Cloud Computing Resource Scheduling Research Articles

Application of machine learning optimization in cloud computing resource scheduling and management

Application of machine learning optimization in cloud computing resource scheduling and management

Deep reinforcement learning-based methods for resource scheduling in cloud computing: a review and future directions

With the acceleration of the Internet in Web 2.0, Cloud computing is a new paradigm to offer dynamic, reliable and elastic computing services. Efficient scheduling of resources or optimal allocation of requests is one of the prominent issues in emerging Cloud computing. Considering the growing complexity of Cloud computing, future Cloud systems will require more effective resource management methods. In some complex scenarios with difficulties in directly evaluating the performance of scheduling solutions, classic algorithms (such as heuristics and meta-heuristics) will fail to obtain an effective scheme. Deep reinforcement learning (DRL) is a novel method to solve scheduling problems. Due to the combination of deep learning and reinforcement learning (RL), DRL has achieved considerable performance in current studies. To focus on this direction and analyze the application prospect of DRL in Cloud scheduling, we provide a comprehensive review for DRL-based methods in resource scheduling of Cloud computing. Through the theoretical formulation of scheduling and analysis of RL frameworks, we discuss the advantages of DRL-based methods in Cloud scheduling. We also highlight different challenges and discuss the future directions existing in the DRL-based Cloud scheduling.

Farmland fertility algorithm based resource scheduling for makespan optimization in cloud computing environment

Resource scheduling (RS) for makespan optimization in a cloud computing (CC) environment is an important aspect of handling effective resources in the cloud. Makespan optimization defines the minimization of time required to complete a collection of tasks in a computational environment. In the context of CC, makespan optimization aims to reduce the overall time required to execute tasks while effectively allocating and managing resources. RS in CC is a difficult task because of the number and variation of resources accessible and the volatility of usage-patterns of the resource assuming that the resource setting is on the service providers. Therefore, this article presents a Farmland Fertility Algorithm based Resource Scheduling for Makespan Optimization (FFARS-MSO) in Cloud Computing Environment. The presented FFARS-MSO technique is mainly based on FFA, which is stimulated by the farmland fertility in nature where the farmers split the various regions of the farm based on soil quality, and thereby every region's soil quality is distinct from others. In addition, the presented FFARS-MSO technique is utilized for load balancing and uniform distribution of resources depending upon the demand. The simulation outcomes ensure that the FFARS-MSO approach has reached effectual resource allocation over other optimization algorithms.

Emergency logistics resource scheduling algorithm in cloud computing environment

In dealing with emergency logistics, it is essential to use regional and central distribution hubs efficiently, and local and international sources of supply for automotive relief supplies are analyzed in this research. Resource scheduling algorithms employed by service providers to supply and assign resources in an environment are collectively referred to as resource scheduling. Cloud computing uses computational resources pooled and made available across a network, including information storage (cloud) and processing capabilities, requiring user participation in its deployment, operation, and maintenance. The challenging characteristics of such emergency logistics resource scheduling are inconsistencies in tracking, empty miles, and delivery delays. Hence, Emergency Supplier Distribution Mobile Edge Computing (ESD-MEC) research has been designed to improve emergency logistics resource scheduling algorithms in cloud computing. With the abovementioned requirements, ESD may address the distributed scheduling issue of vehicle emergency logistics resources. In particular, the MEC employs a specialized negotiating system to manage the scheduling of resources in impacted regions using an agent-based approach to ESD management in light of the need to do so. The proposed technology helps the decision-maker schedule resources in a dynamic environment and addresses supply demands. An ESD-MEC effectively predicts the rise in emergency logistics resources with faster vehicle strategies in cloud computing. The research concludes that ESD-MEC effectively indicates emergency logistics resource scheduling in cloud computing. The experimental analysis of vehicle logistics outperforms the method in terms of performance, accuracy, prediction ratio, mean square error, and efficiency ratio.

Framework of Cloud Computing Resource Scheduling for Vehicle Fault Diagnosis

Framework of Cloud Computing Resource Scheduling for Vehicle Fault Diagnosis

Retracted: Development and Utilization of College Chinese Curriculum Resources Based on Cloud Computing Resource Scheduling Algorithm

Retracted: Development and Utilization of College Chinese Curriculum Resources Based on Cloud Computing Resource Scheduling Algorithm

Reinforcement Learning to Improve Resource Scheduling and Load Balancing in Cloud Computing

Reinforcement Learning to Improve Resource Scheduling and Load Balancing in Cloud Computing

An Improved Ant Colony Algorithm for Virtual Resource Scheduling in Cloud Computing

In order to solve the problems of uneven spatial distribution of data nodes and unclear weight relationship of virtual scheduling features in cloud computing platform, a virtual resource scheduling method based on improved ant colony algorithm is studied and designed to improve the performance of virtual resource scheduling in cloud computing platform by this method. After analyzing the information resource sequence change of the cloud computing platform, according to the STR - Tree partition graph, a simulated annealing-based algorithm is employed to classify the resource types after optimal scheduling into IO types, middle types and CPU types, and the time span and load balance are set as the measurement indexes. The simulation results show that after applying this method, the occupied resources of the main platform are 535 MB, which are much lower than the other two comparison algorithms, and the method has improved the allocation rationality, resource balance, maximum queue length and energy consumption. This result indicates that applying this virtual resource scheduling method can effectively improve the intelligent scheduling of virtual resources in the cloud computing platform.

Prioritized Energy Efficient Resource Scheduling in Cloud Computing

Resource scheduling in cloud computing is one of the impactful area of research. Cloud service providers maintain its efficacy through proper resource management schemes. Users experience seamless cloud services when cloud service provider manages its resources efficiently. Another aspect in resource management is energy efficient schemes. Energy efficiency largely depends on employment of minimum number of resource servers. This article discusses energy efficient resource scheduling mechanism on multi-layer prioritized resource requests. Resource requests are segregated into three categories and those are scheduled on resource servers based on their fast availability. The work focuses on scheduling resource requests on previously active servers as much as possible before activating any idle servers. Proposed scheduling scheme has been tested on a standard benchmark datasets and performance of the proposed method establishes its efficacy.*

Optimizing cloud resource scheduling for enhanced energy efficiency

Energy consumption in cloud computing plays a vital role in operating costs for both the service provider and the cloud user. The cloud is scalable and can provide access as per demand. Due to this, resource access requests are increasing and submitted to the server. To manage all requests, scheduling is the solution to assign requests with the quality of service. To avoid high operating costs, resource scheduling needs to be energy-aware. In this paper, energy-aware resource scheduling in the cloud is proposed. The total resource utilization of each resource has been calculated, and energy is optimized through the antlion optimization algorithm to avoid high power consumption. The resource is identified with its best utilization value and assigned to submitted workloads on a priority basis. The experimental results of the proposed work are analyzed with existing autonomic frameworks, and it is found that the proposed work performs exceptionally well. In addition to addressing the increasing resource access requests, the proposed energy-aware resource scheduling in cloud computing also focuses on optimizing overall system performance. The antlion optimization algorithm is employed to efficiently manage energy consumption, ensuring that power usage is minimized without compromising the quality of service. The algorithm calculates the total resource utilization for each component in the cloud infrastructure, allowing for a thorough understanding of system dynamics. The paper optimizes cloud computing by prioritizing efficient and capacity-based resource assignment, improving responsiveness, user experience, and reducing costs. The approach also promotes environmental sustainability. Experimental results validate its effectiveness in energy efficiency and system responsiveness, contributing to advanced resource management in cloud computing for sustainable and cost-effective services.

Resource Scheduling and Load Balancing Algorithms in Cloud Computing

A technology called cloud computing makes it possible to offer computing resources including storage, software, and services over the internet. Users can access these resources whenever they need them and just pay for what they actually use. There is a strict need to maintain track of all the resources which are being used by machines to perform certain activities such as scheduling of tasks. Resource scheduling and load balancing are important techniques used in modern computing systems to improve performance and resource utilization. Resource scheduling algorithms are used to allocate resources such as CPU, memory, and storage to different tasks or processes. Load balancing algorithms, on the other hand, distribute incoming traffic or workloads across multiple servers or resources to ensure that no single resource becomes overloaded. These techniques are essential for maintaining the stability and efficiency of large-scale systems, such as cloud computing environments, web services, and distributed systems. There are various kinds of load-balancing algorithms that differ in terms of their complexity, flexibility, and performance. Selecting the right algorithm for a specific system depends on the system's specific requirements and constraints. Overall, resource scheduling and load-balancing algorithms play a crucial role in ensuring the smooth operation of modern computing systems and are an active area of research and development.

Information Extraction and Data Planning of Smart City Based on Internet of Things

In order to solve the problem of slow resource scheduling in the process of smart city management, the author proposes a smart city information extraction and data planning system based on the Internet of Things. IoT requires different technologies for analysis for different data types. Managers use different IoT applications to analyze data from different devices and integrate relevant data for possible machine failure or emergency in smart home applications. Situation is predicted. The system includes a cloud platform intelligence center, uses the cloud management module to monitor various hardware devices, constructs the cloud computing resource scheduling objective function, uses the cultural particle swarm algorithm to solve the objective function, and obtains the cloud computing resource scheduling scheme. The experimental results show that the overall utilization rate of the system is the best, close to 100%. Conclusion. The system can realize the effective management of the smart city and monitor the city situation in real time. When implementing resource scheduling, the task completion time is short, the system utilization rate is high, and the resources can be maximized.

Design and Application of Cloud Resource-Based Ideological and Political Online Course Resource Platform

Online teaching platforms have been popularized and promoted as a result of the development of network information technology, and colleges and universities encourage teachers to use a variety of network teaching platforms to innovate teaching models and improve teaching effectiveness. Using an ideological and political online course as an example, it analyzes the teaching design concepts, instructional effects, and existing problems on the online learning platform, and extracts recommendations for online course construction that have a specific reference for online course teaching. Additionally, aiming at the multiobjective cloud resource scheduling problem, this article aims to optimize the total completion time and total execution cost of the task. It does so by utilizing fuzzy mathematics, establishing a fuzzy cloud resource scheduling model, and proposing a hybrid intelligent optimization algorithm CO. The CO algorithm is validated by randomly generating cloud-computing resource scheduling data using the CloudSim simulation platform. The experimental results indicate that the CO algorithm outperforms traditional cloud resource scheduling algorithms in terms of optimization and load balancing performance.

Exploration on the Optimal Application of Mobile Cloud Computing in Enterprise Financial Management under 5G Network Architecture

In order to improve the effect of modern enterprise financial management, this paper improves the mobile cloud computing resource scheduling algorithm under the 5G network architecture. Moreover, this paper introduces mobile edge cloud computing, MEC server, into the financial 5G network system model. Taking into account the possible multihop RSU transmission of the calculation result back, this paper expresses the delay composition of the calculation task on the local execution and offloading to the MEC server in the form of a formula and proposes an offloading strategy based on the MEC load status. On this basis, this paper constructs a mobile cloud computing optimization system under the 5G network architecture and designs experiments to evaluate the performance of the system proposed in this paper. From the experimental research, we can see that the enterprise financial management optimization system based on mobile cloud computing under the 5G network architecture proposed in this paper has good effects in the optimization of corporate financial management.

A Survey of Game-Theoretic Approach for Resource Management in Cloud Computing

Cloud computing is a groundbreaking technique that provides a whole lot of facilities such as storage, memory, and CPU as well as facilities such as servers and web service. It allows businesses and individuals to subcontract their computing needs as well as trust a network provider with its data warehousing and processing. The fact remains that cloud computing is a resource-finite domain where cloud users contend for available resources to carry out desired tasks. Resource management (RM) is a process that deals with the procurement and release of resources. The management of cloud resources is desirable for improved usage and service delivery. In this paper, we reviewed various resource management techniques embraced in literature. We concentrated majorly on investigating game-theoretic submission for the management of required resources, as a potential solution in modeling the resource allocation, scheduling, provisioning, and load balancing problems in cloud computing. This paper presents a survey of several game-theoretic techniques implemented in cloud computing resource management. Based on this survey, we presented a guideline to aid the adoption and utilization of game-theoretic resource management strategy.

Development and Utilization of College Chinese Curriculum Resources Based on Cloud Computing Resource Scheduling Algorithm

In the current situation of university education in China, there are some problems that need to be solved urgently in college Chinese teaching in China, which seriously restrict the realization and implementation of college Chinese teaching objectives. The emergence and development of cloud computing technology can integrate different information media, and through the integration of traditional text, pictures, videos, and other resource media, it can be presented in a clearer and more vivid way. In this paper, a cloud computing resource scheduling model is established for the realization of the platform for the development and utilization of university Chinese cloud computing curriculum resources. The multi-QoS (Quality of Service) objective constraint problem is transformed into a single objective constraint solving problem. The improved GA (genetic algorithm) is used to solve the single objective constraint problem, and the square of the objective function is used as a fitness function, and a better resource allocation strategy is obtained. Experimental results show that the allocation time of the algorithm proposed in this paper is greatly reduced, and the QoS guarantee ability of cloud computing service providers is effectively improved. It has certain reference value for the further research of cloud computing technology in the development and utilization of college Chinese curriculum resources.

Research on cloud computing adaptive task scheduling based on ant colony algorithm

In order to solve the shortcomings of ant colony algorithm in solving large-scale task scheduling problems in cloud computing, the convergence speed is slow and easy to fall into local optimal solutions. This paper designs an adaptive task scheduling algorithm for cloud computing based on ant colony algorithm. On the basis of the polymorphic ant colony algorithm, a pheromone adaptive update adjustment mechanism is added to improve the convergence speed of the algorithm and effectively avoid the emergence of local optimal solutions. The improved algorithm aims to solve a distribution plan with shorter execution time, lower cost and balanced load rate based on the tasks submitted by users. The traditional ant colony algorithm is compared with the improved adaptive ant colony algorithm through the cloud computing platform. Experimental data shows that the improved adaptive ant colony algorithm can quickly find the optimal solution to the cloud computing resource scheduling problem, shorten the task completion time, reduce the execution cost, and maintain the load balance of the entire cloud system center. The performance of this algorithm is better when solving large-scale task scheduling problems.

Cluster resource scheduling in cloud computing: literature review and research challenges

Cluster resource scheduling in cloud computing: literature review and research challenges

Time and Cost Based Cloud Resource Optimization System

Cloud computing is an emerging technology through which resources can be shared over the internet with different users either free or on a rent basis. Resource scheduling in cloud computing is a challenging area for researchers as is maximum utilization can opt through efficient resource scheduling algorithm. Other than this, virtual machine provisioning, packaging, and availability guarantee decrease the performance. Resource management in cloud could be a time and cost effective activity if it is managed property. These resources are accessible and computable which is totally dependent upon the management techniques applied in cloud.In a cloud setting, heterogeneous, vulnerability, and scattering of resources creates many issues of distribution among the workloads which need to be compute. Specialists still face inconveniences to pick the prudent, material and expend less time to execution of resource portion to the cloud. This investigation delineates an expansive composed writing examination of asset administration inside the space of cloud typically and cloud asset administration based on SLA with multi-objective functions like cost and time. In this paper, an autonomic cloud resource management technique is proposed to resolve identified issues by adopting the self-characteristics mechanism and improved Antlion optimization algorithm and tested in cloudsim toolkit and Aws Ec2 environment. The implementation results of proposed work are the evidence that it is better performing as compared with the existing frameworks, however, the performance evaluation method depends upon the different cloud environment and it may vary.

Cloud Computing Resource Scheduling Based on Improved Particle Swarm Optimization Algorithm

With the limited resources of Cloud computing (hereinafter referred to as CC), we must improve the quality of scheduling and cost optimization. Therefore, we must organically integrate the resource pool of servers and computers, which will distribute resources dynamically according to the needs of users. Through CC Resource scheduling (hereinafter referred to as RS), we can improve resource utilization, which will greatly reduce the use cost. Through the CC sharing architecture model, we can implement QoS according to different needs of users, which can achieve flexible resource allocation. Therefore, we need to reasonably design the CC RS and allocation model, which will improve the resource utilization. At the same time, this algorithm can share information among individuals. However, the general PSO (hereinafter referred to as PSO) algorithm has the problem of discretization. This paper proposes an Improved PSO (hereinafter referred to as FPSO) algorithm, which can better solve the RS problem. Firstly, the goal of task scheduling is proposed. Then, an FPSO algorithm is proposed. Finally, some suggestions are put forward.