Distributed collaborative scheduling technology for random access in real-time

In a 5G ultra-dense network, dynamic network topology and traffic patterns lead to excessive system overhead and complex radio resource conflicts. The cloud radio access network and the fog computing have the advantages of high computation capabilities and low transmission delays. Therefore, by taking full advantage of these two characteristics, this study proposes a novel radio resource coordination and scheduling scheme in an ultra-dense cloud-based small cell network. Interference among small cells (or remote radio heads) can be avoided by implementing centralized cooperative processing in the base band unit pool in advance. Resource sharing in coordination and transfer depend on fog computing to relieve the overloaded cloud processing platform and reduce transmission delays, thereby maximizing resource utilization and minimizing system overhead when the network topology and number of users change dynamically. The simulation shows that the proposed scheme can increase the system throughput by 20% compared with the clustering-based algorithm; it can also increase system throughput by 33% compared with the graph coloring algorithm, decrease the signaling overhead by about 50%, and improve network’s quality of service.

The resource scheduling problem becomes complex, as resources are distributed, heterogeneous, dynamic and autonomous. A resource scheduling scheme with three-level for grid computing is proposed in this paper by analyzing present resource scheduling scheme. Petri net is a powerful graphical and mathematics tool for describing the concurrent, asynchronous and dynamic events. This paper models and analyzes the resource scheduling scheme of grid computing using hierarchical Petri net. Different Petri net models for resource scheduling of different layers are given in this paper. Petri model for different layer has different behavior, which represents heterogeneous and autonomous features of grid computing resources. Finally, we get some important results such as throughput, load balance and makespan on resource scheduling by analyzing reachability of Petri nets.

Resource scheduling and performance analysis of hybrid renewable energy systems with carbon neutrality consideration: A scenario-based multi-agent approach

Distributed computing resources are aggregated in a high-performance computing environment. The resources are appropriately allocated according to the user assignments. However, current resources scheduling strategies overly focus on system performance rather than the user fairness. Moreover, most strategies lack the environment adaptability. In order to solve these problems, this paper selects five different priority variables and proposes a multi-dimensional intelligent heterogeneous resource scheduling method for a high-performance cluster. It introduces the job environment queue, refines the state of the job system layer, optimizes the job priority scheduling strategy, and uses the genetic algorithm to optimize the priority parameters to increase the configurability of the job scheduling strategy. Using the algorithm, the overall performance of the platform is enhanced by 12.3% based on the PBS (Portable Batch System) scheduling system.

Heterogeneous Distributed Computing (HDC) system consists of Workstations (WSs) and Personal Computers (PCs). In HDC system, each WS/PC may have different processor and performance. In order to take advantage of this diversity of processing power of a system, an effective task partitioning, scheduling, and load balancing are needed to get the optimum performance. This paper examines the effectiveness of task partitioning and scheduling strategies for image (raytracing) processing application on HDC system. The static and dynamic/Run-time Task Scheduling (RTS) strategies are shown inadequate for balancing the load of HDC system. Two adaptive tasks scheduling strategies are proposed for HDC image computing system. These adaptive strategies are: i) Master Initiate Sub-task size (MIS) strategy (based on centralized resources management approach), and ii) Worker Initiated Sub-task size (WIS) strategy (based on semi-decentralized resources management approach). The measured results show that the adaptive (MIS & WIS) strategies dramatically improve the performance of HDC raytracing system and remedy the defects of static and RTS strategies. Performances of the all investigated strategies are evaluated on manager/master and workers model of HDC system. In performance comparisons of MIS and WIS strategies, the WIS strategy shows a slightly better speedup and scalability over MIS strategy.

Docker as the representative container technology promotes the rapid development of container cloud with its flexible, efficient and fast characteristics. As a better resource virtualization technology, container is widely recognized and favored by the industry. The resource scheduling strategy in the container cloud has an important impact on the cluster performance. The default priorities strategy of Kubernetes container orchestration tool only considers CPU and memory. With the complexity of the production environment, only considering the above two factors in the priorities stage can easily lead to some node load imbalance, which is difficult to guarantee the cluster service performance. In order to balance the resources of container clusters, this paper proposes a new priorities stage strategy based on Kubernetes' default scheduling strategy, which takes the storage and network bandwidth of task nodes into account, then calculates the weight of each resource, and finally puts it into the scoring formula as the basis of resource scheduling. By comparing the load balancing rate of the default priorities scheduling algorithm and the improved priorities scheduling algorithm, it is verified that the improved container cloud resource scheduling strategy can make the load of the task nodes in the cluster more balanced.

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.

The explosive growth of the Internet of Things (IoT) has led to the emergence of the IoT-fog-cloud continuum, in an attempt to facilitate the real-time processing of IoT data. In such multi-tier environments, it is crucial to adopt an efficient resource allocation and scheduling scheme, in order to provide effective load balancing and timeliness for the real-time workload. A load balancing approach that has been proven to be efficient and effective in traditional distributed environments, is the power of two choices – or <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> choices, in its general form. Only recently has this technique been examined in multi-tier environments, without considering, however, important aspects of such frameworks. To this end, in this paper we propose and investigate three resource allocation and scheduling heuristics for real-time workflow jobs in an IoT-fog-cloud environment. The first strategy, performs exhaustive search at each scheduling step in order to find the most suitable resource in the fog and cloud layers for the workload assignment. On the other hand, the two other policies adopt the power of two choices approach. The simulation results shed light on interesting insights regarding the performance and applicability of each method.

Orthogonal Frequency-Division Multiple Access (OFDMA) systems have attracted considerable attention through technologies such as 3GPP Long Term Evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX). OFDMA is a flexible multiple-access technique that can accommodate many users with widely varying applications, data rates, and Quality of Service (QoS) requirements. OFDMA has the advantages of handling lower data rates and bursty traffic at a reduced power compared to single-user OFDM or its Time Division Multiple Access (TDMA) or Carrier Sense Multiple Access (CSMA) counterparts. In our work, we propose a Particle Swarm Optimization based resource allocation and scheduling scheme (PSORAS) with improved quality of service for OFDMA Systems. Simulation results indicate a clear reduction in delay compared to the Frequency Division Multiple Access (FDMA) scheme for resource allocation, at almost the same throughput and fairness. This makes our scheme absolutely suitable for handling real time traffic such real time video-on demand.

Besides avoiding inter-symbol interference and leading to high capacity, wireless orthogonal frequency division multiplexing (OFDM) provide fine granularity for resource allocation since they are capable of dynamically assigning sub-carriers to multiple users and adaptively allocating transmit power. The current dominate layered networking architecture, in which each layer is designed and operated independently, results in inefficient and inflexible resource use in wireless networks due to the nature of the wireless medium, such as time-varying channel fading. Thus, we need an integrated adaptive design across different layers. In this paper, we focus on resource allocation and scheduling in wireless multiuser OFDM networks based on joint physical and medium access control (MAC) layer optimization. An adaptive cross-layer design for the downlink multiuser OFDM systems, to maximize the weighted sum capacity of all users, where each user has multiple heterogeneous traffic queues simultaneously is proposed. A packet dependent (PD) scheduling scheme is employed at the MAC layer, which determines the packet transmission order by assigning different weights to different packets, and is shown by simulations more efficient than the previous methods where all packets in a queue have the same weight. The weight design in PD scheduling considers the delay, size and quality of service (QoS) priority level of packets. Each user weight employed in resource allocation at the physical (PHY) layer is obtained by summing up the weights of selected packets for the user. We also deeply investigate the various resource scheduling schemes for comparisons.

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.

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.

Fog computing provides a distributed infrastructure at the edges of the network, resulting in low-latency access and faster response to application requests when compared to centralized clouds. With this new level of computing capacity introduced between users and the data center-based clouds, new forms of resource allocation and management can be developed to take advantage of the Fog infrastructure. A wide range of applications with different requirements run on end-user devices, and with the popularity of cloud computing many of them rely on remote processing or storage. As clouds are primarily delivered through centralized data centers, such remote processing/storage usually takes place at a single location that hosts user applications and data. The distributed capacity provided by Fog computing allows execution and storage to be performed at different locations. The combination of distributed capacity, the range and types of user applications, and the mobility of smart devices require resource management and scheduling strategies that takes into account these factors altogether. We analyze the scheduling problem in Fog computing, focusing on how user mobility can influence application performance and how three different scheduling policies, namely concurrent, FCFS, and delay-priority, can be used to improve execution based on application characteristics.

Cloud computing and big data: Technologies and applications

Media access control (MAC) plays a pivotal role in ensuring proper operation in wireless body area networks (WBANs). However, current solutions still cannot satisfy multiple performance requirements of such networks. In this article, we propose a priority ladders-based resource scheduling (PLRS) scheme to meet these demands. First, we design a multiple-level priority division approach, which is based on health parameters and data types of patients. Unlike only two priorities of nodes in other schemes, there are four priorities in our solution. This is more practical and more fair for different nodes. Moreover, we devise a ladders-based time-slot allocation method, in which time slots of nodes are increased round by round based on the priority ladders of nodes. This reduces the difference of the number of time slots between disparate nodes with close priorities, and accordingly enhances the fairness of nodes. In addition, we design an interval-based task ordering approach, in which nodes use the allocated time slots in dispersed multiple intervals rather than a continuous time span in conventional solutions. This guarantees high throughput of high-priority nodes and short waiting time of emergency data. Extensive simulations exhibit the advantages of PLRS in terms of energy overhead, delay, and utility.