scholarly journals Dynamic Resource Scheduling for C2 Organizations Based on Multi-Objective Optimization

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 64614-64626 ◽  
Author(s):  
Xun Wang ◽  
Peiyang Yao ◽  
Jieyong Zhang ◽  
Wan Lujun ◽  
Zhiqiang Jiao
Keyword(s):  
Resource Scheduling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Dynamic Resource

scholarly journals Hybrid Algorithm using the Advantage of Krill Herd Algorithm with Opposition- Based Learning for Dynamic Resource Allocation in Cloud Environment

2019 ◽  
Vol 8 (6S) ◽  
pp. 306-311
Keyword(s):  
Resource Allocation ◽  
Optimization Algorithm ◽  
Computational Cost ◽  
Dynamic Resource Allocation ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Opposition Based Learning ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Dynamic Resource

The cloud computing systems have more consideration due to the growing control for elevated concert computing and data storage. Resource allocation plays a vital role in cloud systems. To overcome the obscurity present in resources allocation system. In this paper, we design and develop a technique for dynamic resource allocation. A Hybridized approach is designed with the help of multi-objective oppositional krill herd optimization algorithm (OKHA). It is a combination of the krill herd algorithm and Opposition-based learning (OBL), OBL is added to get enhanced performance of the krill herd algorithm. The objective of this hybridization is to reduce the cost. In this Hybridized process each task consists of two cost i.e monetary cost and computational cost. Here each task is divided into many subtasks and assigns the respective resources to it. Our proposed multi-objective optimization algorithm will decide allocation of resource for the each subtask in this process. Finally, the testing is passed out, we evaluate our proposed algorithm with PSO, and GA algorithm we verified the performance levels of our proposed Multi-objective optimization algorithm.

scholarly journals Joint Optimization of Energy Conservation and Migration Cost for Complex Systems in Edge Computing

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Xiaolong Xu ◽  
Yuan Xue ◽  
Mengmeng Cui ◽  
Yuan Yuan ◽  
Lianyong Qi
Keyword(s):  
Energy Consumption ◽  
Complex Systems ◽  
Optimization Problem ◽  
Renewable Energy Sources ◽  
Resource Scheduling ◽  
Multi Objective Optimization ◽  
Vm Migration ◽  
Multi Objective ◽  
Migration Cost ◽  
Scheduling Method

By means of the complex systems, multiple renewable energy sources are integrated to provide energy supply for users. Considering that there are massive services needed to process in complex systems, the mobile services are offloaded from mobile devices to edge servers for efficient implementation. In spite of the benefits of complex systems and edge servers, massive resource requirements for implementing the increasing resource requests decrease the execution efficiency and affect the whole resource usage of edge servers. Therefore, it remains an issue to achieve dynamic scheduling of the computing resources across edge servers. With the consideration of this issue, a Balanced Resource Scheduling Method, named BRSM, for trade-offs between virtual machine (VM) migration cost and energy consumption of VM migrations for edge server management, named BRSM, is designed in this paper. Technically, we analyze the load conditions of edge servers and formulate the energy consumption of VM migrations and VM migration cost as a multi-objective optimization problem. Then, we propose a dynamic resource scheduling method for WMAN to deal with the multi-objective optimization problem. In addition, nondominated sorting genetic algorithm III (NSGA-III) is adopted to generate optimal resource scheduling strategies. Finally, we conduct experiment simulations to testify the efficiency of the proposed method BRSM.

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