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Resource scheduling in cloud is a challenging job and the scheduling of appropriate resources to cloud workloads depends on the QoS requirements of cloud applications. In cloud environment, heterogeneity, uncertainty and dispersion of resources encounters problems of allocation of resources, which cannot be addressed with existing resource allocation policies. Researchers still face troubles to select the efficient and appropriate resource scheduling algorithm for a specific workload from the existing literature of resource scheduling algorithms. This research depicts a broad methodical literature analysis of resource management in the area of cloud in general and cloud resource scheduling in specific. In this survey, standard methodical literature analysis technique is used based on a complete collection of 110 research papers out of large collection of 1206 research papers published in 19 foremost workshops, symposiums and conferences and 11 prominent journals. The current status of resource scheduling in cloud computing is distributed into various categories. Methodical analysis of resource scheduling in cloud computing is presented, resource scheduling algorithms and management, its types and benefits with tools, resource scheduling aspects and resource distribution policies are described. The literature concerning to thirteen types of resource scheduling algorithms has also been stated. Further, eight types of resource distribution policies are described. Methodical analysis of this research work will help researchers to find the important characteristics of resource scheduling algorithms and also will help to select most suitable algorithm for scheduling a specific workload. Future research directions have also been suggested in this research work.

In the context of Industry 4.0 and intelligent manufacturing, AGVs are widely used in flexible job shop resource transportation, which sharply increases the uncertainty and complexity of the scheduling process. For this reason, an improved double Deep Q Network (DDQN) real-time scheduling method is proposed for the Flexible Job Shop Scheduling Problem with Automated Guided Vehicle (FJSP-AGV) to minimize the makespan. Firstly, the optimization model of the FJSP-AGV is established, and the corresponding constraints and the objective function are defined. Then, the FJSP-AGV is converted into a Markov Decision Process (MDP), in which the state space, action space, and reward function are defined in detail. Next, an improved DDQN is proposed to generate the optimal scheduling policy considering AGV. Finally, the computational experiments are conducted based on data from public benchmarks and the real-world flexible job shop, and the results demonstrate the accuracy and effectiveness of the proposed algorithm.

In recent years, serverless computing has received significant attention due to its innovative approach to cloud computing. In this novel approach, a new payment model is presented, and a microservice architecture is implemented to convert applications into functions. These characteristics make it an appropriate choice for topics related to the Internet of Things (IoT) devices at the network’s edge because they constantly suffer from a lack of resources, and the topic of optimal use of resources is significant for them. Scheduling algorithms are used in serverless computing to allocate resources, which is a mechanism for optimizing resource utilization. This process can be challenging due to a number of factors, including dynamic behavior, heterogeneous resources, workloads that vary in volume, and variations in number of requests. Therefore, these factors have caused the presentation of algorithms with different scheduling approaches in the literature. Despite many related serverless computing studies in the literature, to the best of the author’s knowledge, no systematic, comprehensive, and detailed survey has been published that focuses on scheduling algorithms in serverless computing. In this paper, we propose a survey on scheduling approaches in serverless computing across different computing environments, including cloud computing, edge computing, and fog computing, that are presented in a classical taxonomy. The proposed taxonomy is classified into six main approaches: Energy-aware, Data-aware, Deadline-aware, Package-aware, Resource-aware, and Hybrid. After that, open issues and inadequately investigated or new research challenges are discussed, and the survey is concluded.

Since the base station-centric wireless coverage mode of 5G is difficult to support future stereoscopic global wireless coverage demands, the future infrastructure of 6G satellite-terrestrial integrated network (STIN) with mega constellations will extend from the terrestrial network to the integrated satellite-terrestrial architecture, so as to realize the improvement of wireless coverage capability through extending the spatial and temporal coverage. However, what are the specific quantitative indicators of wireless coverage? What is the basis for the effect of network configuration with mega constellations on coverage performance? How to form non-uniform coverage through intelligent resource scheduling to match non-uniformly distributed service requirements in the future 6G STIN? The aforementioned unknown fundamental problems have become a bottleneck restricting the further development of coverage expansion in the future 6G STIN. In this paper, we start with the evolution route of wireless coverage and the vision of 6G coverage and propose coverage performance evaluation metrics in 6G STINs from the perspective of signal coverage, capacity coverage, and service coverage. Furthermore, we investigate the relationship between coverage structure and coverage capability in 6G STINs with mega constellations and we find network structure characteristics suitable for 6G non-uniform service requirements, thus guiding constellation design in 6G STINs by analyzing and comparing the coverage performance of several typical mega constellations. Afterwards, we explore the application of artificial intelligence in resource collaboration to provide technology reference to enhance coverage capability for dynamic 6G service demands. Finally, we analyze possible technical challenges for improving service coverage performance in 6G STINs to provide researchers with new ideas.

Inverse synthetic-aperture radar (ISAR) can achieve precise imaging of targets, which enables precise perception of battlefield information, and it has become one of the most important tasks for radar systems. In multi-target scenarios, a resource scheduling method is required to improve the sensing ability and the overall efficiency of a radar system due to the limited resources. Considering the motion state of the target will change as the observation distance increases and image defocusing can occur due to the prolonged coherence accumulation time and significant changes in the target’s motion state, the optimal observation period should be an important consideration factor in the resource scheduling method to further improve the imaging efficiency of radar system, which has not yet been involved in existing research. In this paper, we first derive the expressions of the target’s effective rotation angle and the equivalent rotation angular velocity and then define the target’s optimal observation period. Then, for multi-target imaging scenarios, we allocate pulse resources within a given time period based on sparse-aperture ISAR imaging technology. An adaptive radar resource scheduling algorithm for multi-target ISAR imaging is proposed, which prioritizes allocating resources based on the optimal observation periods for the targets. In the algorithm, a radar resource scheduling model for multi-target ISAR imaging is established, and a feedback-based closed-loop search optimization method is proposed to solve the model. Finally, the best scheduling strategy can be obtained, which includes imaging task duration and the pulse allocation sequence for each target. Simulation results validate the effectiveness of the algorithm.

This paper investigates the formation joint resource scheduling problem from the perspective of cooperative jamming against radar systems. First, the formation survivability is redefined based on the task requirements. Then, a hierarchical adaptive scheduling strategy solution framework is constructed for state prediction and detection fusion of the networked radar system. Considering the scene constraints, an Improved Adaptive Parameter Evolution Marine Predators Algorithm is designed as an optimizer and embedded in the proposed framework to jointly optimize the platform beam allocation and jamming mode selection. Based on the original algorithm, real number random coding is used to perform dimensional conversion of decision variables, an adaptive parameter evolution mechanism is designed to reduce the dependence on algorithm parameters, and an adaptive selection mechanism for dominant strategies and a search intensity control strategy are proposed to help decision-makers explore the optimal resource scheduling strategy quickly and accurately. Finally, considering the formation maneuvering behavior and incomplete information, the proposed method is compared with existing base strategies in different typical scenarios. It is proved that the proposed strategy can fully exploit the limited jamming resources and maximize the survivability of the formation in radar system cooperative jamming scenarios, demonstrating superior jamming performance and shorter decision time.

With the increasing number of satellite, the satellite control resource scheduling problem (SCRSP) has been main challenge for satellite networks. SCRSP is a constrained and large scale combinatorial problem. More and more researches focus on how to allocate various measurement and control resources effectively to ensure the normal running of the satellites. However, the sparse solution space of SCRSP leads its complexity especially for traditional optimization algorithms. As the validity of ant colony optimization (ACO) has been shown in many combinatorial optimization problems, a simple ant colony optimization algorithm (SACO) to solve SCRSP is presented in this paper. Firstly, we give a general mathematical model of SCRSP. Then, a optimization model, called conflict construction graph, based on visible arc and working period is introduced to reduce workload of dispatchers. To meet the requirements of TT & C network and make the algorithm more practical, we make the parameters of SACO as constant, which include the bounds, update and initialization of pheromone. The effect of parameters on the algorithm performance is studied by experimental method based on SCRSP. Finally, the performance of SACO is compared with other novel ACO algorithms to show the feasibility and effectiveness of improvements.

With the rise of Internet of Things (IoT) technology, fog computing has emerged as a promising solution for low-latency and real-time applications. As a highly virtualized platform, fog computing provides computing and storage services at the network edge to meet users’ needs for latency-sensitive applications. However, resource scheduling is crucial in meeting customer demands and improving service quality. If the resource scheduling problem for large-scale service requests cannot be effectively solved, it will reduce resource utilization and decrease user satisfaction. Therefore, we propose a resource scheduling model called Normalization Processing to find the optimal pheromone for achieving the lowest total cost. The optimal resource scheduling result can be achieved by changing the ant pheromone concentration in the simulated foraging process. We also propose a resource scheduling algorithm called New Genetic Ant Colony Optimization (NGACO) Algorithm that a combination of the improved genetic algorithm (GA) and the improved ant colony optimization (ACO) algorithm. The GA is improved by incorporating a randomized initialization strategy, while the ACO algorithm is enhanced with the use of niche technology. NGACO algorithm introduces a pheromone update method optimization of three operators and a pheromone correction factor in the pheromone update rule. It can update pheromone generation by roulette algorithm. The NGACO algorithm effectively improves the exploratory power of the algorithm while ensuring initial population diversity. Additionally, we introduce a penalty mechanism to handle constraints, while the niche technology addresses the optimization problem of multimodal functions. The experimental results show that the NGACO algorithm demonstrates excellent resource scheduling performance, with a 14.7%, 25%, and 12.8% reduction in makespan, economic cost, and total cost, respectively, compared to the ACO algorithm. Furthermore, the load balancing is 34.7% higher than the ACO algorithm.

Inverse synthetic aperture radar (ISAR) three-dimensional (3D) imaging technology enables the acquisition of clear 3D structures of targets, significantly enhancing target recognition performance. In resource-constrained environments, an effective resource scheduling algorithm is essential for achieving high-quality 3D imaging of multiple targets. However, existing algorithms often neglect the quality requirements of 3D imaging during resource allocation. A resource scheduling algorithm for multi-target 3D imaging in a radar network based on deep reinforcement learning (DRL) is proposed in this paper, achieving multi-target 3D imaging with minimal time resource consumption while ensuring the imaging quality of targets. First, based on the projection-based multi-view ISAR 3D imaging method, the impact of the radar distribution and radar number on the target imaging quality is analyzed. Subsequently, a resource scheduling model is constructed with the objective of minimizing time consumption while ensuring target imaging quality. The problem is then formulated as a Markov decision process, and the Advantage Actor–Critic (A2C) deep reinforcement learning method is employed to solve the model. By reasonably designing the reward for reinforcement learning and pruning the action space based on domain knowledge, the convergence speed of the network is significantly accelerated. An optimal scheduling strategy including a radar node allocation scheme and timing pulse allocation scheme for each radar can be obtained after convergence. The simulation experiments validate the effectiveness of the proposed algorithm.

Different from traditional construction project management, the prefabricated building (PB) engineering has a complicated restricted relationship in scheduling scheme, and the entire project is accomplished by multi-stage collaboration in construction process. Consequently, it is of great importance for project managers to make reasonable resources scheduling to avoid disruptions caused by resource unavailability. However, the poor interoperability and interactivity still results in diverse constraints, which limit the PB construction progress. Therefore, the aim of this paper is to establish a PB resource scheduling model that satisfying resource constraints and strengthening the PB dispatch time connection. The PB construction process is divided into assembly work space, logistics work space, and production work space where the construction time note is regarded as connection constraint and the three work spaces are restrained mutually. What’s more, the optimal total amount of resources determination technology is presented to arrange the resource schedule in assembly work space to ensure the optimal resource quantity with the goals of the shortest construction time and the lowest cost. The dynamic scheduling coordination technology is put forward to logistics work space and production work space where the resource schedule is arranged with time node constraint. A high-rise building is presented as an example to illustrate the implementation of the proposed model. Results show that the presented method could effectively solve the problem of resource tension problem under the goal of the lowest cost as well as alleviate the shortage of multi-resource scheduling.