Application of Fuzzy Logic for Problems of Evaluating States of a Computing System

The monitoring utilization and workloads of computer hardware components, such as CPU, RAM, bus, and storage, are an ideal way to evaluate the effectiveness of these components. In this paper, we surveyed the basic concepts, characteristics, and parameters of computer systems that determine system performance, and the types of models that provide adequate modeling of these systems. We investigated and developed the applied aspects of the theory of fuzzy sets’ principles and the Matlab environment tools for monitoring and evaluating the state of computing systems. The idea of the paper is to identify the state of the computer infrastructure by using the models of Mamdani and Sugeno FIS (fuzzy inference system) to evaluate the impact of RAM and storage on CPU performance. With this approach, we observed the behavior of computer infrastructure. The results are useful for understanding performance issues with regard to specific bottlenecks and determining the correlation of performance counters. Moreover, the model presents linguistic results. Hereafter, performance counter correlations will support the development of algorithms that can detect whether the performance of a given computer will be affected by a reasonable priority. The performance assertions derived from these approaches allow resource management policies to prevent performance degradation, and as a result, the infrastructure will be able to serve safely as expected. These methods can be applied across the entire spectrum of computer systems, from personal computers to large mainframes and supercomputers, including both centralized and distributed systems. We look forward to their continued use, as well as their improvement when it is necessary to evaluate future systems.

[1]  Muhammad Akram,et al.  A novel fuzzy decision-making system for CPU scheduling algorithm , 2015, Neural Computing and Applications.

[2]  Enda Barrett,et al.  Predicting host CPU utilization in the cloud using evolutionary neural networks , 2018, Future Gener. Comput. Syst..

[3]  Niv Ahituv,et al.  A model for predicting and evaluating computer resource consumption , 1988, CACM.

[4]  A. Gilles,et al.  The Art of Computer Systems Performance Analysis (Techniques for Experimental Design, Measurement, Simulation, and Modeling) , 1992 .

[5]  Najoua Dridi,et al.  Automated Forecasting Approach Minimizing Prediction Errors of CPU Availability in Distributed Computing Systems , 2016 .

[6]  Enda Barrett,et al.  Predicting host CPU utilization in cloud computing using recurrent neural networks , 2017, 2017 12th International Conference for Internet Technology and Secured Transactions (ICITST).

[7]  Aïcha Mokhtari,et al.  Mixture of ANFIS systems for CPU load prediction in metacomputing environment , 2010, Future Gener. Comput. Syst..

[8]  Lefteris Angelis,et al.  Synthetic Metrics for Evaluating Runtime Quality of Software Architectures with Complex Tradeoffs , 2009, 2009 35th Euromicro Conference on Software Engineering and Advanced Applications.

[9]  Vincent Wertz,et al.  Fuzzy Logic, Identification and Predictive Control , 2004 .

[10]  Yi Lin,et al.  Uncertain Fuzzy Preference Relations and Their Applications , 2012, Studies in Fuzziness and Soft Computing.

[11]  Mitsuo Gen,et al.  PERFORMANCE EVALUATION OF COMPUTER SYSTEM WITH FAILURE BASED ON FUZZY SET THEORY , 1995 .

[12]  Sasu Tarkoma,et al.  I/O Is Faster Than the CPU: Let's Partition Resources and Eliminate (Most) OS Abstractions , 2019, HotOS.

[13]  Olivier Barais,et al.  Using Quantile Regression for Reclaiming Unused Cloud Resources While Achieving SLA , 2018, 2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom).

[14]  Jitendra Kumar,et al.  Long Short Term Memory Recurrent Neural Network (LSTM-RNN) Based Workload Forecasting Model For Cloud Datacenters , 2018 .

[15]  Dror G. Feitelson,et al.  Workload Modeling for Computer Systems Performance Evaluation , 2015 .

[16]  T. Ross.  Fuzzy Logic with Engineering Applications , 1994 .

[17]  Aïcha Mokhtari,et al.  CPU load prediction using neuro-fuzzy and Bayesian inferences , 2011, Neurocomputing.

[18]  Kyoung-Don Kang,et al.  Adaptive Fuzzy Control for Utilization Management , 2008, 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC).

[19]  Shatha Jawad.  Design and evaluation of a neurofuzzy CPU scheduling algorithm , 2014, Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control.

[20]  Seth Copen Goldstein,et al.  Factors Influencing the Performance of a CPU-RFU Hybrid Architecture , 2002, FPL.

[21]  Parijat Dube,et al.  Exploiting Resource Usage Patterns for Better Utilization Prediction , 2011, 2011 31st International Conference on Distributed Computing Systems Workshops.

[22]  Zhijia Chen,et al.  Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network , 2015, Comput. Intell. Neurosci..