Computer State Evaluation Using Adaptive Neuro-Fuzzy Inference Systems
Several crucial system design and deployment decisions, including workload management, sizing, capacity planning, and dynamic rule generation in dynamic systems such as computers, depend on predictive analysis of resource consumption. An analysis of the computer components’ utilizations and their wo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/23/9502 |
| _version_ | 1797462044545384448 |
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| author | Abror Buriboev Azamjon Muminov |
| author_facet | Abror Buriboev Azamjon Muminov |
| author_sort | Abror Buriboev |
| collection | DOAJ |
| description | Several crucial system design and deployment decisions, including workload management, sizing, capacity planning, and dynamic rule generation in dynamic systems such as computers, depend on predictive analysis of resource consumption. An analysis of the computer components’ utilizations and their workloads is the best way to assess the performance of the computer’s state. Especially, analyzing the particular or whole influence of components on another component gives more reliable information about the state of computer systems. There are many evaluation techniques proposed by researchers. The bulk of them have complicated metrics and parameters such as utilization, time, throughput, latency, delay, speed, frequency, and the percentage which are difficult to understand and use in the assessing process. According to these, we proposed a simplified evaluation method using components’ utilization in percentage scale and its linguistic values. The use of the adaptive neuro-fuzzy inference system (ANFIS) model and fuzzy set theory offers fantastic prospects to realize use impact analyses. The purpose of the study is to examine the usage impact of memory, cache, storage, and bus on CPU performance using the Sugeno type and Mamdani type ANFIS models to determine the state of the computer system. The suggested method is founded on keeping an eye on how computer parts behave. The developed method can be applied for all kinds of computing system, such as personal computers, mainframes, and supercomputers by considering that the inference engine of the proposed ANFIS model requires only its own behavior data of computers’ components and the number of inputs can be enriched according to the type of computer, for instance, in cloud computers’ case the added number of clients and network quality can be used as the input parameters. The models present linguistic and quantity results which are convenient to understand performance issues regarding specific bottlenecks and determining the relationship of components. |
| first_indexed | 2024-03-09T17:31:52Z |
| format | Article |
| id | doaj.art-5cfa262b6ea54bddb7eae66596953dfa |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T17:31:52Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5cfa262b6ea54bddb7eae66596953dfa2023-11-24T12:15:31ZengMDPI AGSensors1424-82202022-12-012223950210.3390/s22239502Computer State Evaluation Using Adaptive Neuro-Fuzzy Inference SystemsAbror Buriboev0Azamjon Muminov1Department of IT, Samarkand Branch of Tashkent University of Information Technologies, Samarkand 140100, UzbekistanDepartment of Computer Engineering, Gachon University, 7 Sujeong-gu, Seongnam-si 461-701, Republic of KoreaSeveral crucial system design and deployment decisions, including workload management, sizing, capacity planning, and dynamic rule generation in dynamic systems such as computers, depend on predictive analysis of resource consumption. An analysis of the computer components’ utilizations and their workloads is the best way to assess the performance of the computer’s state. Especially, analyzing the particular or whole influence of components on another component gives more reliable information about the state of computer systems. There are many evaluation techniques proposed by researchers. The bulk of them have complicated metrics and parameters such as utilization, time, throughput, latency, delay, speed, frequency, and the percentage which are difficult to understand and use in the assessing process. According to these, we proposed a simplified evaluation method using components’ utilization in percentage scale and its linguistic values. The use of the adaptive neuro-fuzzy inference system (ANFIS) model and fuzzy set theory offers fantastic prospects to realize use impact analyses. The purpose of the study is to examine the usage impact of memory, cache, storage, and bus on CPU performance using the Sugeno type and Mamdani type ANFIS models to determine the state of the computer system. The suggested method is founded on keeping an eye on how computer parts behave. The developed method can be applied for all kinds of computing system, such as personal computers, mainframes, and supercomputers by considering that the inference engine of the proposed ANFIS model requires only its own behavior data of computers’ components and the number of inputs can be enriched according to the type of computer, for instance, in cloud computers’ case the added number of clients and network quality can be used as the input parameters. The models present linguistic and quantity results which are convenient to understand performance issues regarding specific bottlenecks and determining the relationship of components.https://www.mdpi.com/1424-8220/22/23/9502Mamdani and Sugeno adaptive neuro-fuzzy inference systemCPU utilizationcomplex evaluation |
| spellingShingle | Abror Buriboev Azamjon Muminov Computer State Evaluation Using Adaptive Neuro-Fuzzy Inference Systems Sensors Mamdani and Sugeno adaptive neuro-fuzzy inference system CPU utilization complex evaluation |
| title | Computer State Evaluation Using Adaptive Neuro-Fuzzy Inference Systems |
| title_full | Computer State Evaluation Using Adaptive Neuro-Fuzzy Inference Systems |
| title_fullStr | Computer State Evaluation Using Adaptive Neuro-Fuzzy Inference Systems |
| title_full_unstemmed | Computer State Evaluation Using Adaptive Neuro-Fuzzy Inference Systems |
| title_short | Computer State Evaluation Using Adaptive Neuro-Fuzzy Inference Systems |
| title_sort | computer state evaluation using adaptive neuro fuzzy inference systems |
| topic | Mamdani and Sugeno adaptive neuro-fuzzy inference system CPU utilization complex evaluation |
| url | https://www.mdpi.com/1424-8220/22/23/9502 |
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