scholarly journals Edge cloud task scheduling model based on layered excellent gene replication strategy

2021 ◽  
Vol 2132 (1) ◽  
pp. 012002
Author(s):  
Leilei Zhu ◽  
Ke Zhao ◽  
Huaze Lin ◽  
Dan Liu ◽  
Li Li
Keyword(s):  
Resource Allocation ◽  
Task Scheduling ◽  
Large Scale ◽  
Allocation Model ◽  
Good Convergence ◽  
Scheduling Strategy ◽  
Gene Replication ◽  
Individual Fitness ◽  
Replication Strategy ◽  
The Stability

Abstract With the development of the Internet of Things and 5G. Edge cloud technology has gradually become a research hotspot. When facing the massive and concurrent tasks of terminal users, reasonable resource scheduling strategy is a key technology. Because edge cloud needs to respond quickly to real-time tasks and ensure the stability of nodes at the same time, the optimal task scheduling strategy needs to be selected to meet the low latency requirements of edge users. In view of the above problems in resource allocation of edge cloud, this paper established a layered excellent gene replication strategy (HEGPSO model), in which the optimal replicator is added, and an evolutionary particle swarm optimization algorithm is proposed. In each iteration, the population is divided into three layers based on individual fitness. After that, the optimal replication factor is added to each layer of individuals to enhance the global search ability of the algorithm and ensure the good convergence of the algorithm. Finally, a balanced resource allocation model is established. Experiments show that the HEGPSO model proposed in this paper has high fitness and fast convergence speed, and is suitable for large-scale task access scenarios.

scholarly journals Cloud based Cost Effective Resource Allocation Model for Software-as-a-service Deployments

2013 ◽  
Vol 9 (2) ◽  
pp. 1068-1079
Author(s):  
Ibrahim A. Cheema ◽  
Mudassar Ahmad ◽  
Fahad Jan ◽  
Shahla Asadi
Keyword(s):  
Resource Allocation ◽  
Large Scale ◽  
Service Providers ◽  
Cost Effective ◽  
Software As A Service ◽  
Allocation Model ◽  
Application Service Providers ◽  
Resource Allocation Model ◽  
Application Service ◽  
Cloud Infrastructures

The Cloud Computing (CC) provides access to the resources with usage based payments model. The application service providers can seamlessly scale the services. In CC infrastructure, a different number of virtual machine instances can be created depending on the application requirements. The capability to scale Software-as-a-Service (SaaS) application is very attractive to the providers because of the potential to scale application resources to up or down, the user only pay for the resources required. Even though the large-scale applications are deployed on cloud infrastructures on pay-per-use basis, the cost of idle resources (memory, CPU) is still charged to application providers. The issues of saturation and wastage of cloud resources are still unresolved. This paper attempts to propose the resource allocation models for SaaS applications deployments over CC platforms. The best balanced resource allocation model is proposed keeping in view cost and user requirements.

scholarly journals Ultrahigh-Dimensional Model and Optimization Algorithm for Resource Allocation in Large-Scale Intelligent D2D Communication System

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Minxin Liang ◽  
Jiandong Liu ◽  
Jinrui Tang ◽  
Ruoli Tang
Keyword(s):  
Resource Allocation ◽  
Communication System ◽  
Large Scale ◽  
Novel Mutation ◽  
Pso Algorithm ◽  
Spectrum Efficiency ◽  
D2d Communication ◽  
Allocation Model ◽  
User Grouping ◽  
Mutation Strategies

The optimal resource allocation in the large-scale intelligent device-to-device (D2D) communication system is of great importance for improving system spectrum efficiency and ensuring communication quality. In this study, the D2D resource allocation is modelled as an ultrahigh-dimensional optimization (UHDO) problem with thousands of binary dimensionalities. Then, for efficiently optimizing this UHDO problem, the coupling relationships among those dimensionalities are comprehensively analysed, and several efficient variable-grouping strategies are developed, i.e., cellular user grouping (CU-grouping), D2D pair grouping (DP-grouping), and random grouping (R-grouping). In addition, a novel evolutionary algorithm called the cooperatively coevolving particle swarm optimization with variable-grouping (VGCC-PSO) is developed, in which a novel mutation operation is introduced for ensuring fast satisfaction of constraints. Finally, the proposed UHDO-based allocation model and VGCC-PSO algorithm as well as the grouping and mutation strategies are verified by a comprehensive set of case studies. Simulation results show that the developed VGCC-PSO algorithm performs the best in optimizing the UHDO model with up to 6000 dimensionalities. According to our study, the proposed methodology can effectively overcome the “curse of dimensionality” and optimally allocate the resources with high accuracy and robustness.

Research on the Resource Allocation Model for the Satellite Constellation Communication System

2010 ◽  
Vol 121-122 ◽  
pp. 669-677 ◽  
Author(s):  
Li Li Zhu ◽  
Yi Feng Duan
Keyword(s):  
Resource Allocation ◽  
Communication System ◽  
Large Scale ◽  
Mathematic Model ◽  
New Paradigm ◽  
Allocation Model ◽  
Satellite Constellation ◽  
Spatially Distributed ◽  
Degree Of Satisfaction ◽  
Task Oriented

Satellite constellation, emerging as a new paradigm for next-generation communicating, enables large-scale application of the geographically and spatially distributed heterogeneous resources for solving problems in science, engineering, and military affairs. The resource allocation in such a large-scale distributed environment is a complex task. Due to the factors that trigger the deployment of resources in satellite constellation communication system, the artificial immune theory is applied to resource allocation field to propose the task-oriented common mathematic model about resource allocation of communication system, which is aimed at the purpose of improving the effectiveness of resource allocation and is based on the 2 important indicators that are communication task’s effectiveness factors and the degree of satisfaction in the communication system. As the immune system has characteristics of self-adaptive, self-learning and self-organization, an immune allocation algorithm that fuzzy processing time is presented by applying the immune theory to resource allocation. Simulation results show that these methods are feasible and efficient in solving the problems of resource allocation for satellite constellation communication system, and the research on this object is a meaningful exploring.

scholarly journals Study of Task Scheduling Strategy based on Trustworthiness

2021 ◽  
Vol 12 (05) ◽  
pp. 01-09
Author(s):  
Jun QIN ◽  
Yanyan SONG ◽  
Ping ZONG
Keyword(s):  
Task Scheduling ◽  
Programming Model ◽  
Fault Tolerant ◽  
Scheduling Algorithm ◽  
Massive Data ◽  
Scheduling Strategy ◽  
Computing Model ◽  
Scheduling Model ◽  
Task Scheduling Algorithm ◽  
The Stability

MapReduce is a distributed computing model for cloud computing to process massive data. It simplifies the writing of distributed parallel programs. For the fault-tolerant technology in the MapReduce programming model, tasks may be allocated to nodes with low reliability. It causes the task to be reexecuted, wasting time and resources. This paper proposes a reliability task scheduling strategy with a failure recovery mechanism, evaluates the trustworthiness of resource nodes in the cloud environment and builds a trustworthiness model. By using the simulation platform CloudSim, the stability of the task scheduling algorithm and scheduling model are verified in this paper.

An Efficient Kernel FCM and Artificial Fish Swarm Optimization-Based Optimal Resource Allocation in Cloud

2020 ◽  
Vol 29 (16) ◽  
pp. 2050253
Author(s):  
Pravin Albert ◽  
Manikandan Nanjappan
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Optimal Resource Allocation ◽  
Allocation Model ◽  
Swarm Optimization ◽  
Artificial Fish Swarm ◽  
Optimal Resource ◽  
And Task

Cloud computing model allows service-oriented system that fulfills the needs of the consumers. Capable resource management and task allocation are the important issues in cloud computing. Performance of the task scheduling method directly interrupts the utilization of cloud computing resources and the quality of experience of users. For that reason, reasonable virtual machine (VM) allocation and task scheduling are extremely important. In this paper, an efficient resource allocation model is proposed. Initially, the virtual machines are clustered with the help of Kernel Fuzzy [Formula: see text]-Means Clustering (KFCM) algorithm to reduce the complexity. After the clustering process, the user tasks are allocated to the particular VM using artificial fish swarm optimization (AFSO) algorithm. A multi-objective function is designed to achieve an optimal resource allocation. The performance of the suggested technique is tested in terms of different metrics.

scholarly journals Optimization model for QoS based task scheduling in cloud computing environment

2020 ◽  
Vol 18 (2) ◽  
pp. 1081
Author(s):  
Sirisha Potluri ◽  
Katta Subba Rao
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Optimization Model ◽  
Execution Time ◽  
Scheduling Algorithm ◽  
Cloud Services ◽  
Allocation Model ◽  
Task Scheduling Algorithm ◽  
And Task

Shortest job first task scheduling algorithm allocates task based on the length of the task, i.e the task that will have small execution time will be scheduled first and the longer tasks will be executed later based on system availability. Min- Min algorithm will schedule short tasks parallel and long tasks will follow them. Short tasks will be executed until the system is free to schedule and execute longer tasks. Task Particle optimization model can be used for allocating the tasks in the network of cloud computing network by applying Quality of Service (QoS) to satisfy user’s needs. The tasks are categorized into different groups. Every one group contains the tasks with attributes (types of users and tasks, size and latency of the task). Once the task is allocated to a particular group, scheduler starts assigning these tasks to accessible services. The proposed optimization model includes Resource and load balancing Optimization, Non-linear objective function, Resource allocation model, Queuing Cost Model, Cloud cost estimation model and Task Particle optimization model for task scheduling in cloud computing environement. The main objectives identified are as follows. To propose an efficient task scheduling algorithm which maps the tasks to resources by using a dynamic load based distributed queue for dependent tasks so as to reduce cost, execution and tardiness time and to improve resource utilization and fault tolerance. To develop a multi-objective optimization based VM consolidation technique by considering the precedence of tasks, load balancing and fault tolerance and to aim for efficient resource allocation and performance of data center operations. To achieve a better migration performance model to efficiently model the requirements of memory, networking and task scheduling. To propose a QoS based resource allocation model using fitness function to optimize execution cost, execution time, energy consumption and task rejection ratio and to increase the throughput. QoS parameters such as reliability, availability, degree of imbalance, performance and SLA violation and response time for cloud services can be used to deliver better cloud services.

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