A Novel Fault-tolerant Scheduling Algorithm with High Reliability in Cloud Computing Systems

2012 ◽  
Vol 7 (15) ◽  
pp. 107-115 ◽  
Author(s):  
Yun Ling ◽  
Yi Ouyang ◽  
Zhenshan Luo
Keyword(s):  
Cloud Computing ◽  
Fault Tolerant ◽  
High Reliability ◽  
Scheduling Algorithm ◽  
Computing Systems

scholarly journals Minimizing Energy Consumption Scheduling Algorithm of Workflows With Cost Budget Constraint on Heterogeneous Cloud Computing Systems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 205099-205110
Author(s):  
Longxin Zhang ◽  
Lan Wang ◽  
Zhicheng Wen ◽  
Mansheng Xiao ◽  
Junfeng Man
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Scheduling Algorithm ◽  
Budget Constraint ◽  
Computing Systems ◽  
Heterogeneous Cloud

scholarly journals SLA-DQTS: SLA Constrained Adaptive Online Task Scheduling Based on DDQN in Cloud Computing

2021 ◽  
Vol 11 (20) ◽  
pp. 9360
Author(s):  
Kaibin Li ◽  
Zhiping Peng ◽  
Delong Cui ◽  
Qirui Li
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Performance Optimization ◽  
Heuristic Algorithms ◽  
Scheduling Algorithm ◽  
Computing Systems ◽  
Q Learning ◽  
Reward Function ◽  
Task Scheduling Algorithm ◽  
Comparison Algorithms

Task scheduling is key to performance optimization and resource management in cloud computing systems. Because of its complexity, it has been defined as an NP problem. We introduce an online scheme to solve the problem of task scheduling under a dynamic load in the cloud environment. After analyzing the process, we propose a server level agreement constraint adaptive online task scheduling algorithm based on double deep Q-learning (SLA-DQTS) to reduce the makespan, cost, and average overdue time under the constraints of virtual machine (VM) resources and deadlines. In the algorithm, we prevent the change of the model input dimension with the number of VMs by taking the Gaussian distribution of related parameters as a part of the state space. Through the design of the reward function, the model can be optimized for different goals and task loads. We evaluate the performance of the algorithm by comparing it with three heuristic algorithms (Min-Min, random, and round robin) under different loads. The results show that the algorithm in this paper can achieve similar or better results than the comparison algorithms at a lower cost.

The Oil and Gas Pipeline SCADA System Based on Cloud Computing and the Scheduling Algorithm

2014 ◽  
Vol 1044-1045 ◽  
pp. 1406-1410
Author(s):  
Liu Miao ◽  
Man Cang Yuan ◽  
Cheng Qiang Wang ◽  
Rui Qiang Gao
Keyword(s):  
Cloud Computing ◽  
Resource Utilization ◽  
Supervisory Control ◽  
Oil And Gas ◽  
High Reliability ◽  
Scheduling Algorithm ◽  
Gas Pipeline ◽  
Application Framework ◽  
Time Data ◽  
Scada System

For improving the reliability and resource utilization of oil and gas pipeline Supervisory Control and Data Acquisition (SCADA) system, the application framework of oil and gas pipeline SCADA system based on cloud computing and a high reliability scheduling algorithm is proposed in the paper. Simulation results show the SCADA system based on cloud computing with new scheduling algorithm can realize history data backup in many computers and real-time data processed with primary-backup copy. So, the new application framework and new scheduling algorithm can provide better reliability and improve the resource utilization of system.

scholarly journals Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

2021 ◽  
Author(s):  
Poria Pirozmand ◽  
Ali Asghar Rahmani Hosseinabadi ◽  
Maedeh Farrokhzad ◽  
Mehdi Sadeghilalimi ◽  
Seyedsaeid Mirkamali ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Scheduling Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Computing System ◽  
Computing Systems ◽  
Cloud Computing System ◽  
Scheduling Heuristic ◽  
The Cost

AbstractThe cloud computing systems are sorts of shared collateral structure which has been in demand from its inception. In these systems, clients are able to access existing services based on their needs and without knowing where the service is located and how it is delivered, and only pay for the service used. Like other systems, there are challenges in the cloud computing system. Because of a wide array of clients and the variety of services available in this system, it can be said that the issue of scheduling and, of course, energy consumption is essential challenge of this system. Therefore, it should be properly provided to users, which minimizes both the cost of the provider and consumer and the energy consumption, and this requires the use of an optimal scheduling algorithm. In this paper, we present a two-step hybrid method for scheduling tasks aware of energy and time called Genetic Algorithm and Energy-Conscious Scheduling Heuristic based on the Genetic Algorithm. The first step involves prioritizing tasks, and the second step consists of assigning tasks to the processor. We prioritized tasks and generated primary chromosomes, and used the Energy-Conscious Scheduling Heuristic model, which is an energy-conscious model, to assign tasks to the processor. As the simulation results show, these results demonstrate that the proposed algorithm has been able to outperform other methods.

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