Optimal Task Scheduling Research Articles

Intelligent Joint Optimization of Deployment and Task Scheduling for Mobile Users in Multi‐UAV‐Assisted MEC System

Mobile edge computing (MEC) servers integrated with multi‐unmanned aerial vehicles (multi‐UAVs) present a new system the multi‐UAV‐assisted MEC system. This system relies on the mobility of the UAVs to reduce the transmission distance between the servers and mobile users, thereby enhancing service quality and minimizing the overall energy consumption. Achieving optimal UAV deployment and precise task scheduling is crucial for improved coverage and service quality in this system. This problem is framed as a nonconvex optimization problem known as joint task scheduling and deployment optimization. Recently, an optimization technique based on a dual‐layer framework: Upper layer optimization and lower layer optimization have been proposed to tackle this problem and achieved superior performance compared to the alternative methods. In this framework, the lower layer was responsible for task scheduling optimization, while the upper layer was designed to assist in optimizing UAV deployment and thus achieving improved coverage and enhanced task scheduling for mobile users, thereby minimizing the total energy consumption. However, further refinement of upper layer optimization is needed to improve the deployment process. In this study, the upper layer undergoes enhancement through key modifications: First, random selection of the solutions is replaced with sequential selection to maintain the unique characteristics of each individual throughout the optimization process, fostering both exploration and exploitation. Second, a selection of recently reported metaheuristic algorithms, such as spider wasp optimizer (SWO), generalized normal distribution optimization (GNDO), and gradient‐based optimizer (GBO), are adapted to optimize UAV deployments. Both improved upper layer and lower layer optimization led to the development of novel, more effective optimization approaches, including IToGBOTaS, IToGNDOTaS, and IToSWOTaS. These techniques are evaluated using nine instances with a variety of mobile tasks ranging from 100 to 900 to test their stability and then compared to different optimization techniques to measure their effectiveness. This comparison is based on several statistical information to determine the superiority and difference between their outcomes. The results reveal that IToGBOTaS and IToSWOTaS exhibit slightly superior performance compared to all other algorithms, showcasing their competitiveness and efficacy in addressing the optimization challenges of the multi‐UAV‐assisted MEC system.

Particle swarm optimization-based load balance-aware energy-efficient scheduling for mobile crowd computing

Due to their increasing computational power and energy-efficient hardware, today’s smart mobile devices (SMDs) are replacing desktops and laptops as casual computing devices. Moreover, a cluster of such powerful SMDs can garner substantial high-performance computing (HPC). Such an HPC is achieved by utilizing publicly owned SMDs in mobile crowd computing (MCC). Here, a large computing-intensive task is divided and scheduled for the available SMDs for execution, and the results are recollected. This approach provides an economical and sustainable HPC. However, battery-powered constrained energy is a great hindrance to achieving this goal. Therefore, in the MCC, it is crucial to minimize the overall energy consumption to complete the task. This can be achieved to some extent by optimizing task scheduling to the appropriate SMDs. However, considering only energy efficiency might lead to an enormous load imbalance among SMDs, i.e., the most energy-efficient SMDs would be overloaded most of the time. Considering this, in this paper, we present a modified particle swarm optimization (PSO)-based scheduling algorithm to minimize the overall energy consumption among a set of SMDs designated to execute a set of MCC tasks while maintaining a satisfactory load balance level. Extensive simulations with both synthetic and real data sets are carried out to analyze and validate the proposed method. The work was compared with popular heuristic (minimum completion time (MCT), MinMin, MaxMin, and preconditioned progressive iterative approximation (PPIA)) and metaheuristic (genetic algorithm (GA)) optimization algorithms, which yields significant improvements over others in terms of the considered objectives. In addition, an analysis of variance (ANOVA) test is conducted to provide further evidence regarding the distinctiveness of the proposed algorithm.

Two-Timescale Joint Optimization of Task Scheduling and Resource Scaling in Multi-Data Center System Based on Multi-Agent Deep Reinforcement Learning

Two-Timescale Joint Optimization of Task Scheduling and Resource Scaling in Multi-Data Center System Based on Multi-Agent Deep Reinforcement Learning

Dual-Timescales Optimization of Task Scheduling and Resource Slicing in Satellite-Terrestrial Edge Computing Networks

Dual-Timescales Optimization of Task Scheduling and Resource Slicing in Satellite-Terrestrial Edge Computing Networks

Balancing heterogeneous assembly line with multi-skilled human-robot collaboration via Adaptive cooperative co-evolutionary algorithm

In human-centred manufacturing, deploying collaborative robots (cobots) is recognized as a promising strategy to enhance the inclusiveness and resilience of production systems. Despite notable progress, current production scheduling methods for human-robot collaboration (HRC) still fail to adequately accommodate workforce heterogeneity, significantly reducing their adoption and implementation. To address this gap, we introduce a novel model for the Assembly Line Worker Integration and Balancing Problem considering Multi-skilled Human-Robot Collaboration (ALWIBP-mHRC). This model aims to optimize task scheduling between semi-skilled workers and cobots, aiming to maximize productivity and minimize costs. It features a multi-skilled human-robot collaboration (mHRC) task assignment scheme that selects the optimal assembly/collaboration mode from seven scenarios, based on specific task requirements and resource-skill availability, thus maximizing resource-skill complementarity. To tackle the complexities of this problem, we propose an adaptive multi-objective cooperative co-evolutionary algorithm (a-MOCC) that incorporates a sub-problem decomposition and decoding framework tailored for ALWIBP-mHRC, enhanced by an adaptive evolutionary strategy based on Q-learning (Q-Coevolution). Experimental tests demonstrate the superior performance of the proposed method compared to other established metaheuristic algorithms across various instance sizes, underscoring its effectiveness in enhancing the productivity of production systems for semi-skilled workers. The findings are significant for investment decision-making and resource planning, as they highlight the strategic value of integrating cobots in large-scale heterogeneous workforce production. This work underscores the potential of cobots to mitigate skill gaps in assembly systems, laying the groundwork for future research and industrial strategies focused on enhancing productivity, inclusivity, and adaptability in a dynamically changing labour market.

An efficient deep reinforcement learning based task scheduler in cloud-fog environment

Efficient task scheduling in cloud and fog computing environments remains a significant challenge due to the diverse nature and critical processing requirements of tasks originating from heterogeneous devices. Traditional scheduling methods often struggle with high latency and inadequate processing times, especially in applications demanding strict computational efficiency. To address these challenges, this paper proposes an advanced fog-cloud integration approach utilizing a deep reinforcement learning-based task scheduler, DRLMOTS (Deep Reinforcement Learning based Multi Objective Task Scheduler in Cloud Fog Environment). This novel scheduler intelligently evaluates task characteristics, such as length and processing capacity, to dynamically allocate computation to either fog nodes or cloud resources. The methodology leverages a Deep Q-Learning Network model and includes extensive simulations using both randomized workloads and real-world Google Jobs Workloads. Comparative analysis demonstrates that DRLMOTS significantly outperforms existing baseline algorithms such as CNN, LSTM, and GGCN, achieving a substantial reduction in makespan by up to 26.80%, 18.84, and 13.83% and decreasing energy consumption by up to 39.60%, 30.29%, and 27.11%. Additionally, the proposed scheduler enhances fault tolerance, showcasing improvements of up to 221.89%, 17.05%, and 11.05% over conventional methods. These results validate the efficiency and robustness of DRLMOTS in optimizing task scheduling in fog-cloud environments.

Power Optimized Task Scheduling using Genetic Algorithm (POTS-GA) in Cloud Environment

In a cloud environment, the allocation of tasks has become pivotal on account of rapid growth of user requests. The processing of user requests leads to a significant execution time, and a huge amount of power is also consumed. Consequently, task scheduling for optimizing makes pan and power usage has become critical, particularly in a heterogeneous environment. This research work proposes Power-Optimized Task Scheduling using Genetic Algorithm (POTS-GA) that aims to minimize execution time and power consumption. The proposed strategy employs genetic algorithm to take scheduling decision while taking into consideration the execution time and overall power consumption of resources. The fitness computation considering both objectives and the customized genetic operators ensure to search for a better scheduling solution. The experiments performed on a large number of tasks and virtual machines show that the proposed POTS-GA approach outperforms other task scheduling strategies including Efficient Task Allocation using Genetic Algorithm (ETA-GA), Round Robin algorithm (RRA), and First Come First Serve (FCFS) and Greedy algorithm in terms of makes pan and power consumption.

Designing an optimal task scheduling and VM placement in the cloud environment with multi-objective constraints using Hybrid Lemurs and Gannet Optimization Algorithm

ABSTRACT An efficient resource utilization method can greatly reduce expenses and unwanted resources. Typical cloud resource planning approaches lack support for the emerging paradigm regarding asset management speed and optimization. The use of cloud computing relies heavily on task planning and allocation of resources. The task scheduling issue is more crucial in arranging and allotting application jobs supplied by customers on Virtual Machines (VM) in a specific manner. The task planning issue needs to be specifically stated to increase scheduling efficiency. The task scheduling in the cloud environment model is developed using optimization techniques. This model intends to optimize both the task scheduling and VM placement over the cloud environment. In this model, a new hybrid-meta-heuristic optimization algorithm is developed named the Hybrid Lemurs-based Gannet Optimization Algorithm (HL-GOA). The multi-objective function is considered with constraints like cost, time, resource utilization, makespan, and throughput. The proposed model is further validated and compared against existing methodologies. The total time required for scheduling and VM placement is 30.23%, 6.25%, 11.76%, and 10.44% reduced than ESO, RSO, LO, and GOA with 2 VMs. The simulation outcomes revealed that the developed model effectively resolved the scheduling and VL placement issues.

Enhancing Cloud Task Scheduling with Multi-Objective Optimization Using K-Means Clustering and Dynamic Resource Allocation

The scheduling and resource allocation procedure is an essential component of cloud resource management. Effective resource allocation is severely hampered by the task arrival rates' erratic and unclear behavior. To prevent under or overusing resources, an effective scheduling strategy is necessary. To improve scheduling and allocation performance, a multi-objective optimization technique is presented for the best resource allocation and task scheduling inside scientific workflow datasets in a heterogeneous environment. In the first stage, the system calculates four key metrics: Communication Cost, Computation Cost, Earliest Finished Time on a particular VM, and Total Task Length for a specific scientific workflow dataset. These metrics provide a comprehensive understanding of the resource requirements and help make informed scheduling decisions. In the second stage, tasks are clustered using the K-Means clustering algorithm. This clustering groups similar tasks together, making managing and scheduling them easier. In the third stage, the proposed resource allocation algorithm allocates the clustered tasks to the appropriate VMs. This step ensures that the tasks are assigned to the best-suited resources, optimizing the overall system performance and resource utilization. By following this multi-stage process, the system aims to achieve optimal resource allocation and task scheduling, thereby improving the efficiency and effectiveness of cloud resource management. The proposed method significantly outperforms PSO, CSO, and GWO by consistently achieving lower Makespan—under 400 units at 50 tasks—while maintaining high resource utilization rates above 0.75, demonstrating superior efficiency in task execution and resource management.

Efficient microservices offloading for cost optimization in diverse MEC cloud networks

In recent years, mobile applications have proliferated across domains such as E-banking, Augmented Reality, E-Transportation, and E-Healthcare. These applications are often built using microservices, an architectural style where the application is composed of independently deployable services focusing on specific functionalities. Mobile devices cannot process these microservices locally, so traditionally, cloud-based frameworks using cost-efficient Virtual Machines (VMs) and edge servers have been used to offload these tasks. However, cloud frameworks suffer from extended boot times and high transmission overhead, while edge servers have limited computational resources. To overcome these challenges, this study introduces a Microservices Container-Based Mobile Edge Cloud Computing (MCBMEC) environment and proposes an innovative framework, Optimization Task Scheduling and Computational Offloading with Cost Awareness (OTSCOCA). This framework addresses Resource Matching, Task Sequencing, and Task Scheduling to enhance server utilization, reduce service latency, and improve service bootup times. Empirical results validate the efficacy of MCBMEC and OTSCOCA, demonstrating significant improvements in server efficiency, reduced service latency, faster service bootup times, and notable cost savings. These outcomes underscore the pivotal role of these methodologies in advancing mobile edge computing applications amidst the challenges of edge server limitations and traditional cloud-based approaches.

Optimal robot task scheduling in cluttered environments considering mechanical advantage

AbstractIn various industrial robotic applications, the effective traversal of a manipulator amidst obstacles and its ability to reach specific task-points are imperative for the execution of predefined tasks. In certain scenarios, the sequence in which the manipulator reaches these task-points significantly impacts the overall cycle time required for task completion. Moreover, some tasks necessitate significant force exertion at the end-effector. Therefore, establishing an optimal sequence for the task-points reached by the end-effector’s tip is crucial for enhancing robot performance, ensuring collision-free motion and maintaining high-force application at the end-effector’s tip.To maximize the manipulator’s manipulability, which serves as a performance index for assessing its force capability, we aim to establish an optimal collision-free task sequence considering higher mechanical advantage. Three optimization criteria are considered: the cycle time, collision avoidance and the manipulability index. Optimization is accomplished using a genetic algorithm coupled with the Bump-Surface concept for collision avoidance. The effectiveness of this approach is confirmed through simulation experiments conducted in 2D and 3D environments with obstacles employing both redundant and non-redundant robots.

Optimized task scheduling approach with fault tolerant load balancing using multi-objective cat swarm optimization for multi-cloud environment

Multi-cloud environment enables an organization to access services from more than one cloud service providers the use of multiple cloud computing and it can be treated as single heterogeneous environment. It enables autonomy to run the tasks on private or public cloud based on business or technical requirements. In a multi-cloud platform, load balancing is an essential task to serve the requests from multiple users with different resources effectively. It helps to improve utilization of the cloud resources, throughput, reduce makespan and avoid overload at resources. Load balancing also facilitates the redirection of traffic to resources running in another cloud when a failure occurs in a cloud. Hence, it is more vital to have optimized load balancing methods in multi-cloud infrastructure in order to improve the system performance. This paper presents an optimized fault tolerant load balancing method using multi-objective cat swarm optimization algorithm called MCSOFLB and the results are then compared against other powerful optimization algorithms. The experimental results evidently show that the proposed algorithm ranks first on the whole. The MCSOFLB method produces an average improvement of 31 % makespan, 6 % resource utilization, 12 % cost, 6 % success rate and 32 % average throughput over other benchmark algorithms.

A Modified Levy Flight Firefly-Based Approach to Optimize Turnaround Time in Fog Computing Environments

The escalation of Internet of Things (IoT) devices has led to increased data generation at the network edge that has burdened the cloud infrastructure in terms of handling and processing of data. This has led to the rapid adoption of fog computing because of its ability to bring computation and storage closer to the edge and support for real-time applications and services by reducing latency. One of the foremost challenges in the fog computing arena is minimizing turnaround time. This research paper proposes a Modified Levy Flight Firefly Algorithm (MLFFA) to optimize task scheduling for fog computing environments. Specifically, the objective is to minimize the turnaround time of tasks. Moreover, genetic operators like crossover and mutation are also employed to achieve an optimal balance between exploration and exploitation. Experimental observations undertaken show that the proposed method improves the average turnaround time by 55%, 22%, and 13%, average waiting time by 59%, 45%, and 37%, average energy consumption by 19%, 7%, and 4%, and average failure rate by 50%, 28%, and 7% compared to the existing studies, namely Load Balancing and Optimization Strategy (LBOS), Technique for Resource Allocation and Management (TRAM), and Fuzzy Golden Eagle Load Balancing (FGELB), respectively.

Multi-area collision-free path planning and efficient task scheduling optimization for autonomous agricultural robots

Collision-free path planning and task scheduling optimization in multi-region operations of autonomous agricultural robots present a complex coupled problem. In addition to considering task access sequences and collision-free path planning, multiple factors such as task priorities, terrain complexity of farmland, and robot energy consumption must be comprehensively addressed. This study aims to explore a hierarchical decoupling approach to tackle the challenges of multi-region path planning. Firstly, we conduct path planning based on the A* algorithm to traverse paths for all tasks and obtain multi-region connected paths. Throughout this process, factors such as path length, turning points, and corner angles are thoroughly considered, and a cost matrix is constructed for subsequent optimization processes. Secondly, we reformulate the multi-region path planning problem into a discrete optimization problem and employ genetic algorithms to optimize the task sequence, thus identifying the optimal task execution order under energy constraints. We finally validate the feasibility of the multi-task planning algorithm proposed by conducting experiments in an open environment, a narrow environment and a large-scale environment. Experimental results demonstrate the method's capability to find feasible collision-free and cost-optimal task access paths in diverse and complex multi-region planning scenarios.

Optimal Task Scheduling and Resource Allocation for Self-Powered Sensors in Internet of Things: An Energy Efficient Approach

Optimal Task Scheduling and Resource Allocation for Self-Powered Sensors in Internet of Things: An Energy Efficient Approach

Optimizing Task Scheduling: Exploring Advanced Machine Learning in Dew-Powered Cloud Environments

Optimizing Task Scheduling: Exploring Advanced Machine Learning in Dew-Powered Cloud Environments

A deep reinforcement learning-based scheduling framework for real-time workflows in the cloud environment

Benefiting from the flexibility, scalability and many other advantages of cloud computing, an increasing number of research institutions and enterprises are using cloud platforms to deploy their scientific or commercial workflow applications. From the perspective of cloud service providers, how to schedule workflows reasonably to improve user satisfaction and resource utilization is a big challenge. This challenge becomes more prominent in a dynamic cloud environment, where the workload of user-submitted workflows is constantly changing and unpredictable. Traditional scheduling methods designed for fixed workflows or real-time independent tasks are not applicable in such scenarios. In this paper, we present an online scheduling framework for multiple real-time workflows in a dynamic environment. In this framework, the scheduler employs a deep reinforcement learning based algorithm (R-DQN) to assign each task in workflows to a suitable virtual machine instance according to the current situation. Its goal is to achieve optimal task scheduling and resource allocation, so as to minimize workflow makespan and maximize resource utilization. We provide a comprehensive overview of the design and implementation of our approach, and we conduct experiments using various workflow benchmarks with different structures and scales on the WorkflowSim platform. The experimental results demonstrate that our algorithm significantly outperforms the compared algorithms in term of makespan and other metrics. Moreover, our algorithm demonstrates strong performance in generality, robustness and scalability.

Optimization of Task Scheduling in Cloud Computing Using the Sine Cosine Algorithm

Optimization of Task Scheduling in Cloud Computing Using the Sine Cosine Algorithm

Workload prioritization and optimal task scheduling in cloud: introduction to hybrid optimization algorithm

Workload prioritization and optimal task scheduling in cloud: introduction to hybrid optimization algorithm

An Implementation on Energy Efficient Task Scheduling in Cloud Environment

Abstract: Cloud computing has revolutionized the way computing resources are utilized by providing scalable, on-demand access to a shared pool of resources over the internet. It offers significant advantages such as cost savings, flexibility, and accessibility, making it an essential technology for businesses and individuals alike. However, the increasing energy consumption associated with cloud data centres has become a critical concern, necessitating the development of energy-efficient task scheduling algorithms. This paper explores various algorithms designed to optimize task scheduling in cloud environments to reduce energy consumption. Among these, heuristic algorithms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are popular for their ability to find near-optimal solutions in large search spaces. Additionally, machine learning-based approaches, such as Reinforcement Learning (RL), have shown promise in dynamically adapting to workload variations and improving energy efficiency. Other notable algorithms include Ant Colony Optimization (ACO) and Dynamic Voltage and Frequency Scaling (DVFS), each offering unique mechanisms to balance performance and energy usage. The focus of this paper is on the implementation and comparative analysis of these task scheduling algorithms in a cloud environment. We present a comprehensive evaluation of their performance, highlighting their strengths and limitations in achieving energy efficiency. The results demonstrate that while no single algorithm is universally optimal, tailored combinations of these approaches can significantly enhance energy savings in cloud data centres.