Research on Resource Rescheduling of Joint Operations Based on GA-MDLS

Research on Design Method of Dynamic Shop Floor Scheduling System Based on Human-computer Interaction

As part of the DRAGON SLAYER project, the adaptive and reliable distributed file system MELODY has emerged for supporting hard real-time applications in unpredictable environments. In MELODY, the time criticality of tasks and their sensitivity with respect to the latest file information are explicitly used for novel, flexible task scheduling algorithms and file replication management policies, featuring dynamic file replication and relocation of file copies as well as offering file versions of varying recency. The achieved adaptability far outweighs the additional overhead in comparison to simpler models, and enhances both reliability and real-time responsiveness for file access. Both the development of the model and the experimental analysis at the various stages were done in an incremental manner. This was necessary in order to cope with the complexity of the problems resulting from conflicting requirements and dynamic trade-offs (e.g. real-time responsiveness vs. reliability). As the next incremental model extension, and as the major contribution of this paper, a series of integration policies are developed for task and resource scheduling, after redefining the role and order of task and resource scheduling: the periodic and dynamic models (and combinations thereof) are used to invoke the task scheduler for a fixed interval of time. All of these policies compare very convincingly against the "classical" model where the task scheduler only schedules tasks after their resources have been allocated. The results are discussed.

PurposeIn recent years, it is imperative to establish the structure of manufacturing industry in the context of smart factory. Due to rising demand for exchange of information with various devices, and huge number of sensor nodes, the industrial wireless networks (IWNs) face network congestion and inefficient task scheduling. For this purpose, software-defined network (SDN) is the emerging technology for IWNs, which is integrated into cognitive industrial Internet of things for dynamic task scheduling in the context of industry 4.0.Design/methodology/approachIn this paper, the authors present SDN based dynamic resource management and scheduling (DRMS) for effective devising of the resource utilization, scheduling, and hence successful transmission in a congested medium. Moreover, the earliest deadline first (EDF) algorithm is introduced in authors’ proposed work for the following criteria’s to reduce the congestion in the network and to optimize the packet loss.FindingsThe result shows that the proposed work improves the success ratio versus resource usage probability and number of nodes versus successful joint ratio. At last, the proposed method outperforms the existing myopic algorithms in terms of query response time, energy consumption and success ratio (packet delivery) versus number of increasing nodes, respectively.Originality/valueThe authors proposed a priority based scheduling between the devices and it is done by the EDF approach. Therefore, the proposed work reduces the network delay time and minimizes the overall energy efficiency.

Dynamic intelligent scheduling and control of reconfigurable parallel architectures for computer vision/image processing

Aiming at the problem of limited resources and operational time combat air defense combat, naval combat formation analysis dynamic resources scheduling needs, on the basis of the establishment of ship dynamic scheduling model of warship formation combat resources dynamic combat formation resource scheduling, the operational resources and operational objectives, relationship between reasonable allocation of resources to play the warship formation combat operations the maximum efficiency. The design of three kinds of air defense command models: centralized platform free attack mode, command mode, request attack mode of air defense strategy, put forward the dynamic scheduling hybrid algorithm for solving the model, according to the naval fleet air defense combat scenario example for simulation. The simulation results show that the air defense strategy with the request attack mode can have effects on multi-target attacks under the shared combat situation, and the hybrid algorithm based on combat resource dynamic scheduling can meet the real-time operational requirements.

Dynamic scheduling should take into account machine scheduling in that flexible manufacturing system can provide alternative processing routes to produce the same product. Dispatching rules (DRs) are the most prevalent approach in the dynamic scheduling system. Existing rule-based scheduling approaches seldom address the machine selection. Considering both job selection and machine selection, we present contract net based scheduling approach which works by means of interactive biddings. Using dispatching rules, we construct five contract net protocol rules (CNP-rulcs) within the heterarchical control architecture to solve job-shop dynamic scheduling problems. The experimental results of multiple CNP-rules based scheduling approach are provided. Competitive results demonstrate that the CNP-rules not only yield evident improvements in the system performance, but also get better equipment utilization and load balancing level for the scheduling system.

<div class="htmlview paragraph">The rapidly changing needs and opportunities of today's global market require unprecedented levels of interoperability to integrate diverse information systems to share knowledge and collaborate among organizations. The combination of Web services and software agents opens a sophisticated computing paradigm for meeting such requirements. This paper proposes a component called “Agent-based Web Service” (AWS) to provide manufacturing scheduling services for enterprise collaboration. Enterprise level services other than resource scheduling can be replenished later on by introducing a richer set of AWSs. A unique property of this AWS based integration approach is that the scheduling process of an order is orchestrated on the Internet through the negotiation among AWSs. Moreover, the bid proposed by an AWS is supported with dynamic scheduling results of manufacturing resources inside the enterprise. A prototype system for AWS-based manufacturing scheduling is implemented and connected to the backend multi-agent dynamic manufacturing scheduling system.</div>

In this paper, we tackle the challenge of optimizing resource utilization and demand-driven allocation in dynamic, multi-dimensional heterogeneous environments. Traditional containerized task scheduling systems, like Kubernetes, typically rely on default schedulers that primarily focus on CPU and memory, overlooking the multi-dimensional nature of heterogeneous resources such as GPUs, network I/O, and disk I/O. This results in suboptimal scheduling and underutilization of resources. To address this, we propose a dynamic scheduling method for heterogeneous resources using an enhanced Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm that adjusts weights in real time and applies nonlinear normalization. Leveraging parallel computing, approximation, incremental computation, local updates, and hardware acceleration, the method minimizes overhead and ensures efficiency. Experimental results showed that, under low-load conditions, our method reduced task response times by 31–36%, increased throughput by 20–50%, and boosted resource utilization by over 20% compared to both the default Kubernetes scheduler and the Kubernetes Container Scheduling Strategy (KCSS) algorithm. These improvements were tested across diverse workloads, utilizing CPU, memory, GPU, and I/O resources, in a large-scale cluster environment, demonstrating the method’s robustness. These enhancements optimize cluster performance and resource efficiency, offering valuable insights for task scheduling in containerized cloud platforms.

The scheduling of tasks with deadlines on clusters is a key issue for offering quality-of-service (QoS) assurance. A critical challenge in real-time task scheduling is to handle various types of applications. This paper investigates the scheduling problem for processing a set of tasks comprising both divisible and indivisible real-time tasks on cluster systems. Indivisible tasks are characterized by the property that they need to be processed on their entirety on a single processor while divisible tasks can be distributed across several processing nodes by exploiting the underlying data parallelism. We propose a dynamic (on-line) real-time scheduling algorithm referred to as Hybrid Loads Push-Pull Scheduling (HLPPS) algorithm for handling a set of tasks comprising both divisible and indivisible real-time tasks on cluster systems. HLPPS is shown to efficiently exploit the parallelism in divisible tasks without undermining the schedulability of indivisible tasks and thereby optimize the overall performance. We consider two distinct network platforms - tightly coupled and loosely coupled clusters in designing the strategy. We conduct extensive performance evaluation studies to quantify the performance of the proposed algorithm under a variety of scenarios.

Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy of smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto the resource-rich cloud. However, how to achieve energy-efficient computation offloading under the hard constraint for application completion time remains a challenge issue. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate the eDors problem into the energy-efficiency cost (EEC) minimization problem while satisfying the task-dependency requirements and the completion time deadline constraint. To solve the optimization problem, we then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control and transmission power allocation. More importantly, we find that the computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, our experimental results in a real testbed demonstrate that the eDors algorithm can effectively reduce the EEC by optimally adjusting the CPU clock frequency of SMDs based on the dynamic voltage and frequency scaling (DVFS) technique in local computing, and adapting the transmission power for the wireless channel conditions in cloud computing.

Poor surgical scheduling causes major problems in hospital operating rooms, such as long patient wait times, underutilized operating rooms, and high costs. Existing scheduling approaches, which are static or less adaptable, fail to handle real-time unpredictability. To overcome these constraints, this study presents Dynamic Operation Room Scheduling (DORS), a new intraday surgical scheduling system. DORS uses a two-layered architecture: (1) Explainable AI for feature selection that is based on critical scheduling criteria such as Round Robin, and (2) a dynamic scheduling system that includes a Receiving Module, a Checking Module for patient prioritization, and a Scheduling Module provided by a Fuzzy Interface Engine. This system allows for proactive schedule preparation and reactive modifications, making it possible to smoothly include unscheduled surgical operations. In comparison to traditional (FCFS, Round Robin) and optimization-based (genetic algorithm) methods. DORS dynamically modifies schedules to reduce average wait times (AWT), consistently outperforming other approaches by 120–560 min. DORS completes surgical operations more quickly (half of surgical operations in 255–725 min). In addition, DORS retains a modest runtime (45 ms) while increasing scheduling efficiency (98.6%). DORS also demonstrates strong stability, with low Relative Percentage Deviation (RPD) on high-demand days. Finally, DORS achieves the optimal blend of speed, efficiency, and responsiveness, making it the greatest choice for hospitals aiming to eliminate delays, optimize operating room usage, and effectively manage changing surgical needs.

Irregularities of the shapes and sizes of jobs are important factors affecting the performance of a resource scheduling algorithm in a 2-D mesh multicomputer. We examine the performance effect of irregularity, by examining a dynamic scheduling system that schedules jobs with requests that range from regular-shaped partitions of a multicomputer to irregular-shaped partitions. In order to evaluate the effect of irregularity, we examine a job scheduling algorithm called the BWQ-search algorithm, which uses multiple queues for ordering jobs to be placed on a 2-D mesh multicomputer. We find that the performance is similar when the system schedules jobs that request various types of irregular-shaped partitions. A large improvement in performance occurs if all jobs scheduled on the multicomputer request very regular-shaped partitions.

WiMAX network provides effective internet communication between users with low expense and ease of deployment. It is used efficiently in small to medium enterprises. However, proficient resource allocation and scheduling is still a critical requirement in WiMAX networks due to the varying factors related to the network communication. If the network resources are not appropriately allocated, then there are possibilities for missing out critical data, or wasting more resources on transmitting less important data which in turn will have adverse affect on data transmission in the future stages. Hence, in this paper, we propose to develop a Priority Based Dynamic Resource Allocation and Scheduling for WiMAX Networks. In this technique, the incoming data are dynamically allocated, and then users are prioritized to identify the critical users. The allocation and scheduling is performed by considering user priority, robustness and also power consumption rate to ensure effective network performance.

Currently, the scheduling mode used by most TT&C resource scheduling system was based on the mission requirements offered by users. After the scheduling plan is formulated, the resource redundancy has been basically determined, without the advance planning of the resources. This article analyzed the data of the scheduling system, by dividing the requirements of resources into two types of “period” and “peak”. Through analyzing the periodic use of resource historical data, the periodic resource demand will be predicted and guided resources for time-dividing and domain-planning. By analyzing the allocation data of dynamic resource scheduling, the peak resource will be reserved to enable and recycle dynamically in actual. Finally the strategy was verified by simulation, which provided a different approach to improve the utilization rate of the entire network resources.KeywordsData miningPeriodic resourcePeak resource

Deep learning (DL) is gaining popularity in various fields, such as computer vision, speech identification, and self-driving vehicles. Blackwell graphical processing units (GPUs) and Grace central processing units (CPUs) are widely utilized in computing platforms, including portable gadgets and supercomputers. Optimizing performance for multiple-programming workloads on such devices is crucial, as a single application cannot typically utilize all available resources. Developing effective GPU resource administration and scheduling systems is crucial for effective distribution among several clients. Nevertheless, current versions have several flaws. Clients often provide wrong task resource requirements, resulting in cluster resource low utilization. DL-based resource scheduling without considering cluster network features leads to poor operational efficiency. To address these difficulties, this study develops a distributed data center (DDC) framework using Blackwell GPUs and Grace CPUs for resource scheduling and accelerated computing with DL. This study offers a regression model-based technique for estimating task resource requirements, preventing clients from overallocating computer resources. This paper demonstrates smart cluster network-effective scheduling approaches for both prompt and batch forms based on the resource need assessment methodologies described. Study findings demonstrate that the suggested system can improve task computation by 19% overall and reduce mean job completion duration (JCD) by 22% when evaluated against modern approaches. Furthermore, the presented optimization approaches are useful in a variety of contexts.