Software Aging Analysis of Web Server Using Neural Networks

Software aging is a phenomenon that refers to progressive performance degradation or transient failures or even crashes in long running software systems such as web servers. It mainly occurs due to the deterioration of operating system resource, fragmentation and numerical error accumulation. A primitive method to fight against software aging is software rejuvenation. Software rejuvenation is a proactive fault management technique aimed at cleaning up the system internal state to prevent the occurrence of more severe crash failures in the future. It involves occasionally stopping the running software, cleaning its internal state and restarting it. An optimized schedule for performing the software rejuvenation has to be derived in advance because a long running application could not be put down now and then as it may lead to waste of cost. This paper proposes a method to derive an accurate and optimized schedule for rejuvenation of a web server (Apache) by using Radial Basis Function (RBF) based Feed Forward Neural Network, a variant of Artificial Neural Networks (ANN). Aging indicators are obtained through experimental setup involving Apache web server and clients, which acts as input to the neural network model. This method is better than existing ones because usage of RBF leads to better accuracy and speed in convergence.Comment: 11 pages, 8 figures, 1 table; International Journal of Artificial Intelligence & Applications (IJAIA), Vol.3, No.3, May 201

Modelling of advanced submicron gate InGaAs/InAIAs pHEMTS and RTD devices for very high frequency applications

InP based InAlAs/InGaAs pseudomorphic High Electron Mobility Transistors (pHEMTs) have shown outstanding performances, which makes them prominent in high frequency mm-wave and submillimeter-wave applications. However, conventional InGaAs/InAlAs pHEMTs have major drawbacks, i.e., very low breakdown voltage and high gate leakage current. These disadvantages degrade device performance, especially in Monolithic Microwave Integrated Circuit (MMIC) low noise amplifiers (LNAs). The optimisation of InAlAs/InGaAs epilayer structures through advanced bandgap engineering together with gate length reduction from 1 m into deep sub-μm regime is the key solution to enabled high breakdown and ultra-high speed, low noise pHEMT devices to be fabricated. Concurrently, device modelling plays a vital role in the design and analysis of pHEMT device and circuit performance. Physical modeling becomes essential to fully characterise and understand the underlying physical phenomenon of the device, while empirical modelling is significant in circuit design and predicts device’s characteristic performance. In this research, the main objectives to accurately model the DC and RF characteristics of the two-dimensional (2D) physical modelling for sub-μm gate length for strained channel InAlAs/InGaAs/InP pHEMT has been accomplished and developed in ATLAS Silvaco. All modelled devices were optimised and validated by experimental devices which were fabricated at the University of Manchester; the sub-micrometer devices were developed with T-gate using I-line optical lithography. The underlying device physics insight are gained, i.e, the effects of changes to the device’s physical structure, theoretical concepts and its general operation, hence a reliable pHEMT model is obtained. The kink anomalies in I-V characteristics was reproduced and the 2D simulation results demonstrate an outstanding agreement with measured DC and RF characteristics. The aims to develop linear and nonlinear models for sub-μm transistors and their implementation in MMIC LNA design is achieved with the 0.25 m In0.7Ga0.3As/In0.52Al0.48As/InP pHEMT. An accurate technique for the extraction of empirical models for the fabricated active devices has been developed and optimised using Advance Design System (ADS) software which demonstrate excellent agreement between experimental and modelled DC and RF data. A precise models for MMIC passive devices have also been obtained and incorporated in the proposed design for a single and double stage MMIC LNAs in C- and X-band frequency. The single stage LNA is designed to achieve maximum gain ranging from 9 to 13 dB over the band of operation while the gain is increased between 20 dB and 26 dB for the double stage LNA designs. A noise figure of less than 1.2 dB and 2 dB is expected respectively, for the C- and X-band LNA designed while retaining stability across the entire frequency bands. Although the RF performance of pHEMT is being vigorously pushed towards terahertz region, novel devices such as Resonant Tunnelling Diode (RTD) are needed to support future ultra-high speed, high frequency applications especially when it comes to THz frequencies. Hence, the study of physical modelling is extended to quantum modelling of an advanced In0.8Ga0.2As/AlAs RTD device to effectively model both large size and submicron RTD using Silvaco’s ATLAS software to reproduce the peak current density, peak-to-valley-current ratio (PVCR), and negative differential resistance (NDR) voltage range. The simple one-dimensional physical modelling for the RTD devices is optimised to achieve an excellent match with the fabricated RTD devices with variations in the spacer thickness, barrier thickness, quantum well thickness and doping concentration

Measurement-based reliability prediction methodology

In the past, analytical and measurement based models were developed to characterize computer system behavior. An open issue is how these models can be used, if at all, for system design improvement. The issue is addressed here. A combined statistical/analytical approach to use measurements from one environment to model the system failure behavior in a new environment is proposed. A comparison of the predicted results with the actual data from the new environment shows a close correspondence

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption e ciency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence di ers from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.Ministerio de Economía y Competitividad DPI2015-70842-

A computing task ergonomic risk assessment tool for assessing risk factors of work related musculoskeletal disorders

Observation method remains to be the most widely applied method in assessing exposure to risk factors for work-related musculoskeletal disorders (WMSDs) related to office works because it is inexpensive and applicable to wide range of office jobs. However, the existing research that applied this method was mainly focused to a limited range of office components and computer accessories such as seat pan, keyboards, mouse, monitor and telephone. In addition, further testing of reliability and validity of the observational method was less reported. This study was conducted to propose the new office ergonomic risk assessment (OFFERA) method to assess a wide range of office risk factors related to WMSDs, which include office components and office environment where this method covers both right and left side of the body part. The initial development of OFFERA method was divided into two stages, the development of OFFERA system components and psychometric properties of OFFERA method. In reliability testing, the results of inter and intra observer reliability recorded good (K=0.62-0.78) and very good (K=0.81-0.96) agreement among the observers. Meanwhile, in validity testing, the relationship of the final score of OFFERA to the musculoskeletal symptoms statistically shows a significant value for wrists/hands (χ²=7.942; p=0.047), lower back (χ²=13.478; p=0.000), knees (χ²=7.001; p=0.008), and ankle/leg (χ²=5.098; p=0.024). The usability testing shows that the OFFERA method was easy and quick to be used (mean 4.48 ± 0.821) and applicable for wide range of office working activities (mean 4.02 ± 0.952). Based on the results obtained, it can be concluded that the OFFERA method was found to be practically reliable and applicable for wide range of office work-related activities