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http://dx.doi.org/10.3837/tiis.2019.01.006

An Effective Multivariate Control Framework for Monitoring Cloud Systems Performance  

Hababeh, Ismail (German Jordanian University - Faculty of Electrical Engineering and Information Technology)
Thabain, Anton (German Jordanian University - Faculty of Electrical Engineering and Information Technology)
Alouneh, Sahel (German Jordanian University - Faculty of Electrical Engineering and Information Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.1, 2019 , pp. 86-109 More about this Journal
Abstract
Cloud computing systems' performance is still a central focus of research for determining optimal resource utilization. Running several existing benchmarks simultaneously serves to acquire performance information from specific cloud system resources. However, the complexity of monitoring the existing performance of computing systems is a challenge requiring an efficient and interactive user directing performance-monitoring system. In this paper, we propose an effective multivariate control framework for monitoring cloud systems performance. The proposed framework utilizes the hardware cloud systems performance metrics, collects and displays the performance measurements in terms of meaningful graphics, stores the graphical information in a database, and provides the data on-demand without requiring a third party software. We present performance metrics in terms of CPU usage, RAM availability, number of cloud active machines, and number of running processes on the selected machines that can be monitored at a high control level by either using a cloud service customer or a cloud service provider. The experimental results show that the proposed framework is reliable, scalable, precise, and thus outperforming its counterparts in the field of monitoring cloud performance.
Keywords
performance monitoring system; inter-process communication; machine data grid service; threading; performance object service; history data file;
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