Abstract
Monitoring what application or type of applications running on a computer or a cluster without violating the privacy of the users can be challenging, especially when we may not have operator access to these devices, or specialized software. Smart grids and Internet of things (IoT) devices can provide power consumption data of connected individual devices or groups. This research will attempt to provide insides on what applications are running based on the power consumption of the machines and clusters. It is therefore assumed that there is a correlation between electric power and what software application is running. Additionally, it is believed that it is possible to create power consumption profiles for various software applications and even normal and abnormal behavior (e.g., a virus). In order to achieve this, an experiment was organized for the purpose of collecting 48 h of continuous real power consumption data from two PCs that were part of a university computer lab. That included collecting data with a one-second sample period, during class as well as idle time from each machine and their cluster. During the second half of the recording period, one of the machines was infected with a custom-made virus, allowing comparison between power consumption data before and after infection. The data were analyzed using different approaches: descriptive analysis, F-Test of two samples of variance, two-way analysis of variance (ANOVA) and autoregressive integrated moving average (ARIMA). The results show that it is possible to detect what type of application is running and if an individual machine or its cluster are infected. Additionally, we can conclude if the lab is used or not, making this research an ideal management tool for administrators.
Highlights
Unwanted applications cause many hurdles on accessible computers in the lab.Some common hurdles, such as slowness of the computers and security risks are caused due to unwanted background processing
Smart grids have a number of advantages such as unit/cluster power consumption monitoring, automatic control by including hardware and software monitoring and controlled applications [2]
In order to answer the research questions presented in the introduction, we generated our own corresponding power consumption
Summary
Unwanted applications cause many hurdles on accessible computers in the lab.Some common hurdles, such as slowness of the computers and security risks are caused due to unwanted background processing. Unwanted applications cause many hurdles on accessible computers in the lab. Sometimes the machines maybe used remotely or at certain times (e.g., while a lab is closed) run applications that are not supposed to. It is common for users to use the computational power of company machines for personal benefits at the expense of their organization (e.g., cryptocurrency mining, hosting applications, etc.). Organizations have full access to their smart power grid and they have every right to monitor the power consumption for the various computer/clusters or even rooms [1]. Smart grids have a number of advantages such as unit/cluster power consumption monitoring, automatic control by including hardware and software monitoring and controlled applications [2]. It is possible to monitor the power consumption in real time [3]
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