算力网络中面向计算重用的任务调度优化

doi:10.11959/j.issn.1000-436x.2023206

Abstract

Abstract:

To cope with the challenges posed by the future explosive growth in computing power demand, computing reuse technology was introduced into the computing power network to reduce service latency and computational resource consumption by reusing the results of computational tasks.Based on this, a service federation-based context-aware online learning algorithm was proposed.First, the reuse index was designed to reduce the extra lookup latency.Then, online learning was performed based on the service federation mechanism to make computational task scheduling decisions according to contextual information and historical experience.The experimental results show that the proposed algorithm outperforms the baseline algorithms in terms of service latency and computational resource consumption.

Key words: computing power network, computing reuse, task scheduling, online learning

CLC Number:

TP393

Yunxiao MA, Zhonghui WU, Zuyun XU, Lujie ZHONG, Changqiao XU. Optimization of task scheduling for computing reuse in computing power network[J]. Journal on Communications, 2023, 44(11): 129-142.

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References 30

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参数	值
算力节点传输功率/mW	100
噪声功率/dBm	-100
算力节点带宽资源/MHz	[10,20]
子任务的数据量/MB	[0.3,0.5]
子任务的计算需求/GHz	[0.5,0.6]
算力节点计算资源/GHz	[1,10]
查找时延/s	0.05

Optimization of task scheduling for computing reuse in computing power network

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