Task Scheduling Research Articles

Implementing an intelligent learning-based algorithm for efficient task scheduling in cloud computing environments

ABSTRACT Cloud technology provides scaled resources to support real-world applications, which often adopt cloud infrastructure for storage and computing. As cloud platforms enlarge their user numbers, optimizing infrastructure usage and balancing the utility and satisfaction between the service provider and users under the Service Level Agreements (SLAs) becomes crucial. Since the dynamism from millions of user workloads makes task scheduling more challenging, especially in the emergence of energy-saving and real-time Quality of Service (QoS) requirements, traditional heuristic scheduling strategy fails to meet the more accurate and optimized operations. We propose an intelligent learning-based cloud task scheduling (ILbCTS) algorithm that leverages Deep Reinforcement Learning (DRL) technology to address this. The intelligent cloud task scheduling is a dynamic process that continuously adapts the action based on the changing states and the available rewards. The empirical studies with job sets of 1000, 5000 and 10,000 show that the ILbCTS algorithm outperforms the existing task scheduling algorithms, such as PSO, MBO and MOPSO, in terms of execution time, energy conservation and success rate of task scheduling.

Cloud-edge hybrid deep learning framework for scalable IoT resource optimization

In the dynamic environment of the Internet of Things (IoT), edge and cloud computing play critical roles in analysing and storing data from numerous connected devices to produce valuable insights. Efficient resource allocation and workload distribution are vital to ensuring continuous and reliable service in growing IoT ecosystems with increasing data volumes and changing application demands. This study proposes a novel optimisation approach utilising deep learning to tackle these challenges. The integration of Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) offers a practical approach to addressing the dynamic characteristics of IoT applications. The hybrid algorithm's primary characteristic is its capacity to simultaneously fulfil multiple objectives, including reducing response times, enhancing resource efficiency, and decreasing operational costs. DQN facilitates the formulation of optimal resource allocation strategies in intricate and unpredictable environments. PPO enhances policies in continuous action spaces to guarantee reliable performance in real-time, dynamic IoT settings. This method achieves an optimal equilibrium between policy learning and optimisation, rendering it suitable for contemporary IoT systems. This method improves numerous IoT applications, including smart cities, industrial automation, and healthcare. The hybrid DQN-PPO-GNN-RL model addresses bottlenecks by dynamically managing computing and network resources, allowing for efficient operations in low-latency, high-demand environments such as autonomous systems, sensor networks, and real-time monitoring. The use of Graph Neural Networks (GNNs) improves the accuracy of resource representation, while reinforcement learning-based scheduling allows for seamless adaptation to changing workloads. Simulations using real-world IoT data on the iFogSim platform showed significant improvements: task scheduling time was reduced by 21%, operational costs by 17%, and energy consumption by 22%. The method reliably provided equitable resource distribution, with values between 0.93 and 0.99, guaranteeing efficient allocation throughout the network. This hybrid methodology establishes a novel benchmark for scalable, real-time resource management in extensive, data-centric IoT ecosystems, consequently enhancing system performance and operational efficiency.

An Improved Laxity based Cost Efficient Task Scheduling Approach for Cloud-Fog Environment

Task scheduling is critical in fog computing, as it has to assign workloads to fog nodes to save costs and execution times. This study emphasizes the allocation of jobs received from clients to suitable nodes through a proposed scheduling technique, which is deployed on layer 2 servers within a cloud-fog environment. Laxity-based Cost-efficient Task Scheduling (LCTS) is proposed for contemporary task scheduling difficulties, such as balancing cost and delay with optimal energy utilization. The results show that the proposed strategy decreased execution time and cost more than Round Robin (RR) and Genetic Algorithm (GA). Furthermore, the proposed method was less expensive than cloud-based IoT solutions. Compared to GA and RR, the simulation results showed that cost and execution time were reduced by 6.99%-17.36% and 4.58%-9.09%, respectively.

ANALYSIS OF THE IMPACT OF TASK PRIORITIZATION LISTS ON THE POTENTIAL FOR AVOIDING ANOMALIES IN TASK SCHEDULING

This paper addresses relevant issues related to the anomalous deterioration in objective function values when attempting to improve the initial parameters in one of the discrete optimization problems. The primary focus is on investigating the conditions under which it is possible to prevent the occurrence of such anomalies. Contemporary scientific works devoted to schedule optimization and task prioritization management, particularly for location-allocation problems arising in the fields of computer science, engineering, and operations research, are reviewed. A priority dynamic redistribution algorithm is proposed, which al-lows minimizing delays and ensuring efficient resource utilization during parallel task execu-tion. An example of applying the algorithm is provided, and its effectiveness in preventing anomalies.

An Evolutionary Review on Resource Scheduling Algorithms Used for Cloud Computing with IoT Network

The goal of the distributed computing paradigm known as "cloud computing," which necessitates a large number of resources and demands, is to share the resources as services delivered over the internet. Task scheduling is a very significant stage in today's cloud computing. While lowering the makespan and cost, the task scheduling method must schedule the tasks to the virtual machines. Various academics have proposed many scheduling methods for organizing work in cloud computing environments. Scheduling has been considered the most important for cloud computing since it might directly impact a system's performance, including the efficiency of resource utilization and running costs. This paper has compared all the already used algorithms that work on different parameters. We have tried to give better solutions for resource allocation and resource scheduling. In this study, various swarm optimization, evolutionary, physical, evolving, and fusion meta-heuristic scheduling methods are categorized according to the environment of the scheduling problem, the main scheduling goal, the task-resource mapping pattern, and the scheduling constraint. More specifically, the fundamental concepts of cloud task scheduling are addressed without difficulty.

Clustering and Network Analysis of Mobility Patterns as an Analysis Tool for Lean Project

The study aims to optimize internal logistics processes by applying Lean philosophy and data science tools, with a primary focus on qualifying processes to determine their value-added contribution within the logistics context. Utilizing a novel two-step methodology, the research first employs a modified DBSCAN algorithm to analyze indoor positioning data and categorize activities. This is followed by multi-layer network modeling to understand processes and create a framework that enables the reduction of idle activities through optimization algorithms. A real warehouse case study, using a UWB-based Indoor Positioning System (IPS) to track forklifts, demonstrates the method's effectiveness in identifying non-value-added activities. The results reveal specific opportunities for reducing idle, enhancing resource utilization, and improving operational efficiency. This innovative combination of advanced data analysis techniques and Lean principles provides a comprehensive framework for logistics optimization, significantly enhancing process efficiency through optimized task scheduling and resource allocation. Doi: 10.28991/ESJ-2025-09-01-013 Full Text: PDF

An improved beluga whale optimization using ring topology for solving multi-objective task scheduling in cloud

An improved beluga whale optimization using ring topology for solving multi-objective task scheduling in cloud

Multi-objective optimized task scheduling in cognitive internet of vehicles: towards energy-efficiency

The rise of intelligent and connected vehicles has led to new vehicular applications, but vehicle computing capabilities remain limited. Mobile edge computing (MEC) can mitigate this by offloading computation tasks to the network's edge. However, limited computational capacities in vehicles lead to increased latency and energy consumption. To address this, roadside units (RSUs) with cloud servers, known as edge computing devices (ECDs), can be expanded to provide energy-efficient scheduling for task computation. A new energy-efficient scheduling method called multi-objective optimization energy computation (MOEC) is proposed, based on multi-objective particle swarm optimization (MOPSO) to reduce ECDs' energy usage and execution time. Simulation results using MATLAB show that MOEC can balance the trade-off between energy usage and execution time, leading to more efficient offloading.

Pemanfaatan Aplikasi Trello untuk Efisiensi Agenda Manajemen Sekolah di SMK Negeri 5 Balikpapan

This research analyzes the use of the Trello application in improving school management efficiency at SMK Negeri 5 Balikpapan through a qualitative descriptive approach based on case studies. This research uses a qualitative descriptive approach based on case studies at SMK Negeri 5 Balikpapan, with data collection through observation, interviews and documentation. Research results show that Trello significantly improves schedule management, task distribution, and team collaboration through progress visualization, automatic reminders, and comments features. Time efficiency increases by up to 40%, while the on-time task completion rate reaches 85%. These findings support the theories of time management, Agile, and work motivation, and show that Trello has the potential to be an effective digital solution in educational management, especially in overcoming challenges related to organizing tasks and communication between team members. These findings support the theories of time management, Agile, and work motivation, and show the potential of Trello as a digital solution in educational management. It is estimated that the use of applications such as Trello can be further optimized by training staff and developing more specific features according to managerial needs in educational institutions.

Optimizing multiprocessor performance in real-time systems using an innovative genetic algorithm approach

Due to its enormous influence on system functionality, researchers are presently looking into the issue of task scheduling on multiprocessors. Establishing the most advantageous schedules is often regarded as a difficult-to-compute issue. Genetic Algorithm is a recent tool employed by researchers to optimize scheduling tasks and boost performance, although this field of research is yet mostly unexplored. In this article, a novel approach for generating task schedules for real-time systems utilizing a Genetic Algorithm is proposed. The approach seeks to design task schedules for multiprocessor systems with optimal or suboptimal lengths, with the ultimate goal of achieving high performance. This research project focuses on non-preemptive independent tasks in a multiprocessor environment. All processors are assumed to be identical. We conducted a thorough analysis of the proposed approach and pitted it against three frequently utilized scheduling methodologies: the “Evolutionary Fuzzy Based Scheduling Algorithm”, the “Least Laxity First Algorithm”, and the “Earliest Deadline First Algorithm”. The Proposed Algorithm demonstrated superior efficiency and reliability compared to Earliest Deadline First, Least Laxity First, and Evolutionary Fuzzy-based Scheduling Algorithm. It consistently achieved zero missed deadlines and the lowest average response and turnaround times across all scenarios, maintaining optimal performance even under high load conditions.

Independent task scheduling algorithms in fog environments from users’ and service providers’ perspectives: a systematic review

Independent task scheduling algorithms in fog environments from users’ and service providers’ perspectives: a systematic review

Leveraging Incremental Machine Learning for Reconfigurable Systems Modeling under Dynamic Workloads

Dynamic workload orchestration is one of the main concerns when working with heterogeneous computing infrastructures in the edge-cloud continuum. In this context, FPGA-based computing nodes can take advantage of their improved flexibility, performance and energy efficiency provided that they use proper resource management strategies. In this regard, many state-of-the-art systems rely on proactive power management techniques and task scheduling decisions, which in turn require deep knowledge about the applications to be accelerated and the actual response of the target reconfigurable fabrics when executing them. While acquiring this knowledge at design time was more or less feasible in the past, with applications mostly being static task graphs that did not change at run time, the highly dynamic nature of current workloads in the edge-cloud continuum, where tasks can be deployed on any node and at any time, has removed this possibility. As a result, being able to derive such information at run time to make informed decisions has become a must. This paper presents an infrastructure to build incremental ML models that can be used to obtain run-time power consumption and performance estimations in FPGA-based reconfigurable multi-accelerator systems operating under dynamic workloads. The proposed infrastructure features a novel stop-and-restart resource-aware mechanism to monitor and control the model training and evaluation stages during normal system operation, enabling low-overhead updates in the models to account for either unexpected acceleration requests (i.e., tasks not considered previously by the models) or model drift (e.g., fabric degradation). Experimental results show that the proposed approach induces a maximal additional error of 3.66% compared to a continuous training alternative. Furthermore, the proposed approach incurs only a 4.49% execution time overhead, compared to the 20.91% overhead induced by the continuous training alternative. The proposed modeling strategy enables innovative scheduling approaches in reconfigurable systems. This is exemplified by the conflict-aware scheduler introduced in this work, which achieves up to a 1.35 times speedup in executing the experimental workload. Additionally, the proposed approach demonstrates superior adaptability compared to other methods in the literature, particularly in response to significant changes in workload and to mitigate the effects of model overfitting. The portability of the proposed modeling methodology and monitoring infrastructure is also shown through their application to both Zynq-7000 and Zynq UltraScale+ devices.

A Dynamic Energy‐Efficient Scheduling Method for Periodic Workflows Based on Collaboration of Edge‐Cloud Computing Resources

ABSTRACTEdge‐cloud computing offers an efficient method to flexibly allocate various computing resources for periodic workflow applications commonly employed in industrial production, commercial operations, and scientific research. Rationalized allocation of computational resources for scheduling in the edge‐cloud environment is the key to reducing energy consumption of the periodic workflows scheduling process. To this end, this paper proposes an optimization method for dynamic energy‐efficient scheduling of periodic workflows based on the collaboration of edge‐cloud computational resources while satisfying the constraints of workflow deadlines. In our method, periodic workflow scheduling is defined to be performed on a three‐tier integrated scheduling architecture of user terminals, edge computing platform, and cloud computing platform. Task groups are generated based on workflow critical paths, and appropriate edge‐cloud computing resources are selected for workflow tasks using corresponding scheduling policies at each scheduling stage. It reduces energy consumption during task scheduling while satisfying workflow deadline constraints. Comparative experiments in a simulated edge‐cloud environment show that our method reduces energy consumption of the scheduling process by 19.38%, 22.7%, and 37.34% compared to GA, PSO, and cloud computing, respectively. That is, the method effectively reduces the scheduling energy consumption during periodic workflow processing and significantly improves computational resource utilization.

Synchronous Remote Calibration for Electricity Meters: Application and Optimization

Remote calibration is an advanced methodology that leverages electricity meters, intelligent detection, and computing technologies to enhance calibration efficiency and precision significantly. However, current research predominantly focuses on isolated calibration architectures tailored to single-laboratory environments. In contrast, distributed remote calibration systems that integrate multiple nodes remain in their early developmental stages, despite their considerable potential for improving scalability and operational efficiency. The purpose of this paper is to propose a multi-point collaborative distributed remote calibration model that improves scalability and operational efficiency for remote sensing devices. It addresses the challenge of resource allocation for synchronous calibration across distributed nodes by introducing a hybrid genetic algorithm that optimises scheduling and resource management. Experimental results reveal that the proposed algorithm surpasses other comparable methods in its category, highlighting its capability to improve resource efficiency in distributed remote calibration systems. Additionally, the hybrid genetic algorithm offers profound insights and effective solutions to the intricate challenges of task scheduling in dual-container synchronisation, enhancing both scheduling performance and system dependability.

MESSI: Task Mapping and Scheduling Strategy for FPGA-based Heterogeneous Real-Time Systems

Continuous demands for improved performance within constrained resource budgets are driving a move from homogeneous to heterogeneous processing platforms for the implementation of today’s Real-Time (RT) embedded systems. The applications executing on such systems are typically represented as a Precedence Task Graph (PTG), where a node represents a task or algorithm for one functionality and edges represent the complex interactions between multiple functionalities. Due to RT constraints, the task graph needs to be executed within a specified deadline. Although some existing studies have looked into solving this challenge, comprehensive studies that combine the theoretical features of RT task-graph mapping and scheduling with practical runtime architectural characteristics have mostly been ignored to date. Hence, in this paper, we consider the challenge of scheduling a RT application modelled as a single PTG, with the objective of minimizing the overall execution time under Hardware (HW) resource and deadline constraints for heterogeneous Central Processing Unit (CPU) + Field Programmable Gate Array (FPGA) architectures. First, we introduce an optimal solution using Integer Linear Programming (ILP). However, this ILP-based optimal solution suffers from computational complexity and does not scale well even for moderately large problem sizes. Hence, we additionally propose heuristic algorithms for task mapping and scheduling. The efficiency of the proposed scheme, named MESSI, has been evaluated through experiments using PTGs on a practical CPU+FPGA system regarding current technology restrictions. Our experiments demonstrate that performance gains of 55.6 % and area usage reductions of 46.3 % are possible compared to full Software SW and HW execution, respectively.

Edge Computing- Smart Cities: Optimizing Data Processing & Resource Management in Urban Environments

As urbanization accelerates, smart cities are emerging as innovative ecosystems that integrate technology to address challenges related to sustainability, mobility, and infrastructure. Among these technologies, edge computing has gained prominence as a transformative solution to optimize data processing and resource management in urban environments. This paper explores the role of edge computing in enabling efficient, real-time decision-making by bringing computational power closer to data sources. Unlike traditional cloud-centric models, edge computing reduces latency, enhances data security, and improves bandwidth utilization by distributing data processing across decentralized nodes. The integration of edge computing in smart cities supports various applications, including intelligent transportation systems, energy-efficient smart grids, and real-time public safety monitoring. By processing data locally, edge devices can handle massive volumes of information generated by Internet of Things (IoT) devices, ensuring seamless service delivery without overwhelming centralized systems. Furthermore, this decentralized approach enhances resilience by reducing dependency on remote servers, a crucial factor for mission-critical urban applications. A significant focus of this paper is on resource management, particularly the allocation of computational resources across edge nodes. Strategies such as dynamic resource scheduling, load balancing, and adaptive task offloading are analyzed for their effectiveness in maintaining operational efficiency. Moreover, the research highlights the importance of leveraging machine learning and artificial intelligence algorithms within edge computing frameworks to predict traffic patterns, optimize energy consumption, and enhance waste management systems. Security and privacy concerns, often considered barriers to edge computing adoption, are addressed through advanced encryption techniques and secure communication protocols. This paper also evaluates challenges associated with edge computing deployment, such as hardware limitations, interoperability issues, and the need for robust regulatory frameworks. Case studies from leading smart city projects illustrate successful implementations and offer insights into overcoming these obstacles. In addition to technical aspects, this research underscores the socioeconomic benefits of edge computing in urban settings. Improved public services, reduced environmental impact, and cost-effective infrastructure management demonstrate the potential of edge computing to revolutionize city living. By enabling real-time analytics and localized decision-making, edge computing supports a more responsive and adaptive urban ecosystem. The findings presented in this paper emphasize the critical role of edge computing in bridging the gap between urban challenges and technological solutions. As cities continue to evolve, adopting edge computing technologies will not only enhance operational efficiency but also foster innovation, sustainability, and inclusivity. Future research directions include exploring hybrid models combining edge and cloud computing, advancing hardware capabilities, and developing standardized frameworks to accelerate adoption. This paper contributes to the growing body of knowledge on edge computing, offering a comprehensive analysis of its applications, challenges, and potential in shaping the future of smart cities. By optimizing data processing and resource management, edge computing emerges as a cornerstone technology for creating smarter, more resilient urban environments.

Tren Algoritma Penjadwalan Tugas Pada Cloud Computing: Systematic Review Literature

Task scheduling in cloud computing environments is a crucial aspect in optimizing resource allocation and improving system efficiency. This research aims to analyze trends in task scheduling algorithms in cloud computing using a Systematic Literature Review (SLR) approach on various scientific publications published between 2018 and 2025. The results of the study show that Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA) algorithms are the most commonly used methods in solving task scheduling problems. PSO stands out as an effective algorithm due to its ability to find global optimal solutions, handle non-linear and multimodal problems, and its efficiency in managing computational resources. Additionally, various studies have shown that optimization of scheduling algorithms can be achieved through a combination or modification of existing methods to improve system performance. This study provides in-depth insights into the development of scheduling algorithms in cloud computing and opens up opportunities for further research in developing more innovative and adaptive approaches.

Enhancing task scheduling and QoS optimization in mobile edge computing via microservice-oriented container selection

Enhancing task scheduling and QoS optimization in mobile edge computing via microservice-oriented container selection

ESDN: edge computing task scheduling strategy based on dilated convolutional neural network and quasi-newton algorithm

ESDN: edge computing task scheduling strategy based on dilated convolutional neural network and quasi-newton algorithm

Reinforcement learning-based task scheduling for heterogeneous computing in end-edge-cloud environment

Reinforcement learning-based task scheduling for heterogeneous computing in end-edge-cloud environment