Faculty Publications

Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736

Publications by NITK Faculty

Browse

Has files: NoSubject: search.filters.subject.Performance Degradation

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    The effect of software aging on power usage
    (Institute of Electrical and Electronics Engineers Inc., 2015) Mohan, R.; Guddeti, G.
    This paper tries to establish relation between the power usage and software aging. Software aging is the performance degradation of long running software due to shrinking in physical memory, increase in swap read and write rate and increase in CPU utilization. This paper tries to establish the relation between the Software aging and the power usage. Experimental results demonstrate that CPU utilization increases over a period of time, when the work load remains the constant. Linear Regression analysis is used for establishing this trend. © 2015 IEEE.
  • No Thumbnail Available
    Item
    Analysis of free physical memory in server virtualized system
    (Institute of Electrical and Electronics Engineers Inc., 2015) Mohan, R.; Guddeti, G.
    Degradation of the performance is the part of any long running software systems. This is due to memory leakage, unreleased file descriptors, round off errors and disk and memory fragmentation. It has been found that the memory leakage is the primary cause of any software performance degradation. In order to predict the software performance degradation, the analysis of the resource usage is essential. Here the free physical memory of a server virtualised system is analysed using time series analysis. © 2015 IEEE.
  • No Thumbnail Available
    Item
    Resource usage prediction based on ARIMA-ARCH model for virtualized server system
    (GEOMATE International Society geomate@gi-j.com, 2017) Mohan, B.R.; Guddeti, G.R.M.
    Performance degradation is unavoidable in server systems and this is because of factors such as shrinkage of system resources, data corruption, and numerical error accumulation. The resource shrinkage leads to the system failure due to the error propagation. Thus the resource prediction is useful to the administrator of the system so that an accidental outage can be avoided. It has been observed in past that most of the failures occur due to the exhaustion of free physical memory, so here free physical memory of a server consolidation setup is observed. It is also found that most of the studies in this direction were using the measurement-based approach with time series models for prediction. This paper reviews the effectiveness of such models and it examines whether volatility is present in the data or not. It checks whether Gauss-Markov assumptions about homoscedasticity holds good for the ordinary least square estimators of such models or not. This paper applies a combination of AutoRegressive Integrated Moving Average - AutoRegressive Conditional Heteroskedastic (ARIMA-ARCH) model to predict resource usage. Experimental results demonstrate that the goodness of fit of the ARIMA-ARCH Model has improved when compared to the linear ARIMA model. © Int. J. of GEOMATE.