Efficient Computing Resource Metric Method in Computing-First Network

被引:0
|
作者
Chai R. [1 ]
Gao S. [1 ]
Lan J. [1 ]
Liu N. [1 ]
机构
[1] School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing
基金
中国国家自然科学基金;
关键词
computing resource metric; computing resources; computing-first network; hybrid metric method;
D O I
10.7544/issn1000-1239.202330003
中图分类号
学科分类号
摘要
With the continuous development of new network services and the increasing demand for computing, computing-first network (CFN) has attracted people’s attention and is gradually developing. As a method to measure the computing and storage capacity of various computing platforms, the computing resource metric plays an important role in achieving user awareness and efficient scheduling of computing resources in CFN. At present, the research on computing resource metrics is in its infancy. Most of those that only consider some static or dynamic indicators are relatively simple, which cannot guarantee the utilization of computing resources and the precision of matching computing resources. In this study, we design a hybrid metric method (HMM), which combines static and dynamic indicators to measure computing resources. This method takes the basic performance of the computing nodes and the dynamic changes in their working state into account. In addition, we also consider lots of static and dynamic indicators to enhance the comprehensiveness of HMM. The experiments and a large number of data analyses show that the metric method we propose has good improvement in the utilization of computing resources and the precision of matching computing resources. © 2023 Science Press. All rights reserved.
引用
收藏
页码:763 / 771
页数:8
相关论文
共 14 条
  • [1] Chu Qiao, Analysis of computing metric and computing resource scheduling idea[J], Communication Technology, 55, 9, (2022)
  • [2] Yukun Sun, Xing Zhang, Bo Lei, Research on intelligent arithmetic-aware routing allocation strategy in edge arithmetic networks[J], Radio Communication Technology, 48, 1, (2022)
  • [3] Yuhan Zhao, Zheng Chong, Xueying Han, Et al., Simulation study of routing mechanism in the computing-aware network[C], Proc of the 10th Int Conf on Networks, Communication and Computing, pp. 126-134, (2021)
  • [4] Qiyue Cheng, Structural entropy weighting of evaluation indicators[J], Systems Engineering Theory and Practice, 30, 7, (2010)
  • [5] Quinlan J R., C4. 5: Programs for Machine Learning, (2014)
  • [6] Carbo-Dorca R, Besalu E., Geometry of n-dimensional Euclidean space Gaussian enfoldments[J], Journal of Mathematical Chemistry, 49, 10, pp. 2244-2249, (2011)
  • [7] Emma P G., Understanding some simple processor-performance limits[J], IBM Journal of Research and Development, 41, 3, pp. 215-232, (1997)
  • [8] Shekofteh S K, Noori H, Naghibzadeh M, Et al., Metric selection for GPU kernel classification[J], ACM Transactions on Architecture and Code Optimization, 15, 4, pp. 1-27, (2019)
  • [9] Haifeng Wang, Qingkui Chen, Energy consumption control model of GPU cluster with multi-index self-optimization[J], Journal of Computer Research and Development, 52, 1, (2015)
  • [10] Zou Qiang, An analytical performance and power model based on the transition probability for hard disks[C], Proc of the 3rd Int Conf on Awareness Science and Technology (iCAST), pp. 111-116, (2011)