HaiFeng Lv
ABSTRACT
At present, the resource scheduling and allocation of cloud data center usually adopts the static scheduling method. After the scheduling is completed, the resource allocation will not change for a long time. However, with the expansion and continuous use of cloud data centers, there will be serious resource imbalance. To solve this problem, this paper proposes a cloud data center resource dynamic scheduling algorithm to achieve a balanced load of resources on the computing nodes of the data center. The algorithm monitors the computing resources, selects the physical nodes with large deviation from the average load value according to the monitoring results, and scores them according to the use of virtual resources. According to the scoring results, the appropriate virtual resources are selected for dynamic balancing, so that the load value of the physical node is stable near the average load value. The simulation results show that the algorithm can schedule resources dynamically periodically and realize the load balancing of cloud data center, which is effective and stable.
- ZHAO Tangjun.Research on the Application of BIM Technology in Building Construction Management based on Virtual Reality Environment— Take “Shanghai Keppel Jing'an Center” Project as an Example[J].Housing Science,2021,41(11):59-63.DOI:10.13626/j.cnki.hs.2021.11.012Google Scholar
- HAO Jiwei.Research on the Improvement of National Strategic Material Reserve Management Under the Background of Big Data[J].Journal of Luoyang Institute of Science and Technology(Social Science Edition),2021,36(06):37-41Google Scholar
- HAO Jiwei.Application and Development Trend of Geospatial Informatics in the Era of Internet of Things[J].Journal of Geomatics,2020,45(05):78-83Google Scholar
- Huang H J , Kau L H , Wang H S , Large-scale data analysis of PECVD amorphous silicon interface passivation layer via the optical emission spectra for parameterized PCA[J]. The International Journal of Advanced Manufacturing Technology, 2019, 101(1-4).Google Scholar
- Sinthong P , Carey M J . AFrame: Extending DataFrames for Large-Scale Modern Data Analysis (Extended Version)[J]. 2019Google Scholar
- Kurashige H , Kaneko J , Yamashita Y , Revealing Relationships Among Cognitive Functions Using Functional Connectivity and a Large-Scale Meta-Analysis Database[J]. Frontiers in Human Neuroscience, 2020, 13:457.Google ScholarCross Ref
- Liu D , Li J , Du B , A hybrid neural network approach to combine textual information and rating information for item recommendation[J]. Knowledge and Information Systems, 2020:1-26.Google Scholar
- Hadikhani P , Hadikhani P . An adaptive hybrid algorithm for social networks to choose groups with independent members[J]. Evolutionary Intelligence, 2020, 13(4)Google Scholar
- Mirobi G J , Arockiam L . Dynamic Load Balancing Approach for Minimizing the Response Time Using An Enhanced Throttled Load Balancer in Cloud Computing[C]// 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2020.Google Scholar
- Rajendrakumar S , Parvati V K , Rajashekarappa, Automation of irrigation system through embedded computing technology[C]// the 3rd International Conference. 2019.Google Scholar
- Wu S , Zheng L , Hu W , Improved Deep Belief Network and Model Interpretation Method for Power System Transient Stability Assessment[J]. Journal of Modern Power Systems and Clean Energy, 2020.Google Scholar
- Mahendravarman I , SA Elankurisil, Venkateshkumar M , Artificial intelligent controller-based power quality improvement for microgrid integration of photovoltaic system using new cascade multilevel inverter[J]. Soft Computing, 2020:1-18.Google Scholar
- Malarvizhi N , Priyatharsini G S , Koteeswaran S . Cloud Resource Scheduling Optimal Hypervisor (CRSOH) for Dynamic Cloud Computing Environment[J]. Wireless Personal Communications, 2020(1).Google Scholar
- Zhou G ,Tian W ,Buyya R . Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions[J]. 2021.Google Scholar
- Zhang Y , Du P , Wang J , Resource Scheduling for Delay Minimization in Multi-Server Cellular Edge Computing Systems[J]. IEEE Access, 2019, 7(99):86265-86273.Google ScholarCross Ref
Recommendations
Towards a multi-QoS human-centric cloud computing load balance resource allocation method
In the large-scale clustering resource pool as human-centric cloud computing, peer load balance not only improves overall system efficiency, but also saves energy. As various factors should be considered in resource scheduling and each has different ...
Load balancing for data centre: a brief survey
Cloud computing is an emergent technology in computer science. To provide high performance for servers, how the resources are scheduled is an important issue in cloud computing. To solve this problem, many load-balancing resource scheduling strategies ...
Cloud resource provisioning: survey, status and future research directions
Cloud resource provisioning is a challenging job that may be compromised due to unavailability of the expected resources. Quality of Service (QoS) requirements of workloads derives the provisioning of appropriate resources to cloud workloads. Discovery ...
Comments