Authors:
Ruksar Shaikh
;
Cristina Muntean
and
Shaguna Gupta
Affiliation:
School of Computing, National College of Ireland, Dublin, Ireland
Keyword(s):
Cloud Computing, Machine Learning, Deep Learning, Resource Utilization, CPU Utilization, Network Transmission Throughput.
Abstract:
In today’s modern computing infrastructure, cloud computing has emerged as a pivotal paradigm that offers scalability and flexibility to satisfy the demands of a wide variety of specific applications. Maintaining optimal performance and cost-effectiveness inside cloud settings continues to be a significant problem and one of the most important challenges is efficient resource utilisation. A resource utilization prediction system is required to aid the resource allocator in providing optimal resource allocation. Accurate prediction is difficult in such a dynamic resource utilisation. The applications of machine learning techniques are the primary emphasis of this research project which aims to predict resource utilisation in cloud computing systems. The dataset GWA-T-12 Bitbrains have provided the data of timestamp, cpu usage, network transmitted throughput and Microsoft Azure traces has provided the data of cpu usage of a cloud server. To predict VM workloads based on CPU utilization
, machine learning models such as Linear Regression, Decision Tree Regression, Gradient Boosting Regression, and Support Vector Regression are used. In addition to these, deep learning models such as Long Short-Term Memory and Bi-directional Long Short-Term Memory have also been evaluated in our approach. Bi-directional Long Short Term Memory approach is considered more effective as compared to other models in terms of CPU Utilisation and Network Transmitted Throughput as its R2 score is close to 1 and hence can produce more accurate results.
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