Dynamic Resource Scheduling Research Articles

Virtualization and energy management optimization of high speed computer network data centers based on optical switching and network technology

With the development of cloud computing and big data technology, the scale and complexity of data centers are increasing, and the thermal energy consumption management of computer processors can effectively improve the energy efficiency of data centers and reduce operating costs. Therefore, this study discusses the application of high-speed computer network architecture based on optical switching and network technology in data center virtualization. By constructing a high-speed computer network model based on optical switching technology, virtualization technology is used to dynamically schedule and manage resources, combined with real-time monitoring system, and the energy consumption data of the processor is collected. Using the data analysis method, the energy consumption performance under different load conditions is evaluated and the optimization strategy is proposed. The experimental results show that the network architecture using optical switching technology significantly reduces the latency of data center and improves the resource utilization. In the thermal management of the processor, dynamic resource scheduling successfully reduces the energy consumption and effectively maintains the stability and performance of the system. As a result, data center virtualization solutions based on optical switching and networking technologies can significantly optimize the power management of computer processors and improve overall energy efficiency.

Dynamic resource allocation and scheduling for appointment-based systems with walk-ins

Effectively managing constrained and perishable capacity amid fluctuating demands is a common challenge in service industries, often addressed through appointment-based systems that incorporate walk-ins. Balancing appointments and walk-ins is crucial for enhancing system value and maintaining stability. In this article, we study an integrated decision problem for appointment scheduling and resource allocation across multiple facilities, considering stochastic demands from three segments: priority customers, walk-in customers, and appointment customers. We formulate the problem as a sequential scheduling-allocation challenge using stochastic dynamic programming. Leveraging the anti-multimodularity of the value function, we fully characterize the optimal dynamic resource allocation policy and its monotone structural properties under any appointment schedule. Given the complexity and intractability of optimal scheduling with dynamic resource allocation, we introduce two suboptimal scheduling policies—vertical scheduling and horizontal scheduling—and a heuristic allocation policy. Additionally, we create an Upper Bound (UB) for the original problem as a surrogate, establishing an asymptotically optimal UB for the scaled problem through its objective value and a corresponding lower bound through its solution. This UB solution serves as an efficient and effective heuristic, demonstrating superior performance compared with the combined performance of suboptimal scheduling policies with optimal dynamic resource allocation, especially as problem size increases. It also holds significant practical implications for integrated pooling systems.

Mathematical optimization strategy for online scheduling of complex manufacturing systems based on thermal energy optimization and fuzzy mathematical model

With the rapid development of the manufacturing industry and the advancement of the intelligent process, the scheduling problem of complex manufacturing systems becomes more and more complicated, especially in the aspect of energy management and optimization. This study aims to propose an online scheduling mathematical optimization strategy based on thermal energy optimization and fuzzy mathematical model, so as to improve the scheduling efficiency and resource allocation rationality of complex manufacturing systems under different production conditions. A scheduling model considering thermal energy utilization rate, production priority and real-time demand is established by using fuzzy mathematics. The model deals with the uncertain factors by fuzzy logic and solves them by genetic algorithm to realize the dynamic adjustment and optimal scheduling of production resources. Through the experimental verification of a complex manufacturing system, the improvement of scheduling efficiency not only reduces the operating cost, but also improves the flexibility and response speed of the system. The fuzzy mathematical model based on thermal energy optimization provides an effective on-line scheduling strategy for complex manufacturing systems, which can realize efficient scheduling and energy management in dynamic environment.

AI Services-Oriented Dynamic Computing Resource Scheduling Algorithm Based on Distributed Data Parallelism in Edge Computing Network of Smart Grid

Massive computational resources are required by a booming number of artificial intelligence (AI) services in the communication network of the smart grid. To alleviate the computational pressure on data centers, edge computing first network (ECFN) can serve as an effective solution to realize distributed model training based on data parallelism for AI services in smart grid. Due to AI services with diversified types, an edge data center has a changing workload in different time periods. Selfish edge data centers from different edge suppliers are reluctant to share their computing resources without a rule for fair competition. AI services-oriented dynamic computational resource scheduling of edge data centers affects both the economic profit of AI service providers and computational resource utilization. This letter mainly discusses the partition and distribution of AI data based on distributed model training and dynamic computational resource scheduling problems among multiple edge data centers for AI services. To this end, a mixed integer linear programming (MILP) model and a Deep Reinforcement Learning (DRL)-based algorithm are proposed. Simulation results show that the proposed DRL-based algorithm outperforms the benchmark in terms of profit of AI service provider, backlog of distributed model training tasks, running time and multi-objective optimization.

DetFed: Dynamic Resource Scheduling for Deterministic Federated Learning Over Time-Sensitive Networks

DetFed: Dynamic Resource Scheduling for Deterministic Federated Learning Over Time-Sensitive Networks

Comparative Study: Cloud Computing

The concept of cloud computing was invented by the Internet service providers to support a large number of users and elastic services with the least number of resources. This expanding field of cloud computing has become the need of the future and is at the cutting edge. The hosting process of cloud computing has been transformed from internal IT systems to public services, from cost-effective tools to revenue generator tools, and from ISP to telecom services. This paper describes the concept, overview, services of cloud computing, issues and challenges of cloud computing as the value chain and standardization effort. Working of Cloud is the distribution of Computing services which include servers, database, networking devices, storage devices, software, analytics and intelligence-over the Internet (“the Cloud“) to offer faster revolution, cost-saving and flexible resources, and economies of scale. The purpose of cloud computing is to deliver computing services which include servers, storage, database, networking, software, analytics, intelligence, and more, over the Cloud (Internet).Overviewing at the characteristics of cloud computing there are majorly 5 characteristics : (1) large scale computing resources (2) high scalability & elasticity (3)shared resource pool (virtualized and physical resource) (4)dynamic resource scheduling (5) general purpose

Toward Improving Dynamic Resource Scheduling in the Context of Digital Twin of Emergency Department

Toward Improving Dynamic Resource Scheduling in the Context of Digital Twin of Emergency Department

Improved Chimp Optimization Algorithm for Matching Combinations of Machine Tool Supply and Demand in Cloud Manufacturing

Cloud manufacturing is a current trend in traditional manufacturing enterprises. In this environment, manufacturing resources and manufacturing capabilities are allocated to corresponding services through appropriate scheduling, while research on the production shop floor focuses on realizing a basic cloud manufacturing model. However, the complexity and diversity of tasks in the shop floor supply and demand matching environment can lead to difficulties in finding the optimal solution within a reasonable time period. To address this problem, a basic model for dynamic scheduling and allocation of workshop production resources in a cloud-oriented environment is established, and an improved Chimp optimization algorithm is proposed. To ensure the accuracy of the solution, two key improvements to the ChOA are proposed to solve the problem of efficient and accurate matching combinations of tasks and resources in the cloud manufacturing environment. The experimental results verify the effectiveness and feasibility of the improved ChOA (SDChOA) using a comparative study with various algorithms and show that it can solve the workshop supply and demand matching combination problem and obtain the optimal solution quickly.

Secure Multi-Path Selection with Optimal Controller Placement Using Hybrid Software-Defined Networks with Optimization Algorithm

The Internet's growth in popularity requires computer networks for both agility and resilience. Recently, unable to satisfy the computer needs for traditional networking systems. Software Defined Networking (SDN) is known as a paradigm shift in the networking industry. Many organizations are used SDN due to their efficiency of transmission. Striking the right balance between SDN and legacy switching capabilities will enable successful network scenarios in architecture networks. Therefore, this object grand scenario for a hybrid network where the external perimeter transport device is replaced with an SDN device in the service provider network. With the moving away from older networks to SDN, hybrid SDN includes both legacy and SDN switches. Existing models of SDN have limitations such as overfitting, local optimal trapping, and poor path selection efficiency. This paper proposed a Deep Kronecker Neural Network (DKNN) to improve its efficiency with a moderate optimization method for multipath selection in SDN. Dynamic resource scheduling is used for the reward function the learning performance is improved by the deep reinforcement learning (DRL) technique. The controller for centralised SDN acts as a network brain in the control plane. Among the most important duties network is selected for the best SDN controller. It is vulnerable to invasions and the controller becomes a network bottleneck. This study presents an intrusion detection system (IDS) based on the SDN model that runs as an application module within the controller. Therefore, this study suggested the feature extraction and classification of contractive auto-encoder with a triple attention-based classifier. Additionally, this study leveraged the best performing SDN controllers on which many other SDN controllers are based on OpenDayLight (ODL) provides an open northbound API and supports multiple southbound protocols. Therefore, one of the main issues in the multi-controller placement problem (CPP) that addresses needed in the setting of SDN specifically when different aspects in interruption, ability, authenticity and load distribution are being considered. Introducing the scenario concept, CPP is formulated as a robust optimization problem that considers changes in network status due to power outages, controller’s capacity, load fluctuations and changes in switches demand. Therefore, to improve network performance, it is planned to improve the optimal amount of controller placements by simulated annealing using different topologies the modified Dragonfly optimization algorithm (MDOA).

Research on dynamic scheduling and perception method of assembly resources based on digital twin

ABSTRACT The uncertainty and dynamic changes in assembly resources can seriously affect the normal operation of the assembly plant. In response to the problems of incomprehensive resource control mechanism, poor timeliness of monitoring data, and low level of scheduling intelligence in the assembly plant, a dynamic scheduling and perception method of assembly resources based on digital twin is proposed so that the uncertainties in the assembly process can be monitored and dealt with in time. In this paper, a dynamic scheduling model of assembly resources based on a digital twin is constructed, and the operation mechanism of assembly resources in the constructed digital twin model is expounded. And the dynamic perception method of assembly resources based on the Petri network is studied in detail, and the perception and interaction models of four assembly resources in the product assembly process are constructed: workpiece, handling equipment, assembly center, and storage area. Finally, combined with the assembly workshop of enterprise A’s frame factory, the Petri network model is simulated with the help of the CPN Tools simulation tool to obtain real-time and simulation data such as assembly resources and workstation operation time are obtained, which provides a scientific basis for the smooth implementation of enterprise assembly plan and dynamic scheduling of assembly resources.

Modeling and Simulation of Real-Time Virtual Machine Allocation in a Cloud Data Center

For dynamic resource scheduling in cloud data centers, a novel lightweight simulation system is proposed; two existing simulation systems at the application level for cloud computing are reviewed; and results gained using the suggested simulation system are examined and discussed. The usage of resources and energy efficiency in cloud data centers can be improved by load balancing and the consolidation of virtual machines. An aspect of dynamic virtual machine consolidation that directly affects resource usage and the quality of service the system is delivering is the timing of when it is ideal to reallocate Virtual Machines from an overloaded host [1]. Because server overloads result in a lack of resources and a decline in application performance, they have an impact on quality of service. In order to determine the best answer, existing approaches to the problem of host overload detection typically rely on statistical analysis inspired by nature. These strategies' drawbacks include the fact that they provide less-than-ideal outcomes and prevent the explicit articulation of a Quality-of-Service target. By optimizing the mean inter-migration time under the defined Quality of Service target ideally, we present a novel method for detecting host overload for any stationary workload that is known and a particular state configuration [2]. We demonstrate that our technique exceeds the best benchmark algorithm and offers over 88%of the performance of the ideal offline algorithm through simulations with real-world workload traces from more than a thousand Virtual Machines.

Multi-objective load balancing in cloud infrastructure through fuzzy based decision making and genetic algorithm based optimization

Cloud computing became a popular technology which influence not only product development but also made technology business easy. The services like infrastructure, platform and software can reduce the complexity of technology requirement for any ecosystem. As the users of cloud-based services increases the complexity of back-end technologies also increased. The heterogeneous requirement of users in terms for various configurations creates different unbalancing issues related to load. Hence effective load balancing in a cloud system with reference to time and space become crucial as it adversely affect system performance. Since the user requirement and expected performance is multi-objective use of decision-making tools like fuzzy logic will yield good results as it uses human procedure knowledge in decision making. The overall system performance can be further improved by dynamic resource scheduling using optimization technique like genetic algorithm.

Intelligent Blockchain-Enabled Adaptive Collaborative Resource Scheduling in Large-Scale Industrial Internet of Things

With the explosive growth of devices and tasks deployed in the industrial Internet of Things (IIoT), the lack of interconnection and collaboration between devices leads to poor timeliness and security in IIoT resource scheduling. This article focuses on the issue of adaptive scheduling of resources in large-scale IIoT. First, a collaborative terminal-edge IIoT architecture is designed, which introduces blockchain and AI technology to support dynamic resource scheduling in untrustworthy environments. Then, a smart contract-based multidimensional resource transaction model is developed to improve the efficiency and security of resource scheduling by establishing a credit-based consensus mechanism. Distributed transaction learning resource scheduling algorithm is further proposed to implement resource-adaptive scheduling between devices in IIoT. Extensive simulation experiments are conducted to evaluate the proposed method with respect to several performance aspects covering the scheduling decision delay, transaction generation ratio, and security. The obtained results demonstrate that the comprehensive scheduling performance of the proposed method outperforms other existing algorithms.

Hypergraph-Based Autonomous Networks: Adaptive Resource Management and Dynamic Resource Scheduling

As development steadily advances and the capacity for practical applications grows, 5G networks are being deeply expanded in many fields, such as manufacturing, transportation and logistics, and agriculture. The rapid growth in the number of devices in application deployments is causing manual methods of managing resources to become inefficient and network operation costs to increase significantly. Therefore, autonomous networks framework that can adapt to dynamic network environments need to be designed to undertake network resource management. This paper investigates the characteristics of 5G networks, and a hypergraph-based 5G autonomous networks framework is proposed to achieve stable interconnection within the system. First, the network topology is accurately represented by introducing the concept of hypergraph, and an RL algorithm is used to optimize the management of network resources based on the network topology. Then, a BERT model is adopted for resource state awareness from the user satisfaction perspective, and a fuzzy decision based collaborative resource scheduling algorithm is designed to improve service quality. Finally, the key challenges that will still be faced and need to be further solved in the future development of 5G autonomous networks are deeply explored.

Large-Scale Multimedia Communication on A Cloud Computing Platform

Abstract: This research examines the creation of a multimedia communication platform to expand the size of multimedia communication based on cloud computing (CC). The research focuses on high-quality material, multimedia profit avenues, and copyright awareness. Big data diversion under cloud computing optimizes data grouping and pattern identification. Cloud computing's typical large data shunting technology uses open-source embedded system architecture. The connecting coil ties the shunting system's switch to the cloud computing platform's switch, resulting in data loss and corruption. Using this method, it has been shown that CC cannot reach the ideal resource utilization, SLA violation rate, and energy use. Experiments reveal a divergence from this research. The major goal of media development is now the growth and self-awareness of media. Largescale multimedia communication needs an architecture for cloud computing with dynamic growth, efficient processing, and flexible resource scheduling

Dynamic resource scheduling for real-time group broadcasting in 6G cellular vehicular networks

Vehicle-to-Vehicle (V2V) messaging is an indispensable component of connected autonomous vehicle systems. The 3rd Generation Partnership Project (3GPP) is designed to support V2V communication without any infrastructure using sensing-based Semi-Persistent Scheduling (SPS) in order to avoid resource collisions for Cooperative Awareness Messages (CAMs) transmission. However, the legacy system suffers from significant collisions in a traffic congestion environment and resource waste in light traffic conditions. To address these problems, in this paper we propose a novel dynamic resource scheduling algorithm for real-time group broadcasting, inspired by the Lyapunov optimization framework, where the optimization objective is to minimize time-average failure probability subject to queue stability. Through extensive simulations, we show that the proposed scheduling algorithm outperforms the standard in both traffic jams and leisurely road environments.

Dynamic domain transformation resource scheduling approach: water irrigation scheduling for urban farming

<p>Scheduling resources under limited resources using tailored approaches can be done successfully. However, there are situations and problems that require a schedule to handle uncertainties dynamically. The changes in the environment could lead to a non-optimal schedule, which could lead to the wastage of resources. The infeasible schedule could also be an outcome of changes that would render the schedule obsolete, and a new schedule must be generated. The majority of the scheduling problems are solved by a heuristic approach that utilizes a random number generator, thus the outcome is not guaranteed to be optimal. Domain transformation approach (DTA) is a scheduling methodology that has confirmed its expressive power in producing feasible and good quality schedules through avoidance of randomness elements as highly used in heuristic approaches. DTA has been employed in this study to solve the water irrigation scheduling for urban farming. The proposed model was tested on three different datasets. It was observed that the costs obtained on all datasets without utilizing the dynamic DTA are higher in all instances, which indicates that the solution produced by DTA is of higher quality. Thus, dynamic DTA is a more effective way of scheduling resources with considering ad-hoc changes.</p>

Low-Complexity Dynamic Resource Scheduling for Downlink MC-NOMA Over Fading Channels

Author(s): Kim, Do-Yup; Jafarkhani, Hamid; Lee, Jang-Won | Abstract: In this paper, we investigate dynamic resource scheduling (i.e., joint user, subchannel, and power scheduling) for downlink multi-channel non-orthogonal multiple access (MC-NOMA) systems over time-varying fading channels. Specifically, we address the weighted average sum rate maximization problem with quality-of-service (QoS) constraints. In particular, to facilitate fast resource scheduling, we focus on developing a very low-complexity algorithm. To this end, by leveraging Lagrangian duality and the stochastic optimization theory, we first develop an opportunistic MC-NOMA scheduling algorithm whereby the original problem is decomposed into a series of subproblems, one for each time slot. Accordingly, resource scheduling works in an online manner by solving one subproblem per time slot, making it more applicable to practical systems. Then, we further develop a heuristic joint subchannel assignment and power allocation (Joint-SAPA) algorithm with very low computational complexity, called Joint-SAPA-LCC, that solves each subproblem. Finally, through simulation, we show that our Joint-SAPA-LCC algorithm provides good performance comparable to the existing Joint-SAPA algorithms despite requiring much lower computational complexity. We also demonstrate that our opportunistic MC-NOMA scheduling algorithm in which the Joint-SAPA-LCC algorithm is embedded works well while satisfying given QoS requirements.

Elastic Resource Provisioning Using Data Clustering in Cloud Service Platform

Currently, cloud computing has received great attention in commerce and scientific research due to its flexibility and strong data processing capability. However, in view of the fact that the types of tasks display an upward trend as the growth of service demands, and the different types of tasks arrive at the system without regularity. Moreover, the resources deployed in cloud are insufficiency to be flexibly provisioned in the face of obvious workload fluctuations. In this article, we present a method of elastic resource provisioning using date clustering in cloud service platform. The framework of proposed method consists of three core components: tasks clustering, the amount of tasks prediction in cluster, dynamic resource provisioning and scheduling. In workload classification, we propose a clustering ensemble method, which utilizes a novel distance decision-making method to obtain the final results. Our method can effectively partition the arriving tasks into several clusters based on similarity among tasks. For each cluster, we forecast the amount of tasks arriving at next moment by prediction model based on time-series to provide reference for the follow-up resource provisioning. Afterwards, an energy-saving resource provisioning method is designed to dynamically provide resources for tasks in each cluster to meet their performance requirements. We implement the experiments in Google cloud traces dataset and the results show that our method achieves 92.3, 91.2 percent, and 3679.2 kW<inline-formula><tex-math notation="LaTeX">$ \cdot$</tex-math><alternatives><mml:math><mml:mo>&#x00B7;</mml:mo></mml:math><inline-graphic xlink:href="zhu-ieq1-3002755.gif"/></alternatives></inline-formula>h respectively in terms of guarantee ratio, resource utilization and total energy consumption, which demonstrates the effectiveness of proposed method for dynamic resource provisioning.

Dynamic scheduling of heterogeneous resources across mobile edge-cloud continuum using fruit fly-based simulated annealing optimization scheme

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.