Research on area comprehensive utilization rate based on entropy weight and gray comprehensive relational analysis method

Zeng, Yuan; Guo, Waiying; Zhang, Fengbin; Qian, Yuping; Hu, Bo; Han, Xinyang; Nan, Kaihui

doi:10.1109/tencon.2017.8228217

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Where w jk is the weight coefficient, which is calculated from the historical samples of global idle rate of node resources by entropy weight method [8]. The process of weight calculation of entropy weight method has gone through three steps: standardization of metrics, calculation of information entropy and calculation of metric weight.…”

Section: A Basic Scoring Algorithmmentioning

confidence: 99%

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Research on Task Collaborative Scheduling Algorithm for Container Cloud

2021

Proceedings of 2021 the 11th International Workshop on Computer Science and Engineering

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The early task scheduling algorithm generally completes the static scheduling action from the perspective of the inherent resource usage. This way also lacks the feedback regulation of the running environment, and it is easy to fall into local optimum and cannot achieve the best convergence. To solve this problem, a rule-based collaborative scheduling algorithm for container cloud (CSC2T) is proposed. Firstly, we construct the scheduling rules according to the relationship between nodes and tasks, and describe the problem of finding the best node for scheduling. Then, the nodes are divided into task-related nodes and taskirrelated nodes according to their states. For task-related nodes, the correlation coefficient of twin services and dependent services are described by AHP model. For the task-irrelated nodes, pearson similarity model is used to describe the correlation coefficient of the nodes with twin instances. Combined with two scoring methods, the forward scheduling calculation based on multi-objective is realized. Finally, four kinds of indicators are selected as the feedback evaluation criteria to update the parameters. The experimental part verifies the feasibility of the algorithm from different angles such as resource imbalance degree, resource utilization rate, replica scheduling imbalance degree and dependency scheduling imbalance degree, and improves the overall efficiency of task scheduling in container cloud.

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Section: A Basic Scoring Algorithmmentioning

confidence: 99%

“…The related details are referred to Ref. [8]. w jk can reflect the usage of node resources in real time, and then play the purpose of dynamic balancing for the cluster resources.…”

Section: A Basic Scoring Algorithmmentioning

confidence: 99%