Journal of Jilin University(Engineering and Technology Edition) ›› 2020, Vol. 50 ›› Issue (5): 1832-1843.doi: 10.13229/j.cnki.jdxbgxb20190042

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Cost⁃effective elastic resource allocation strategy in large⁃scale streaming data processing

Li-na LI1,2(),Xiao-hui WEI1,3,Lin-lin HAO1,Xing-wang WANG1(),Chu WANG1   

  1. 1.College of Computer Science and Technology，Jilin University，Changchun 130012，China
    2.College of Computer Science and Technology，Changchun University，Changchun 130022，China
    3.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education，Jilin University，Changchun 130012，China
  • Received:2019-01-11 Online:2020-09-01 Published:2020-09-16
  • Contact: Xing-wang WANG E-mail:linal11@mails.jlu.edu.cn;xww@jlu.edu.cn

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Abstract:

In large-scale stream processing, resource reconfiguration in strict synchronization with changes in the stream data can guarantee the performance of application, however the resource reconfiguration cost of the system is relatively higher. Based on the idea of resource reservation and combining with the load forecasting model, this paper designs two proactive elastic resource allocation strategies from heuristic and intelligent perspective respectively, which aim to minimize the application reconfiguration cost and maximize the resource utilization. Both strategies calculate the latency of the application in the pipelined processing mode, and probabilistically guarantee the constraint of the application latency performance. Among them, the heuristic strategy uses the local threshold method for resource allocation, and the intelligent strategy adaptively allocates resources based on the reinforcement learning technology. Experimental results show that compared with the reactive strategies, these two proactive resource allocation strategies proposed in this paper both have low reconfiguration cost and high resource utilization, and the performance of the intelligent strategy is better, especially when the change of sudden load is relatively stable.

Key words: computer architecture, stream data processing, resource allocation, elastic adjustment, reinforcement learning

CLC Number: 

  • TP391

Table 1

Initial state of lookup table"

延迟阈值百分比区间收缩不变扩展
（0%，10%）1.000.500.40
[10%，20%）0.950.600.45
[20%，30%）0.900.660.50
[30%，40%）0.800.750.55
[40%，50%）0.750.800.65
[50%，60%）0.700.850.72
[60%，70%）0.650.880.76
[70%，80%）0.600.900.80
[80%，90%）0.500.950.84
[90%，100%）0.450.800.90
[100%，+∞）0.400.601.00

Fig.1

Trend at different random loads"

Fig.2

Latency violation rate at different loads"

Fig.3

Reconfiguration cost (number of VMs) at different loads"

Fig.4

Reconfiguration cost (number of adjustments) at different loads"

Fig.5

Reconfiguration cost (VM number variance) at different loads"

Fig.6

Resource utilization at different loads"

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