Support vector regression for link load prediction

2009 ◽  
Vol 53 (2) ◽  
pp. 191-201 ◽  
Author(s):  
Paola Bermolen ◽  
Dario Rossi
Keyword(s):  
Support Vector Regression ◽  
Support Vector ◽  
Load 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.

Load prediction using support vector regression

2017 ◽  
Author(s):  
Lee Wai Chong ◽  
Divish Rengasamy ◽  
Yee Wan Wong ◽  
Rajprasad Kumar Rajkumar
Keyword(s):  
Support Vector Regression ◽  
Support Vector ◽  
Load Prediction

Energy-aware VM migration using dragonfly–crow optimization and support vector regression model in Cloud

2018 ◽  
Vol 09 (06) ◽  
pp. 1850050 ◽  
Author(s):  
Nitin S. More ◽  
Rajesh B. Ingle
Keyword(s):  
Energy Consumption ◽  
Support Vector Regression ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Support Vector ◽  
Load Prediction ◽  
Energy Aware ◽  
Support Vector Regression Model ◽  
Migration Cost ◽  
And Migration

Nowadays, virtual machine migration (VMM) is a trending research since it helps in balancing the load of the Cloud effectively. Several VMM-based strategies defined in the literature have considered various metrics, such as load, energy, and migration cost for balancing the load of the model. This paper introduces a novel VMM strategy by considering the load of the Cloud network. Two important aspects of the proposed scheme are the load prediction through the support vector regression (SVR) and the optimal VM placement through the proposed dragonfly-based crow (D-Crow) optimization algorithm. The proposed D-Crow optimization algorithm is developed by incorporating crow search algorithm (CSA) into dragonfly algorithm (DA). Also, the proposed VMM strategy defines a load balancing model based on the energy consumption, load, and the migration cost to achieve the energy-aware VMM. The simulation of the proposed VMM strategy is done based on the metrics such as load, energy consumption, and the migration cost. From the results, it can be shown that the proposed VMM strategy surpassed other comparative models by achieving the minimum values of 7.3719%, 10.0368%, and 11.0639% for the load, energy consumption, and migration cost, respectively.

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