/AIMS/BHI/V10N1P2

EGCY-Net: An ELAN and GhostConv-Based YOLO Network for Stacked Packages in Logistic Systems

Dispatching, receiving, and transporting goods involve a large amount of manual effort. Within a logistics supply chain, a wide variety of transported goods need to be handled, recognized, and checked at many different points. Effective planning of automated guided vehicle (AGV) transportation can reduce equipment energy consumption and shorten task completion time. As the need for efficient warehouse logistics has increased in manufacturing systems, the use of AGVs has also increased to reduce working time. These processes hold automation potential, which we can exploit by using computer vision techniques. We propose a method for the complete automation of box recognition, covering both the types and quantities of boxes. To do this, an ELAN and GhostConv-based YOLO network (EGCY-Net) is proposed with a Conv-GhostConv Stack (CGStack) module and an ELAN-GhostConv Network (EGCNet). To enhance inter-channel relationships, the CGStack module captures complex patterns and information in the image by using ghost convolution to increase the model inference speed while retaining the ability to capture spatial features. EGCNet is designed and constructed based on ELAN and the CGStack module to capture and utilize hierarchical features efficiently in layer aggregation. Additionally, the proposed methodology involves the creation of a dataset comprising images of boxes taken in warehouse settings. The proposed system is realized on the NVIDIA Jetson Nano platform, using an Arducam IMX477 camera. To evaluate the proposed model, we conducted experiments with our own dataset and compared the results with some state-of-the-art (SOTA) models. The proposed network achieved the highest detection accuracy with the fewest parameters compared to other SOTA models.

Comparing spatial navigation in a virtual environment vs. an identical real environment across the adult lifespan

Virtual environments (VEs) are increasingly being used as a research platform for investigating human responses to environmental variables. While VEs provide tremendous advantages in terms of variable isolation and manipulation, and ease of data-collection, some researchers have expressed concerns about the ecological validity of VE-based findings; that is, their transferability to real-world contexts. In the current study we created a virtual replica of a real-world, multi-level educational facility, and compared data collected in the VE vs. the real-world environment as participants completed identical navigational tasks. We found significant differences in all of the measures used, including distance covered, number of mistakes made, time for task completion, spatial memory, extent of backtracking, observation of directional signs, perceived uncertainty levels, perceived cognitive workload, and perceived task difficulty. We also analyzed potential age-related effects to look for heightened VE/real-world response discrepancies among older adult participants (age 55 or over) compared to younger adults (18–30 years of age). This analysis yielded no significant age-by-condition interaction effects. Finally, we examined the spatial distribution of self-reported wayfinding uncertainty across the building floorplan, finding that areas in which uncertainty was most pronounced were similar between the real-world and VE settings. Overall, these findings indicate that while virtual reality can be a useful design research tool, caution is needed when interpreting and generalizing its results.

Optimization of a task schedule for teams with members having various skills

We consider the real-life problem of planning tasks for teams in a corporation, in conditions of some restrictions. The problem takes into account various constraints, such as for instance flexible working hours, common meeting periods, time set aside for self-learning, lunchtimes and periodic performance of tasks. Additionally, only a part of the team may participate in meetings, and each team member may have their own periodic tasks such as self-development. We propose an algorithm that is an extension of the algorithm dedicated for scheduling on parallel unrelated processors with the makespan criterion. Our approach assumes that each task can be defined by a subset of employees or an entire team. However, each worker is of a different efficiency, so task completion times may differ. Moreover, the tasks are prioritized. The problem is NP-hard. Numerical experiments cover benchmarks with 10 instances of 100 tasks assigned to a 5-person team. For all instances, various algorithms such as branch-and-bound, genetic and tabu search have been tested.

Maintaining the completion-time mechanism for Greening tasks scheduling on DVFS-enabled computing platforms

The key factor in reducing the consumed energy when dependent-tasks applications are scheduled on DVFS-enabled computing platforms is task execution time slots. The unique and axiomatic approach to reduce the energy consumption on such platforms involves scaling down the execution frequency of each task within its execution time slot, provided a suitable scaling-down frequency is available. Regrettably, scheduling algorithms often shrink task execution time slots due to minimizing task completion times. This paper presents BlueMoon, a mechanism that reschedules the application tasks to extend the execution time slot of each task while ensuring that the overall completion time of the application tasks remains unaffected. BlueMoon is implemented and tested on numerous schedules of application graphs. The experimental results, conducted through computer simulations, demonstrate that BlueMoon substantially extends the execution time slots of tasks when compared to other mechanisms.

Distributed short-term predictive control for AUV clusters in underwater cooperative hunting tasks

A distributed short-term predictive control method is proposed to be applied to autonomous underwater vehicle (AUV) clusters for underwater cooperative hunting tasks. To support AUV clusters in unknown underwater dynamic environments, the constraints of information acquisition, AUV motion constraints, collision avoidance, and smoothness requirements of motion paths in the tasks are considered. First, a distributed multi-constraint optimization model including target attraction, interactions between AUVs, continuous obstacle avoidance, and control effort is proposed. Effective information between AUVs is extended under limited information conditions. Second, each AUV solves the distributed optimization problem and determines the desired trajectory in real time using information from the prediction horizon. In the simulation experiments, the proposed method is demonstrated to provide at least 12.19%, 11.08%, and 79.89% improvement in terms of task completion time, energy consumption, and path smoothing, respectively, when compared to other methods under the same information conditions. The robustness of the proposed method under different noise conditions is verified by positioning noise experiments, and the task can still be accomplished even if the noise level reaches 75% of the safe distance. The adaptability of the proposed method is demonstrated by simulations in different environments.

Assessment of an arm-support exoskeleton on physical demands, task performance, and usability during simulated agricultural tasks

This laboratory-based study aimed to determine whether an arm-support exoskeleton (ASE) would be an effective intervention to reduce the physical strain associated with manual agricultural work. Twenty-four (gender-balanced) participants performed pruning and harvesting tasks (at four different heights: knee, elbow, shoulder, and overhead), lifting/lowering, and Timed Up & Go (TUG) tasks with and without an ASE. During these tasks, muscle activity (in the upper trapezius, anterior deltoid, biceps brachii, and erector spinae), task completion time, perceived exertion, and usability were assessed. The results indicated a significant reduction (31.7%–60.2%) in muscle activity, particularly in the upper trapezius and anterior deltoid, when using the ASE during tasks at shoulder work height or above. However, there was an observed increase in muscle strain in the erector spinae, suggesting potential risk to the lower back. Given these findings, a more rigorous evaluation of ASEs in agricultural tasks should be pursued before implementing exoskeletons in agricultural tasks to avoid unintended health hazards. Relevance to industryA relatively new application of exoskeleton technology, extensively studied in fields like rehabilitation, manufacturing, and the military, is its use in agriculture. This study details the physical requirements of specific tasks to offer insights into the challenges that exoskeleton technologies for agriculture may encounter.

The virtual reality classroom: a randomized control trial of medical student knowledge of postpartum hemorrhage emergency management.

To investigate the use of a virtual reality learning environment (VRLE) to enhance medical student knowledge of postpartum hemorrhage (PPH) emergency management and insertion of a postpartum balloon. A randomized control trial involving medical students from University College Dublin, Ireland. Participants were randomly allocated to the intervention group (VRLE tutorial) or control group (PowerPoint tutorial on the same topic). All participants completed pre-learning experience and post-learning experience surveys. Both groups were timed and assessed on postpartum balloon insertion technique on a model pelvis. The primary outcome was assessment of student knowledge. Secondary outcomes included confidence levels, time taken to complete the task, technique assessment, satisfaction with the learning environment, and side effects of VR. Both learning experiences significantly (p < 0.001) enhanced student performance on the post-learning experience multiple choice questionnaire, with no difference between the intervention and control groups. In the intervention group, time for task completion was significantly less compared to the control group (1-2 min vs. 2-3 min, p = 0.039). Both learning experiences significantly (p < 0.001) enhanced student confidence, with no significant difference between intervention and control groups. 100% of the students using the VRLE enjoyed the experience, and 82.4% were very likely to recommend use of VRLE in medical education. 94.1% of the students felt the VRLE was beneficial over didactic teaching. Receiving formal instruction, regardless of format, enhances students' knowledge and confidence of the topic covered. Students who received instruction via the VRLE assembled the postpartum balloon faster than students who received didactic teaching. VR may be beneficial in teaching hands-on procedural skills in obstetrics and gynecology education.

A spectral clustering-based deployment strategy for roadside units in vehicular edge computing environments

Roadside units deployment strategy in vehicular edge computing (VEC) environments plays a crucial role in balancing deployment expenses with user experience. This paper tackles this challenge by formulating the communication and computation cost of offloading vehicle tasks to RSUs and, subsequently, optimizing the RSU deployment problem to minimize the average task completion time. We propose the spectral clustering-based deployment strategy (SCBDS), which determines the optimal number and precise locations of RSUs by identifying clusters of vehicular traffic demands. Performance evaluation through simulations in real-world traffic scenarios demonstrates that SCBDS outperforms existing algorithms in terms of task completion time, showcasing its effectiveness in enhancing VEC efficiency and user experience.

Enhancing User Experience Using a Framework Integrating Emotion Recognition and Eye-Tracking

In a rapidly evolving digital landscape, ensuring a seamless and engaging user experience (UX) has become paramount. This study delves into the intricate realm of web interaction design, aiming to enhance user satisfaction and engagement. Through the integration of emotion recognition and eye-tracking technologies, a profound relationship between user emotions and web interface design is unveiled. The central theme of this research revolves around the integration of emotion recognition and eye-tracking technologies to evaluate web interaction designs. An experimental study involving 24 participants from diverse backgrounds and age groups was conducted. These participants navigated web interfaces that featured both emotion recognition and eye-tracking technologies. Three distinct tasks were carefully crafted to represent a spectrum of web interactions, ranging from basic navigation to complex decision-making. Data collection involved real-time valence and arousal scores, video recordings, visual attention patterns, task completion times, and questionnaires. The analysis revealed a series of significant discoveries. Users who engaged with simplified web versions consistently exhibited elevated valence values, indicative of heightened positive emotional feedback, diverging starkly from their counterparts navigating complex web iterations. The dynamic ebb and flow of emotional states, underscored by arousal levels, underscore the pivotal role of real-time emotional assessment in the critical evaluation of web interfaces. Moreover, the study unveiled a persistent preference for a state of emotional calmness during interactions, demonstrating a universal need for user-centered design principles that prioritize minimal cognitive load and emotional tranquility. In summation, this research contributes a robust framework to the design community and academia for the comprehensive evaluation of web interaction designs. The findings underscore the paramount significance of simplicity, real-time emotional evaluation, and unwavering adherence to user-centered design principles in the realm of web interaction. This study constitutes an invaluable repository for designers, developers, and researchers, steering their endeavors towards the relentless pursuit of optimized user experiences within the ever-evolving digital landscape.

Effects of Instability Neuromuscular Training Using an Inertial Load of Water on the Balance Ability of Healthy Older Women: A Randomized Clinical Trial.

The reflexive responses to resist external forces and maintain posture result from the coordination between the vestibular system, muscle, tendon, and joint proprioceptors, and vision. Aging deteriorates these crucial functions, increasing the risk of falls. This study aimed to verify whether a training program with water bags, an Instability Neuromuscular training (INT) using the inertial load of water, could positively impact balance ability and dynamic stability. This study was conducted with twenty-two healthy older women aged ≥ 65 (mean age: 74.82 ± 7.00 years, height: 154.20 ± 5.49 cm, weight: 55.84 ± 7.46 kg, BMI: 23.55 ± 3.58 kg/m2). The participants were randomly allocated into two groups: a group that used water bags and a control group performing bodyweight exercises. The intervention training lasted 12 weeks, with 2 sessions per week totaling 24 sessions, each lasting 60 min. The pre- and post-tests were compared using t-tests to examine within- and-between-group differences. The effect size was examined based on the interaction between group and time using a two-way repeated measures ANOVA. The Modified Timed Up and Go manual (TUG manual), Sharpened Romberg Test (SRT), and Y-balance test (YBT) were conducted to assess dynamic stability, including gait function, static stability, and reactive ability. In comparison between groups, the waterbag training group showed a decrease in task completion time associated with an increase in walking speed in the TUG manual test (p < 0.05), and an increase in static stability and reaction time in the Sharpened Romberg test with eyes closed (p < 0.05), and an increase in single-leg stance ability in both legs in the Y-balance test (p < 0.05). All statistical confidence interval levels were set 95%. INT using the inertial load of water enhanced the somatosensory system and gait related to dynamic stability in older women. Therefore, the clinical application of this training program is expected to reduce the risk of falls in healthy older women, improving dynamic stability related to gait.

Resource Allocation Scheduling Optimization Based on Internet of Things under Cloud Platforms

In this paper, we explore how to optimize IoT-based resource allocation and scheduling in a cloud platform environment, focusing on improving computing resource utilization and quality of service, while reducing latency and packet loss. a model is adopted, which contains a number of edge servers and randomly generated computational tasks, taking into account the network conditions between the servers and the tasks. an objective function is established, aiming to maximize the computational resource utilization and QoS, and the corresponding constraints are proposed. Simulations are conducted using CloudSim, and the experimental results show that the total number of VoCS increases from 243.63 to 1397.71 when the scheduling demand is increased from 8 to 64, demonstrating the adaptability and efficiency of the algorithm under different demands. In addition, the algorithm is effective in dealing with both small-scale (200 tasks) and large-scale (6000 tasks) tasks. In addition, the algorithm demonstrates low load imbalance and short task completion time when dealing with both small-scale (200 tasks) and large-scale (6000 tasks) task sets, which proves its effectiveness. Ultimately, the scheduling method proposed in this study not only improves resource utilization and quality of service, but also reduces task completion time and cost.