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A Load Balancing Algorithm Based on Fair Scheduler
Shengjun Xue,Zhengwei Chen,Jingyi Chen 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.10
With Hadoop been widely used, job scheduling technology, as a key technology in Hadoop, has been developed rapidly. However, Hadoop's default scheduling algorithm Fair Scheduler when performing the task scheduler does not consider load balancing status of each node cluster system, leading to low efficiency great job. For defect fair share scheduling algorithm, and combined LATE scheduling algorithm, load balancing scheduling algorithm based on a fair share. Experimental results show that the fair share scheduling algorithm can be improved in the scheduling task when taking into account the situation of each node load balancing, improve the efficiency of large jobs.
Shengjun Xue,Jingyi Chen,Mengying Li 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.11
With the exponential increase of the cloud business volume, Data center occurs load imbalance caused by some physical machine inefficiency due to the diversity of users requirements. Therefore the cloud datacenter need an appropriate algorithm to balance the PMs load and ensure the resource utilization in the cloud datacenter. The paper defines and formulates the problem parameters and proposes a Multi-objective Discrete Particle Swarm Optimization (MDPSO) to schedule the resources to the VMs requests according to the requirements. The simulation shows that the MDPSO algorithm not only guarantees the resource utilization, but also insures the PMs Load balance.
An Improved Multi-Objective Optimization Algorithm Based on NPGA for Cloud Task Scheduling
Peng Yue,Xue Shengjun,Li Mengying 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.4
As a commercial distributed computing mode, cloud computing needs to meet the quality of service (QoS) requirement of users, which is its top priority. However, cloud computing service providers also need to consider how to reduce the overhead of data center, and keep load balancing is one of the key points to maximize the use of the resource in the data center. In this paper, we propose an improved multi-objective niched Pareto genetic algorithm (NPGA) to take load balancing into consideration without affecting performance of time consumption and financial cost of handling the user’s cloud computing tasks by presenting the load balancing shift mutation operator. The simulation results and analysis show that the proposed algorithm performs better than NPGA in maintaining the diversity and the distribution of the Pareto-optimal solutions in the cloud tasks scheduling under the same population size and evolution generation.