Efficient scheme for load balancing on heterogeneous Biswapped Networks

This paper develops a multi-physics interface code MC-FLUENT to couple the Monte Carlo code OpenMC with the commercial computational fluid dynamics code ANSYS FLUENT. The implementations and parallel performances of block Gauss–Seidel-type and block Jacobi-type Picard iterative algorithms have been investigated. In addition, this paper introduces two adaptive load-balancing algorithms into the neutronics and thermal-hydraulics coupled simulation to reduce the time cost of computation. Considering that the different scalability of OpenMC and FLUENT limits the performance of block Gauss–Seidel algorithm, an adaptive load-balancing algorithm that can increase the number of nodes dynamically is proposed to improve its efficiency. Moreover, with the natural parallelism of block Jacobi algorithm, another adaptive load-balancing algorithm is proposed to improve its performance. A 3 x 3 PWR fuel pin model and a 1000 MWt ABR metallic benchmark core were used to compare the performances of the two algorithms and verify the effectiveness of the two adaptive load-balancing algorithms. The results show that the adaptive load-balancing algorithms proposed in this paper can greatly improve the computing efficiency of block Jacobi algorithm and improve the performance of block Gauss–Seidel algorithm when the number of nodes is large. In addition, the adaptive load-balancing algorithms are especially effective when a case demands different computational power of OpenMC and FLUENT.

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Efficient Load Balancing Using Active Replica Management in a Storage System

Mathematical Problems in Engineering ◽

10.1155/2016/4751829 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9

Author(s):

Jui-Pin Yang

Keyword(s):

Load Balancing ◽

Storage System ◽

Data Access ◽

Optimal Number ◽

Load Imbalance ◽

Dynamic Replication ◽

Optimal Load ◽

Replica Management ◽

Replication Scheme ◽

Efficient Scheme

Many algorithms can uniformly distribute data to storage nodes in a storage system. However, it cannot avoid load imbalance because data has different popularity. To resolve this issue, we propose a novel dynamic replication scheme, namely, Active Replica Management (ARM). ARM actively establishes optimal number of copies for hotspot data according to data access behaviors and then efficiently distributes copies to other storage nodes based on current amount of copies related to hotspot data. To improve storage utilization, ARM automatically and gradually dereplicates the useless copies of hotspot data when they become nonhotspot data. ARM resolves load imbalance by allocating dynamic copies to adequate storage nodes, and hence it can prevent partial storage nodes from overburdening. Simulation results demonstrate that ARM is an efficient scheme with excellent performance on load balancing, significantly closer to Optimal Load Balancing (OLB). In addition, ARM’s performance outperforms both Static Load Balancing (SLB) and No Replica schemes.

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Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset21841125 ◽

2018 ◽

pp. 96-101

Author(s):

Shailendra Raghuvanshi ◽

Priyanka Dubey

Keyword(s):

Load Balancing ◽

Task Scheduling ◽

Waiting Time ◽

Virtual Machines ◽

Scheduling Algorithm ◽

Cloud Environment ◽

Task Scheduling Algorithm ◽

Machine Utilization ◽

Independent Tasks ◽

Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

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