A Study on the Estimation of Optimal ESS Capacity Considering Resource scheduling and commitment

Kim, Byungoh; Kwag, Kyuhyeong; Kim, Tae Hyun; Shin, Hyun-Suk; Kim, Wook

doi:10.5370/kiee.2021.70.3.447

Cited by 3 publications

(1 citation statement)

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“…After receiving the set of scheduling tasks, the scheduling allocation algorithm first evaluates the attributes of different subtasks in the task set, classifies and processes the subtasks in the task set by comparing the value of different subtasks, the time required for the task, and the size of the resources expected to be occupied by the task, and ranks the tasks according to their importance. By ranking the tasks, the tasks can be assigned to the best execution nodes according to the properties and characteristics of different tasks, thus ensuring the maximum utilization of heterogeneous cloud resources [2]. In addition, in the process of allocating heterogeneous cloud resources, the system also needs to monitor the allocated heterogeneous cloud resources in real time to ensure the even distribution of all resources, calculate the remaining resources to be allocated according to the current allocation of heterogeneous cloud resources, and reserve the execution nodes for the allocated resources in advance.…”

Section: Introductionmentioning

confidence: 99%

Research on collaborative and integrated resource schedulingalgorithm in heterogeneous cloud environment

Fu,

Liao,

Meng

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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The current conventional collaborative resource scheduling algorithms in heterogeneous cloud environments mainly process the allocation through the quantified results of data characteristics of heterogeneous cloud resources, which leads to low integrated scheduling efficiency due to the differences in the attributes of resources. In this regard, a collaborative and comprehensive resource scheduling algorithm in heterogeneous cloud environment is proposed. Firstly, the heterogeneous cloud resource information data is sampled and processed, and the resource quality is graded. The scheduling task model is constructed by constructing the mapping function of scheduling task assignment sub-nodes, and the hierarchical scheduling strategy is proposed by combining with ant colony algorithm. In the experiments, the designed collaborative integrated scheduling algorithm is tested for the scheduling efficiency. The final results can prove that the algorithm has a lower average delay and a more desirable integrated scheduling efficiency when the proposed method is used for scheduling heterogeneous cloud resources.

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Section: Introductionmentioning

confidence: 99%