Multi-UAV cooperative task scheduling and trajectory optimization system under communication constraints

At present, there is no unified network resource scheduling and business optimization system in cloud-edge collaboration services with supercomputing as the core, especially network transmission and data management. In this poster, we focus on how to optimize the quality of user experience while meeting huge computing power requirements, and design a novel network resource scheduling and traffic optimization platform centered on supercomputing system, which is called PANGU. PANGU is a multi-dimensional network resource scheduling solution for supercomputing that can guarantee collaborative network services. PANGU can make up for the lack of computing power of edge computing while solving the adaptability of supercomputing nodes to pervasive cloud edge computing. Through effective network resource management, PANGU can provide higher productivity.

We propose a cloud service scheduling model that is referred to as the Task Scheduling System (TSS). In the user module, the process time of each task is in accordance with a general distribution. In the task scheduling module, we take a weighted sum of makespan and flowtime as the objective function and use an Ant Colony Optimization (ACO) and a Genetic Algorithm (GA) to solve the problem of cloud task scheduling. Simulation results show that the convergence speed and output performance of our Genetic Algorithm-Chaos Ant Colony Optimization (GA-CACO) are optimal.

Resource Leveling Based on Line of Balance and Constraint Programming

In the intelligent transformation of the global manufacturing industry, petroleum equipment processing is facing problems such as improving efficiency and ensuring quality, and the intelligent development of Manufacturing Execution System is crucial. Based on this, starting from the optimal scheduling algorithm and system architecture design, an intelligent manufacturing system scheme integrating artificial bee colony algorithm was proposed. In this scheme, a mathematical model is constructed, and the artificial bee colony algorithm is used to optimize the scheduling. Design a layered architecture to improve system performance; It has functions such as record rollback, intelligent splitting and dispatching quality inspection, and assembly line planning tree. Practice shows that this solution effectively improves the production efficiency and product quality of petroleum equipment processing, and helps the digital transformation of the industry.

In parallel and distributed applications, it is very likely that object‐oriented languages, such as Java and Ruby, and large‐scale semistructured data written in XML will be employed. However, because of their inherent dynamic memory management, parallel and distributed applications must sometimes suspend the execution of all tasks running on the processors. This adversely affects their execution on the parallel and distributed platform. In this paper, we propose a new task scheduling method called CP/MM (Critical Path/Memory Management) which can efficiently schedule tasks for applications requiring memory management. The underlying concept is to consider the cost due to memory management when the task scheduling system allocates ready (executable) coarse‐grain tasks, or macro‐tasks, to processors. We have developed three task scheduling modules, including CP/MM, for a task scheduling system which is implemented on a Java RMI (Remote Method Invocation) communication infrastructure. Our experimental results show that CP/MM can successfully prevent high‐priority macro‐tasks from being affected by the garbage collection arising from memory management, so that CP/MM can efficiently schedule distributed programs whose critical paths are relatively long. Copyright © 2005 John Wiley & Sons, Ltd.

Smart grid is an inevitable choice for the future development of the grid. Smart scheduling is one of the cores to build the strong and smart grid in China. Optimization and Management System for Grid Maintenance Scheduling is an in-depth exploration to achieve dispatching digitalization, intellectualization and visualization. This paper gives a rather detailed description about the functionality and implementation of Optimization and Management System for Grid Maintenance Scheduling. With received maintenance schedules as the basic data, OCCI-based data processing techniques and PSO-based optimization methodology are adopted to achieve the standardization and intellectualization of the grid maintenance scheduling. Moreover, the maintenance schedules generated and related information are managed in a standardized way. The design from this paper together with its programs has been applied in practice, achieving satisfying effect.

Optimal Scheduling System in Small Enterprise by Use of Mini-computer

Task scheduling and workload management on high-performance computing environments is usually done with tools such as SLURM MyQueue is a front-end for schedulers that makes handling of tasks easy. It has a command-line interface called mq with a number of sub-commands and a Python interface for managing workflows. Currently, the following schedulers are supported: SLURM, PBS, and LSF.

In reality, peripheral devices often make a significant contribution to the power consumption of the entire system. An effective energy-efficient scheduling algorithm should consider not only the energy consumption of the processor but also the usages of devices. In this paper, we explore energy-efficient scheduling of periodic real-time tasks in a system with a dynamic-voltage-scaling (DVS) processor and multiple non-DVS system devices. We consider systems that any device used by a task remains operating while the task is active. We propose scheduling algorithms in the management of task preemption to reduce the energy consumption of devices. Simulation results show that our proposed algorithms could not only reduce the number of task preemption significantly but also minimize the energy consumption, compared to earliest-deadline-first scheduling.

This research was driven by the need for a more efficient production scheduling system in a consumable liquid product division of a large consumer products company. The manufacturing process under inspection consists of continuous and discrete elements, on both production and packaging lines. The production lines are split into continuous production lines and batch production lines which produce the product in fixed batch amounts. Then there are several bottling lines, some of which package a particular bottle size and others that can package multiple bottle sizes. The main objective of this research was to reduce the amount of time it takes for the client to create production and bottling schedules. An optimization model was developed to automate this process and provide the client with the best possible schedule. The objective of the model is to minimize cost by minimizing the number of switches across the production and bottling lines, as well as minimizing the amount of overproduction. Inputs into the model include model parameters, like the number of shifts to schedule, and monthly demand numbers for each stock keeping unit (SKU). The variables being solved for are the amount of each flavor to be produced across the production lines during each shift, and the number of bottles of each SKU to be bottled across the bottling lines during each shift. Due to the unique constraints and resources of the client, a custom formulation using mixed integer programming was necessary to achieve these objectives. Overall, our model fell short in some areas but succeeded in others. Our analysis showed that the model had a 13% average decrease in production switches but an 87% average increase in bottling switches compared to the current manual scheduling system. However, the ability of our system to create a good enough initial schedule reduces the time it takes expert human schedulers to develop a final schedule by up to 85%. Runtime and computational constraints barred us from creating an optimal, cost-minimized solution for our client, and future work can be directed toward solving these issues.

While cloud-edge infrastructures demand flexible and sophisticated resource management, 6G networks necessitate very low latency, great dependability, and broad connection. Cloud computing’s scalability and agility enable it to prioritize service delivery at various levels of detail while serving billions of users. However, due to resource inefficiencies, virtual machine (VM) issues, response delays, and deadline violations, real-time task scheduling is challenging in these settings. This study develops an AI-powered task scheduling system based on the newly published Unfair Semi-Greedy (USG) algorithm, Earliest Deadline First (EDF), and Enhanced Deadline Zero-Laxity (EDZL) algorithm. The system chooses the best scheduler based on load and work criticality by combining reinforcement learning adaptive logic with a dynamic resource table. Over 10,000 soft real-time task sets were utilized to evaluate the framework across various cloud-edge scenarios. When compared to solo EDF and EDZL solutions, the recommended hybrid method reduced average response times by up to 26.3% and deadline exceptions by 41.7%. The USG component achieved 98.6% task stimulability under saturated edge settings, indicating significant changes in workload. These findings suggest that the method might be useful for applications that need a speedy turnaround. This architecture is especially well-suited for autonomous systems, remote healthcare, and immersive media, all of which require low latency and dependability, and it may be extended to AI-native 6G networks.

An approach for mapping applications represented as Directed Acyclic Graphs (DAGs) on platforms consisting of heterogeneous cores considering the communication overhead between the cores is introduced. The approach is based on the Benders decomposition principle and integrates Integer Linear and Constraint Programming formulations. Both formulations take into account the communication delay between dependent tasks that are assigned to different cores trying to optimize the application's execution time. The proposed approach succeeds to provide the optimal solution in all cases of synthetic and real-application DAGs, while the pure ILP model fails more than half of them. Also, the average solution time of the proposed method is about 1 minute, whereas for instances solved by both models, the speedup equals to 11× over the ILP model.

Monitoring in large scale environments is a typical mission in cooperative robotics. This task requires the exploration of a huge domain by a generally small number of sensor equipped mobile robots. As time restrictions prohibit an exhaustive global search, a sampling strategy is required that allows an efficient spatial mapping of the environment. This paper proposes an adaptive sampling strategy for efficient simultaneous tracking of multiple concentration levels of an atmospheric plume by a team of cooperating unmanned aerial vehicles (UAVs). The approach combines uncertainty and correlation-based concentration estimates to generate sampling points based on already gathered data. The adaptive generation of sampling locations is coupled to a distributed modelpredictive controller for planning optimal vehicle trajectories under collision and communication constraints. Simulation results demonstrate that connectivity of all involved vehicles can be maintained and an accurate reconstruction of the plume is obtained efficiently.

The demand for high-quality education delivery in increasingly dynamic and competitive educational markets has intensified the need for intelligent and adaptive scheduling systems. Manual classroom scheduling methods, which rely on human decision-making, often fail to optimize critical resources such as classrooms, teachers, and student time, leading to inefficiencies and economic losses. This paper proposes a comprehensive Smart Classroom Scheduling and Optimization System (LMSOPT) that leverages Artificial Intelligence (AI), advanced time-series forecasting, constraint-based multi-objective optimization, and real-time data integration. The proposed system employs Long Short-Term Memory (LSTM) neural networks for highly accurate demand forecasting, alongside heuristic and metaheuristic optimization algorithms such as Constraint Programming (CP), Genetic Algorithms (GA), and Tabu Search. The system aims to dynamically balance multiple conflicting objectives: maximizing classroom occupancy rates, minimizing student waiting times, and aligning teacher availability with student preferences. The expected contributions are multifold: significant operational cost savings, measurable improvements in resource utilization, increased student satisfaction, and the creation of an extensible research framework for AI applications in education management. The study aligns with national strategies for digital transformation and supports the vision of data-driven decision-making in educational administration. Empirical results and comparative analyses are presented to validate the system’s effectiveness and demonstrate its replicability for institutions of various scales.

Wireless rechargeable sensor networks with a charging unmanned aerial vehicle (CUAV) have broad application prospects in the power supply of the rechargeable sensor nodes (SNs). However, how to schedule a CUAV and design the trajectory to improve the charging efficiency of the entire system is still a vital problem. In this article, we formulate a joint-CUAV scheduling and trajectory optimization problem (JSTOP) to simultaneously minimize the hovering points of CUAV, the number of the repeatedly covered SNs, and the flying distance of CUAV for charging all SNs. Due to the complexity of JSTOP, it is decomposed into two optimization subproblems that are CUAV scheduling optimization problem (CSOP) and CUAV trajectory optimization problem (CTOP). CSOP is a hybrid optimization problem that consists of the continuous and discrete solution space, and the solution dimension in CSOP is not fixed since it should be changed with the number of hovering points of CUAV. Moreover, CTOP is a completely discrete optimization problem. Thus, we propose a particle swarm optimization (PSO) with a flexible dimension mechanism, a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means operator, and a punishment-compensation mechanism (PSOFKP) and a PSO with a discretization factor, a 2-opt operator, and a path crossover reduction mechanism (PSOD2P) to solve the converted CSOP and CTOP, respectively. Simulation results evaluate the benefits of PSOFKP and PSOD2P under different scales and settings of the network, and the stability of the proposed algorithms is verified.