Task Deadlines Research Articles

Factors Influencing the Bid‐Winning of Respondents in the Context of Crowdsourcing

ABSTRACTThis article takes crowdsourcing as the research background, collects secondary data from the Yipin Freelancer website, constructs panel data of “respondent–date,” and uses the research framework of the elaboration likelihood model (ELM) to study the key factors influencing the bid‐winning probability of respondents. The study found that for the central path, there is a nonlinear inverted U‐shaped relationship between the respondents' success experience and skill diversity and the bid‐winning probability. For the peripheral path of task settings, the study found that a larger task bonus, more bonus recipients, and longer task deadlines are beneficial for respondents to win bids successfully. Finally, this article discusses the theoretical and practical significance of the research.

An electricity price and energy-efficient workflow scheduling in geographically distributed cloud data centers

The cloud computing platform has become a favorable destination for running cloud workflow applications. However, they are primarily complicated and require intensive computing. Task scheduling in cloud environments, when formulated as an optimization problem, is proven to be NP-hard. Thus, efficient task scheduling plays a decisive role in minimizing energy costs. Electricity prices fluctuate depending on the vending company, time, and location. Therefore, optimizing energy costs has become a serious issue that one must consider when building workflow applications scheduling across geographically distributed cloud data centers (GD-CDCs). To tackle this issue, we have suggested a dual optimization approach called electricity price and energy-efficient (EPEE) workflow scheduling algorithm that simultaneously considers energy efficiency and fluctuating electricity prices across GD-CDCs, aims to reach the minimum electricity costs of workflow applications under the deadline constraints. This novel integration of dynamic voltage and frequency scaling (DVFS) with energy and electricity price optimization is unique compared to existing methods. Moreover, our EPEE approach, which includes task prioritization, deadline partitioning, data center selection based on energy efficiency and price diversity, and dynamic task scheduling, provides a comprehensive solution that significantly reduces electricity costs and enhances resource utilization. In addition, the inclusion of both generated and original data transmission times further differentiates our approach, offering a more realistic and practical solution for cloud service providers (CSPs). The experimental results reveal that the EPEE model produces better success rates to meet task deadlines, maximize resource utilization, cost and energy efficiencies in comparison to adapted state-of-the-art algorithms for similar problems.

Research on Public Policies to Promote the Organic Connection Between Rural Revitalization and Poverty Alleviation

Poverty alleviation is foundational to maintaining Chinas comprehensive progress towards a moderately prosperous society. Rural revitalization is a crucial measure to further realize the modernization of agriculture and rural areas. The organic connection between these two is a focal issue in the development process of China's agricultural and rural areas, playing a strategic support role in achieving Chinas second centennial goal. Based on this, this paper focuses on the organic connection between poverty alleviation and rural revitalization in the new era. By summarizing the basic background and overall connotations of both, and employing a research methodology that integrates theory with practice, this study explores the achievements and inherent logical relations of poverty alleviation and rural revitalization at this stage, as well as the strategic significance of their integration in various aspects. With the shift of strategic focus to the "three rural issues," promoting the organic connection between poverty alleviation and rural revitalization may face challenges from group coverage, spatial scope, work objectives, task deadlines, and policy allocation in the future. Therefore, this paper further proposes feasible suggestions for the implementation of governance policies that promote the organic connection between poverty alleviation and rural revitalization from the perspectives of people, society, organizations, institutions, and development.

Wireless and Service Allocation for Mobile Computation Offloading With Task Deadlines

Wireless and Service Allocation for Mobile Computation Offloading With Task Deadlines

A greedy randomized adaptive search procedure for scheduling IoT tasks in virtualized fog–cloud computing

AbstractVirtualized fog–cloud computing (VFCC) has emerged as an optimal platform for processing the increasing number of emerging Internet of Things (IoT) applications. VFCC resources are provisioned to IoT applications in the form of virtual machines (VMs). Effectively utilizing VMs for diverse IoT tasks with varying requirements poses a significant challenge due to their heterogeneity in processing power, communication delay, and energy consumption. In addressing this challenge, in this article, we propose a system model for scheduling IoT tasks in VFCCs, considering not only individual task deadlines but also the system's overall energy consumption. Subsequently, we employ a greedy randomized adaptive search procedure (GRASP) to determine the optimal assignment of IoT tasks among VMs. GRASP, a metaheuristic‐based technique, offers appealing characteristics, including simplicity, ease of implementation, a limited number of tuning parameters, and the potential for parallel implementation. Our comprehensive experiments evaluate the effectiveness of the proposed method, comparing its performance with the most advanced algorithms. The results demonstrate that the proposed approach outperforms the existing methods in terms of deadline satisfaction ratio, average response time, energy consumption, and makespan.

Energy efficient Security Driven Scheduling for Real-Time Tasks through DVS-enabled Fog Networks

The issue of task scheduling for a multi-core processor in Fog networks, with a focus on security and energy efficiency is of great importance in real-time systems. Currently, scheduling algorithms designed for cluster computing environments utilize dynamic voltage scaling (DVS) to decrease CPU power consumption, albeit at the expense of performance. This problem becomes more pronounced when a real-time task requires robust security, resulting in heavily overloaded nodes (CPUs or computing systems) in a cluster computing environment. To address such challenges, a solution called “Energy efficient Security Driven Scheduling of Real-Time Tasks using DVS-enabled Fog Networks (ESDS)” has been proposed. The primary goal of ESDS is to dynamically adjust CPU voltages or frequencies based on the workload conditions of nodes in Fog networks, thereby achieving optimal trade-offs between security, scheduling, and energy consumption for real-time tasks. By dynamically reducing voltage or frequency levels, ESDS conserves energy while still meeting deadlines for both running and new tasks, especially during periods of high system workload. Comprehensive experiments have been carried out to compare the ESDS algorithm with established baseline algorithms, including MEG, MELV, MEHV, and AEES. These experiments affirm the originality and effectiveness of the ESDS algorithm.

ETFC: Energy-efficient and deadline-aware task scheduling in fog computing

The Internet of Things (IoT) is constantly evolving and expanding. However, due to the limited IoT resources, it is intertwined with fog computing to use their resources to compensate for the limitations of IoT resources. On the other hand, fog devices face challenges, such as resource heterogeneity, high distribution, dynamism, and limitations, so an efficient task scheduling approach is needed to deploy fog computing resources effectively and improve the quality of service (QoS). This work mathematically formulates the task scheduling problem to minimize energy consumption and cost and improve QoS by reducing response time and deadline violation times of IoT tasks. Then, it proposes an Energy-efficient and deadline-Aware Task scheduling in Fog Computing (ETFC) method that predicts the traffic of fog nodes by a Support Vector Machine (SVM) and divides them into low-traffic and high-traffic groups. Next, the ETFC method schedules the low-traffic part with an algorithm based on reinforcement learning using the proposed ICLA-SOA, which is an algorithm based on irregular cellular learning automata and schedules the tasks of the high-traffic part with a metaheuristic algorithm using the proposed Non-dominated Sorting Genetic Algorithm (NSGA-III). The simulation results demonstrate that the ETFC method exhibits up to an 84 % enhancement in response time, up to a 33 % reduction in energy consumption, up to a 30 % decrease in costs, and up to a 28 % advancement in meeting task deadlines compared to other methods.

CoTEV: Trustworthy and Cooperative Task Execution in Internet of Vehicles

Due to the increasing number of service requests from the vehicles, the load at the road side units (RSUs) increases, which affects the delay-sensitive vehicle services. In Internet of Vehicles (IoV), the vehicles can communicate directly with other vehicles and take help from the vehicles to cooperatively accomplish a task. However, it is very challenging to cooperatively execute a task in an IoV environment with high traffic and dynamic vehicle movements. Furthermore, it is difficult for a task vehicle to choose trustworthy and cooperative vehicles. In this paper, we propose algorithms for cooperative task execution by taking the help of trusted vehicles, when it is not possible to complete a deadline-specified task through the RSUs. We propose a hedonic coalition formation game-based approach to form distributed coalitions of cooperative vehicles. We consider the trust score of the vehicles along with their computational capabilities and journey routes. After each task execution, the service feedback is reflected in the trust score of each cooperative vehicle in the coalition. Our proposed algorithms allow the cooperative vehicles to autonomously choose the coalitions and select a vehicle task to maximize their payoffs. To satisfy the task deadlines in multiple coalitions, we design the merging of vehicle coalitions. We consider the simulation of urban mobility (SUMO) tool to generate the mobility traces of the vehicles in a real road network of Berlin city, which considers the traffic junctions and vehicle density on the roads. Through extensive simulations, we show that the proposed algorithms significantly increase the service rate of delay-sensitive task requests by at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$30.5 \%$</tex-math></inline-formula> and the trust score by at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$20.61 \%$</tex-math></inline-formula> , compared to the benchmark schemes.

TCV-D: An Approach for Path Selection in Vehicular Task Offloading

The vehicular task offloading technology enhances the transportation system by offloading tasks of task-requesting vehicles (TRV) to either vehicular fog nodes (VFN) or road-side units (RSU) using single-hop or multi-hop communication. Due to the limited availability of VFN/RSU within a single-hop of TRV, a multi-hop path is employed for offloading tasks from TRV to VFN/RSU. However, the multi-hop path selection approaches have several issues, such as frequent re-connections due to varying vehicle speed, lower successful offloading when task deadline is missed, increased outage time when no VFN/RSU is within communication range of TRV, and inefficient vehicle selection to offload task leading to longer path lifetime. To address these issues, we have proposed a novel approach called Time-Computation-Variance based deadline sensitive path selection (TCV-D), which considers contextual information from k-hop neighbors. The approach offers four offloading modes: Direct mode, RSU mode, VFN mode, and Search mode, depending on the availability of RSUs/VFNs in single and multi-hop scenarios. To ensure tasks are delivered within deadlines, the proposed approach executes tasks by task forwarding vehicle or neighbor of task forwarding vehicle instead of designated VFN/RSU if delivering the task directly to the destination would exceed the deadline constraint. Extensive result analysis demonstrates substantial improvements compared to the existing k-hop-limited offloading time-based path selection (k-hop-limited-OTS) approach, including a 60% reduction in re-connections, a 35% decrease in path life time, a 30% decrease in outage time, and an 84% increase in successful offloading ratio, approximately.

Online two-timescale service placement for time-sensitive applications in MEC-assisted network: A TMAGRL approach

Mobile edge computing (MEC) integrated with the Network Functions Virtualization (NFV) technique has been regarded as a promising solution for flexible services provision and user service experience improvement. However, existing service placement in such systems still faces the challenge of satisfying computing tasks with strict latency requirements, especially when massive mobile users roam around different coverage areas of edge servers. For this purpose, we first adopt a novel service placement framework that combines proactive replicas pre-deployment and reactive service migration. Based on this, we investigate the dynamic placement problem of multiple types of services achieved by the various virtualized network functions (VNFs) to minimize long-term redeployment costs in MEC-assisted systems, subject to the completion deadline of tasks and limited computing resources of edge servers. Considering that the update timescale of VNF replicas pre-deployment is different, we design a novel two-timescale multi-agent graph convolutional network-based reinforcement learning algorithm (TMAGRL) by invoking a long-timescale training layer for proactive VNF replicas placement and a short-timescale training layer for reactive VNF migration. Extensive numerical results reveal that TMAGRL, based on the designed hybrid framework, can learn a VNF placement strategy to adapt to the dynamics of the system without any prior information. Moreover, we verify its superior performance in terms of average service response latency and overall redeployment cost by comparing it with baselines.

Attentional Biases and Their Association with Substance-Use-Related Problems and Addictive Behaviors: The Utility of a Gamified Value-Modulated Attentional Capture Task

BackgroundAttentional biases towards reward stimuli have been implicated in substance use-related problems. The value-modulated attentional capture (VMAC) task assesses such reward-related biases. The VMAC task widely used in lab studies tends to be monotonous and susceptible to low effort. We therefore tested a gamified online version of the VMAC that aimed to increase participant engagement. Our goal was to examine how VMAC is associated with substance use-related problems and addictive behaviors, and whether this association is moderated by cognitive control. MethodsWe recruited 285 participants from an online community, including heavy alcohol users. All participants completed a novel gamified version of the VMAC task, measures of substance use and addictive behaviors (addictive-like eating behavior, problematic smartphone use), the WebExec measure of problems with executive functions, and the Stroop Adaptive Deadline Task (SDL) as a measure of cognitive control. ResultsThe gamified VMAC task successfully identified value-modulated attentional capture effects towards high-reward stimuli. We found no significant associations between VMAC scores, problematic alcohol or cannabis use, addictive behaviors, or any moderation by a behavioral measure of cognitive control. Exploratory analyses revealed that self-reported cognitive problems were associated with more alcohol-, and cannabis-related problems, and addictive behaviors. Greater attentional capture (VMAC) was associated with more cannabis use-related problems among individuals with higher levels of self-reported cognitive problems. ConclusionsOur study is one of the first to demonstrate the utility of the gamified version of the VMAC task in capturing attentional reward biases. Self-reported problems with cognitive functions represent a key dimension associated with substance use-related problems and addictive behaviors.

Internet of Vehicles (IoV)-Based Task Scheduling Approach Using Fuzzy Logic Technique in Fog Computing Enables Vehicular Ad Hoc Network (VANET)

The intelligent transportation system (ITS) relies heavily on the vehicular ad hoc network (VANET) and the internet of vehicles (IoVs), which combine cloud and fog to improve task processing capabilities. As a cloud extension, the fog processes’ infrastructure is close to VANET, fostering an environment favorable to smart cars with IT equipment and effective task management oversight. Vehicle processing power, bandwidth, time, and high-speed mobility are all limited in VANET. It is critical to satisfy the vehicles’ requirements for minimal latency and fast reaction times while offloading duties to the fog layer. We proposed a fuzzy logic-based task scheduling system in VANET to minimize latency and improve the enhanced response time when offloading tasks in the IoV. The proposed method effectively transfers workloads to the fog computing layer while considering the constrained resources of car nodes. After choosing a suitable processing unit, the algorithm sends the job and its associated resources to the fog layer. The dataset is related to crisp values for fog computing for system utilization, latency, and task deadline time for over 5000 values. The task execution, latency, deadline of task, storage, CPU, and bandwidth utilizations are used for fuzzy set values. We proved the effectiveness of our proposed task scheduling framework via simulation tests, outperforming current algorithms in terms of task ratio by 13%, decreasing average turnaround time by 9%, minimizing makespan time by 15%, and effectively overcoming average latency time within the network parameters. The proposed technique shows better results and responses than previous techniques by scheduling the tasks toward fog layers with less response time and minimizing the overall time from task submission to completion.

Influence of Work Motivation and Workload on Work Fatigue among Mechanic at the Train Maintenance Centre in Yogyakarta

The amount of risk and job demands, and monotony of work and task deadlines that are too short, can affect the decrease in employee work motivation. Work motivation can affect work fatigue through work capacity, workload and work stress. The purpose of this study was to determine the effect between work motivation and workload on work fatigue in employees of Maintenance Center Unit (UPT) Balai Yasa Yogyakarta in the production department. The research method used is quantitative research with a correlational approach. The sampling process is carried out using the purposive sampling method, with criteria for being 20 – 56 years old and having worked at UPT Balai Yasa Yogyakarta for at least 1 year. The instrument used to collect the data in this study were work fatigue scale, work motivation scale, and workload scale. The results of this study show that: work motivation has an influence on the condition of work fatigue of UPT Balai Yasa Yogyakarta employees, workload also has an influence on work fatigue of UPT Balai Yasa Yogyakarta employees, and motivation and workload have an influence together on the condition of work fatigue of UPT Balai Yasa Yogyakarta employees.

GA-based QOS-aware workflow scheduling of deadline tasks in grid computing

Grid computing is the aggregation of the power of heterogeneous, geographically distributed computing resources to provide high-performance computing. To benefit from the grid computing capabilities, effectual scheduling algorithms are primarily essential. This paper presents a GA-based approach, called Grid Workflow Tasks Scheduling Algorithm (GWTSA), for scheduling workflow tasks on grid services based on users’ QoS (quality of service) constraints in terms of cost and time. For a given set of inter-dependent workflow tasks, it generates an optimal schedule, which minimizes the execution time and cost, such that the optimized time be within the time constraints (deadline) imposed by the user. In GWTSA, the workflow tasks are modeled as a DAG, which is divided, then the optimal sub-schedules of all task divisions are computed and used to obtain the execution schedule of the entire workflow. A GA-based technique is employed in GWTSA to compute the optimal execution sub-schedule for each branch division that consists of a set of sequential tasks. In this technique, the chromosome represents a branch division, where each gene holds the id of the service provider chosen to execute the corresponding task in the branch; and the fitness function is formulated as a multi-objective function of time and cost, this gives users the ability to determine their requirements if speed against cost or vice versa, by changing the weighting coefficients in the fitness function. The paper also exhibits the experimental results of assessing the performance of GWTSA with workflow samples of different sizes.

Deadlines Meeting Challenge in Leaders

Purpose: This paper looks at the concept of deadlines meeting challenges by leaders and how this problem affects performance and goal attainment in organizations. Deadlines may be defined as prescribed or expected timelines for task accomplishment and submission. It forms a framework within which a task or assignment should be performed so as to succeed in goal attainment. The paper focuses on why deadline meeting has become such a huge challenge to leaders in organizations and the challenges this problem poses to performance in work places.&#x0D; Materials and Methods: It was assumed in this presentation that there seems to be no proper connectivity and meaningful engagement between those who make task deadlines and those who are expected to carry out tasks and meet deadlines.&#x0D; Findings: The paper will then proceed to suggest best practices in dealing with deadlines so as to promote goal attainment.&#x0D; Implications to Theory, Practice and Policy: The paper concludes that lack of proper task accomplishment systems and implementation of best deadlines meeting practices are the major causes of failure to meet deadlines by most organizational leadership.

Heterogeneous GNN-RL-Based Task Offloading for UAV-Aided Smart Agriculture

Having unmanned aerial vehicles (UAVs) with edge computing capability hover over smart farmlands supports Internet of Things (IoT) devices with low processing capacity and power to accomplish their deadline-sensitive tasks efficiently and economically. In this work, we propose a graph neural network-based reinforcement learning solution to optimize the task offloading from these IoT devices to the UAVs. We conduct evaluations to show that our approach reduces task deadline violations while also increasing the mission time of the UAVs by optimizing their battery usage. Moreover, the proposed solution has increased robustness to network topology changes and is able to adapt to extreme cases, such as the failure of a UAV.

Collaborative computation offloading for scheduling emergency tasks in SDN-based mobile edge computing networks

Due to the growing demand for time-intensive tasks, several tasks coexist, some of which last for a long time or only occur once in a while, such as emergencies. Emergencies such as natural disasters, accidents or medical emergencies require immediate attention and often demand real-time execution. Such tasks are the most difficult to plan for and foresee. Planning, resource allocation, and clear communication between all involved parties and network layers are crucial for scheduling emergency tasks. In the extensive literature, when an emergency task triggers, it will be handled like any other regular task. If the deadline is missed, a catastrophe will ensue. They ignored the impact of insufficient mobile edge computing (MEC) capacity and network congestion, which might result in execution delays. Collaborative offloading, which involves distributing computation tasks across multiple servers, shows promise in enhancing MEC network performance, particularly in emergency scenarios. In this context, efficient task scheduling plays a vital role in minimizing the total execution time of regular tasks while meeting the deadlines of emergency tasks. Our proposed scheme utilizes a collaborative offloading approach to schedule emergency tasks in MEC networks, leveraging the computing capacity of edge-deployed MEC servers. By utilizing the software-defined networking (SDN)’s global view of the network, task requests are collected and allocated to suitable MEC servers capable of meeting the demands. To address key challenges, our scheme propose four scheduling algorithms to address the following issues: (i) ensuring tasks are assigned to the nearest MEC server with sufficient computational resources, (ii) controlling a threshold to prevent network congestion, (iii) selecting an optimal collaborative MEC server for executing overloaded tasks based on collaborative offloading decisions, and (iv) allocating resources for emergency tasks when triggered to meet urgent deadlines by stealing resources from regular tasks without compromising their deadlines. Extensive simulations were conducted to assess the effectiveness of the proposed scheme. The results clearly illustrate its enhanced performance in terms of the total execution time of regular tasks and the ability to meet deadlines for emergency tasks.

NASLMRP: Design of a Negotiation Aware Service Level Agreement Model for Resource Provisioning in Cloud Environments

Cloud resource provisioning requires examining tasks, dependencies, deadlines, and capacity distribution. Scalability is hindered by incomplete or complex models. Comprehensive models with low-to-moderate QoS are unsuitable for real-time scenarios. This research proposes a Negotiation Aware SLA Model for Resource Provisioning in cloud deployments to address these challenges. In the proposed model, a task-level SLA maximizes resource allocation fairness and incorporates task dependency for correlated task types. This process's new tasks are processed by an efficient hierarchical task clustering process. Priority is assigned to each task. For efficient provisioning, an Elephant Herding Optimization (EHO) model allocates resources to these clusters based on task deadline and make-span levels. The EHO Model suggests a fitness function that shortens the make-span and raises deadline awareness. Q-Learning is used in the VM-aware negotiation framework for capacity tuning and task-shifting to post-process allocated tasks for faster task execution with minimal overhead. Because of these operations, the proposed model outperforms state-of-the-art models in heterogeneous cloud configurations and across multiple task types. The proposed model outperformed existing models in terms of make-span, deadline hit ratio, 9.2% lower computational cycles, 4.9% lower energy consumption, and 5.4% lower computational complexity, making it suitable for large-scale, real-time task scheduling.

Dynamic Scheduling Stochastic Multiworkflows With Deadline Constraints in Clouds

Nowadays, more and more workflows with different computing requirements are migrated to clouds and executed with cloud resources. In this work, we study the problem of stochastic multi-workflows scheduling in clouds and formalize this problem as an optimization problem that is NP-hard. To solve this problem, an efficient stochastic multi-workflows dynamic scheduling algorithm called SMWDSA is designed to schedule multi-workflows with deadline constraints for optimizing multi-workflows scheduling cost. The proposed SMWDSA consists of three stages including multi-workflows preprocessing, multi-workflow scheduling and scheduling feedback. In SMWDSA, a novel task sub-deadlines assignment stretagy is design to assign the task sub-deadlines to each task of multi-workflows for meeting workflow deadline constraints. Then, we propose a task scheduling method based on the minimal time slot availability to execution task for minimizing workflow scheduling cost while meetingt workflow deadlines. Finally, a scheduling feedback strategy is adopted to update the priorities and sub-deadlines of unscheduled tasks, for further minimizing workflow scheduling cost. We conduct the experiments using both synthetic data and real-world data to evaluate SMWDSA. The results demonstrate the superiority of SMWDSA as compared with the state-of-the-art algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Workflow scheduling in clouds is significantly challenging due to not only the large scale of workflows but also the elasticity and heterogeneity of cloud resources. Moreover, minimizing workflow scheduling cost and satisfying workflow deadlines are two critical issues in scheduling with cloud resources, especially the uncertainty of workflow arrive time and task execution time are considered. To meet workflow deadlines, it is an effective strategy to decompose workflow deadline constraints into task sub-deadline constraints. To minimize the workflow scheduling cost, each task in a workflow needs to be assigned to their most suitable VMs for execution. This article presents a novel workflow scheduling algorithm to schedule stochastic multi-workflows in clouds for optimizing multi-workflows scheduling cost and meeting workflows deadlines. This algorithm obtains the task sub-deadline constraints based on the characteristics of workflows for meeting the worklfow deadline constraint. Under the premise of meeting task deadlines, it schedules tasks to a VM with minimum the slot time, for minimizing the cost. Case studies based on well-known real-world workflows data sets suggest that it outperforms traditional ones in terms of success and cost of multi-workflows scheduling. It can thus aid the design and optimization of multi-workflows scheduling in a cloud environment. It can help practitioners better manage the scheduling cost and performance of real-world applications built upon cloud services.

Pembinaan Karakter Bagi Anak-Anak Putus Sekolah Pada Program Kesetaraan Pendidikan di Kota Padang

This study seeks to elucidate the pivotal role played by community learning activity centers in fostering character development through educational activities. Employing a descriptive qualitative approach, this research employed purposive sampling to select informants. Data was gathered through interviews, observations, and documentation, with data validity established through source triangulation. Subsequently, data underwent analysis involving reduction, presentation, and conclusion drawing. The findings of this investigation reveal that PKBM Farilla Ilmi utilizes various coaching strategies (Package A, B, and C) to guide students, including setting an example, instilling habits, offering constructive feedback, and employing a relational approach. These strategies are primarily motivated by students' spiritual needs, with observed outcomes including notable improvements in student behavior. Specifically, students exhibit enhanced discipline in meeting task deadlines and punctuality. Moreover, they display increased politeness when interacting with adults, demonstrate respect for both parents and peers, and exhibit greater honesty.