Priority based Dynamic Resource Allocation and Scheduling for Wimax Networks

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.

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.

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.

W-CDMA is the strongest candidate as the air interface technology of the third generation mobile communication systems, which are expected to support multimedia services with QoS requirements. In this paper, we review the W-CDMA technology and consider the issues in multimedia support. We propose the dynamic resource scheduling mechanism for QoS provisioning in W-CDMA through optimal power assignment and code hopping. Our framework is able to carry variable QoS multimedia traffic, efficiently serving a wide range of classes with CBR, VBR, ABR and UBR characteristics. Dynamic resource scheduling improves capacity and throughput performance. Other advantages are power saving and faster convergence to the desired power level.

Advance reservation of lightpaths in grid environments is necessary to guarantee QoS and reliability. In this paper, we have evaluated and compared several algorithms for dynamic scheduling of lightpaths using a flexible advance reservation model. The main aim is to find the best scheduling policy for a grid network resource manager that improves network utilization and minimizes blocking. The scheduling of lightpaths involves both routing and wavelength assignment. Our simulation results show that minimum‐cost adaptive routing where link costs are determined by the current and future usage of the link provides the minimum blocking. For wavelength assignment, we have used a scheme that reduces fragmentation by minimizing unused gaps. We have also analyzed approaches for failure recovery and resource optimization. Copyright © 2008 John Wiley & Sons, Ltd.

Emergency projects' optimal resource scheduling covers a complex and dynamic environment. The theory of constraints confirms that a bottleneck determines the whole system's performance. Dynamic scheduling of bottlenecks can simplify the complex system's problems. Firstly, the problem's characteristics were analyzed from the aspects of the optimal goals, which are the machine environment, processing characters, and constraints. The mathematical model was then built, and using the hybrid rescheduling strategy and the improved genetic algorithm, the dynamic scheduling of the bottleneck resources was accomplished. Finally, an example case demonstrates the algorithm's feasibility and effectiveness.

Achieving sustainable profit advantage, cost reduction and resource utilization are always a bottleneck for resource providers, especially when trying to meet the computing needs of resource hungry applications in mobile edge-cloud (MEC) continuum. Recent research uses metaheuristic techniques to allocate resources to large-scale applications in MECs. However, some challenges attributed to the metaheuristic techniques include entrapment at the local optima caused by premature convergence and imbalance between the local and global searches. These may affect resource allocation in MECs if continually implemented. To address these concerns and ensure efficient resource allocation in MECs, we propose a fruit fly-based simulated annealing optimization scheme (FSAOS) to serve as a potential solution. In the proposed scheme, the simulated annealing is incorporated to balance between the global and local search and to overcome its premature convergence. We also introduce a trade-off factor to allow application owners to select the best service quality that will minimize their execution cost. Implementation of the FSAOS is carried out on EdgeCloudSim Simulator tool. Simulation results show that the FSAOS can schedule resources effectively based on tasks requirement by returning minimum makespan and execution costs, and achieve better resource utilization compared to the conventional fruit fly optimization algorithm and particle swarm optimization. To further unveil how efficient the FSAOSs, a statistical analysis based on 95% confidential interval is carried out. Numerical results show that FSAOS outperforms the benchmark schemes by achieving higher confidence level. This is an indication that the proposed FSAOS can provide efficient resource allocation in MECs while meeting customers’ aspirations as well as that of the resource providers.

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.

Increasing traffic demand has prompted the construction of additional runways and taxiways in the National Airspace System (NAS). As airport taxi networks become more complex, the difficulty of managing these operations often results in surface congestion and delays. New technologies are needed to help manage these airport surfaces. Typically, airport capacity is described in terms of the tradeoff in the use of the airport runways – a Pareto-optimal curve represents the set of optimal tradeoffs. In this paper, a MaxFlow algorithm is used as the basis for determining instantaneous capacity states for an airport described as a network. This generalizes the notion of capacity to include the “end-to-end” (runway to gate) airport throughput. Results obtained in applying MaxFlow to several airports are compared with Pareto curves. Issues of “reachability” of each capacity state (or configuration) and “workload” required to transition between arbitrary states are investigated so as to best match the network configuration (direction of flow) to a timevarying demand. A simple scheduling heuristic is introduced to implement airport-wide configuration states and the results of several experiments determine the feasibility of the approach.

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

With the increasing capabilities of mobile terminals, the reception of mul-timedia content is becoming more and more attractive for customers. Im-proving high bandwidth provision enables 3G cellular systems to deliver such data to the end user. The 3G mobile experience is following a trend towards personalized services and applications which are used not only for voice communication but also for entertainment. Customers favour short video/audio clips rather than watching complete movies on their small screen. The deployment of new broadcasting networks like Digital Video Broadcasting - Handheld (DVB-H) results in large costs both in terms of capital as well as operating expenditure. Hence it is an enormous commer-cial risk for providers to install such systems. Multimedia Broadcast Multicast Service (MBMS) is considered a prom-ising technology to distribute multicast and broadcast services efficiently and at reasonable costs within existing 3GPP cellular networks. Enriched interactive multimedia services can be offered providing, for example, the latest news or publishing the current top ten music video clips. The trend towards user generated content distribution which is observed in the Inter-net will no doubt increase the number of available services and content. Set against this background of predicted increase both in terms of num-ber of services/content and users, the competition for limited resources will only intensify. MBMS is regarded as a bearer technology and not a com-plete service provisioning system therefore efficient scheduling for optimal utilization of radio and network resources is essential. Scheduling in MBMS comprises several levels of abstraction which require different strategies. This chapter presents several scheduling techniques and ap-proaches for MBMS content distribution. It explains the concepts of (1) a Dynamic MBMS Resource Scheduler located in the Radio Access Network (RAN), (2) a Carousel Service Scheduling approach and (3) means for an optimal scheduling of Multicast Streaming Services. The latter two sched-ule services and content while the first deals with IP packets.

This paper proposes a novel framework for virtual content delivery networks (CDNs) based on cloud computing. The proposed framework aims to provide multimedia content delivery services customized for content providers by sharing virtual machines (VMs) in the Infrastructure-as-a-Service cloud, while fulfilling the service level agreement. Furthermore, it supports elastic virtual CDN services, which enables the capabilities of VMs to be scaled to encompass the dynamically changing resource demand of the aggregated virtual CDN services. For this, we provide the system architecture and relevant operations for the virtual CDNs and evaluate the performance based on a simulation.

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.

DRDC Valcartier has initiated, through a PRECARN partnership project, the development of an advanced simulation testbed for the evaluation of the effectiveness of Network Enabled Operations in a coastal large volume surveillance situation. The main focus of this testbed is to study concepts like distributed information fusion, dynamic resources and networks configuration management, and self synchronising units and agents. This article presents the requirements, design and first implementation builds, and reports on some preliminary results. The testbed allows to model distributed nodes performing information fusion, dynamic resource management planning and scheduling, as well as configuration management, given multiple constraints on the resources and their communications networks. Two situations are simulated: cooperative and non-cooperative target search. A cooperative surface target behaves in ways to be detected (and rescued), while an elusive target attempts to avoid detection. The current simulation consists of a networked set of surveillance assets including aircraft (UAVs, helicopters, maritime patrol aircraft), and ships. These assets have electrooptical and infrared sensors, scanning and imaging radar capabilities. Since full data sharing over datalinks is not feasible, own-platform data fusion must be simulated to evaluate implementation and performance of distributed information fusion. A special emphasis is put on higher-level fusion concepts using knowledge-based rules, with level 1 fusion already providing tracks. Surveillance platform behavior is also simulated in order to evaluate different dynamic resource management algorithms. Additionally, communication networks are modeled to simulate different information exchange concepts. The testbed allows the evaluation of a range of control strategies from independent platform search, through various levels of platform collaboration, up to a centralized control of search platforms.

The main objective of this study was to assess the effect of resource management on performance of selected international compassion projects in Rwanda while the following are specific objectives: to assess the effect of resource planning on performance of International Compassion projects in Kinyinya Parish, Rwanda, to assess the effect of resource scheduling on performance of International Compassion projects in Kinyinya Parish, Rwanda, to assess the effect of resource allocation on performance of International Compassion projects in Kinyinya Parish, Rwanda, and to assess the effect of resource monitoring on performance of International Compassion projects in Kinyinya Parish. The stratified sample of 132 individuals, using Yamane’s formula, is expected to be used and was selected among 197 staffs engaged in projects financed by International Compassion in ADPR where all parishes of Gasabo were considered The SPSS Version 23 was used to perform the analysis while the questionnaire was used as the main tool for data collection.  The study revealed that a mixed survey design, being both descriptive and correlative, was used in this study where this study involved gathering and analyzing data to describe the current state of resource management and correlate it with the project performance within compassion projects, in Gasabo district. The findings on model summary indicate R-value of 0.407 indicates a moderate positive correlation between the combined predictors (Resource monitoring, Resource allocation, and Resource scheduling) and project performance (Performance of Projects). However, the relatively low R Square value of 0.166 suggests that only approximately 16.6% of the variability in project performance can be explained by these predictors, with a substantial portion of variability remaining unaccounted for. Analysis of Variance (ANOVA) results demonstrating the statistical significance of the regression model, encompassing the predictors "Report monitoring, Resource allocation, Resource planning, and Resource scheduling," in explaining variations in the dependent variable "Performance of Projects". Importantly, the associated p-value (Sig.) of 0.002 (denoted as "b") falls below the conventional significance threshold of 0.05, indicating the model's statistical significance. Therefore, it was recommended, encourage continuous improvement in resource planning adequacy to minimize restructuring needs. Key terms: Resource planning, Resource scheduling, Resource allocation, Resource monitoring and performance