Real-Time Prediction for Product Surface Roughness by Support Vector Regression

2021 ◽  
Vol 44 (3) ◽  
pp. 117-124
Author(s):  
Sujin Choi ◽  
◽  
Dongju Lee
Keyword(s):  
Surface Roughness ◽  
Support Vector Regression ◽  
Real Time ◽  
Support Vector ◽  
Time Prediction ◽  
Product Surface

Real-time prediction of order flowtimes using support vector regression

2008 ◽  
Vol 35 (11) ◽  
pp. 3489-3503 ◽  
Author(s):  
Abdulrahman Alenezi ◽  
Scott A. Moses ◽  
Theodore B. Trafalis
Keyword(s):  
Support Vector Regression ◽  
Real Time ◽  
Support Vector ◽  
Time Prediction

scholarly journals Stream Data Load Prediction for Resource Scaling Using Online Support Vector Regression

Algorithms ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 37 ◽  
Author(s):  
Zhigang Hu ◽  
Hui Kang ◽  
Meiguang Zheng
Keyword(s):  
Support Vector Regression ◽  
Real Time ◽  
Virtual Machines ◽  
Time Window ◽  
Performance Model ◽  
Streaming Data ◽  
Support Vector ◽  
Load Prediction ◽  
Stream Data ◽  
Online Support Vector Regression

A distributed data stream processing system handles real-time, changeable and sudden streaming data load. Its elastic resource allocation has become a fundamental and challenging problem with a fixed strategy that will result in waste of resources or a reduction in QoS (quality of service). Spark Streaming as an emerging system has been developed to process real time stream data analytics by using micro-batch approach. In this paper, first, we propose an improved SVR (support vector regression) based stream data load prediction scheme. Then, we design a spark-based maximum sustainable throughput of time window (MSTW) performance model to find the optimized number of virtual machines. Finally, we present a resource scaling algorithm TWRES (time window resource elasticity scaling algorithm) with MSTW constraint and streaming data load prediction. The evaluation results show that TWRES could improve resource utilization and mitigate SLA (service level agreement) violation.

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