Decomposition Based Cloud Resource Demand Prediction Using Extreme Learning Machines
- PDF / 780,975 Bytes
- 19 Pages / 439.37 x 666.142 pts Page_size
- 2 Downloads / 202 Views
Decomposition Based Cloud Resource Demand Prediction Using Extreme Learning Machines Jitendra Kumar1 · Ashutosh Kumar Singh2 Received: 23 October 2019 / Revised: 9 July 2020 / Accepted: 15 July 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Cloud computing has drastically transformed the means of computing in past few years. Apart from numerous advantages, it suffers with a number of issues including resource under-utilization, load balancing and power consumption. The workload prediction is being widely explored to solve these issues using time series analysis regression and neural networks based models. The time series analysis based models are unable to capture the dynamics in the workload behavior whereas neural network based models offer better accuracy on the cost of high training time. This paper presents a workload prediction model based on extreme learning machines (ELM) whose learning time is very low and forecasts the workload more accurately. The performance of the model is evaluated over two real world cloud server workloads i.e. CPU and Memory demand traces of Google cluster and compared with predictive models based on state-of-art techniques including Auto Regressive Integrated Moving Average (ARIMA), Support Vector Regression (SVR), Linear Regression (LR), Differential Evolution (DE), Blackhole Algorithm (BhA), and Propagation (BP). It is observed that the proposed model outperforms the state-of-art techniques by reducing the mean prediction error up to 100% and 99% on CPU and memory request traces respectively. Keywords Workload forecasting · Google cluster trace · Neural network · Statistical analysis
* Jitendra Kumar [email protected] Ashutosh Kumar Singh [email protected] 1
Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Tamilnadu, India
2
Department of Computer Applications, National Institute of Technology, Kurukshetra, Haryana, India
13
Vol.:(0123456789)
Journal of Network and Systems Management
1 Introduction With the evolution of cloud computing, resources like processing, memory, and bandwidth are available on-demand over the Internet. The cloud services are enabled with several features such as scalability, mobility, flexibility, elasticity, robustness, and disaster recovery. Elasticity has become a critical feature as it allows an application to scale the resources as per its requirements anytime in its lifespan [1, 2]. However, the rapid changes in the resource demands may force an application to move from one physical machine to another. The resource utilization of a cloud system drops down if the resources are not provisioned efficiently. For instance, IBM observed 17.76% and 77.93% usage of CPU and memory in one of its studies [3]. Similarly, the CPU and memory utilization of Google cluster trance did not exceed 60% and 50% respectively [4]. The electricity consumption grows as the resource utilization drops down due to the fact that more servers will be running than required. The electr
Data Loading...
