Novel method to forecast software aging problems

Yan, Yongquan

doi:10.1049/joe.2018.5407

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…Some researchers combine time series analysis with machine learning methods to improve the accuracy of aging predictions. Yan [15] used the ARIMA model to represent the linear trend of system resource consumption, and used BP artificial neural network to fit the hidden nonlinear features and residual linear features in the trend to build a hybrid model to predict the resource consumption trend. The requirements of time series signals are also relatively high, and at the same time, there are problems of slow convergence speed and easy to fall into local extreme values.…”

Section: Related Woekmentioning

confidence: 99%

Software aging prediction framework based on optimized TCN model with grey correlation analysis

Wang

Yao²,

Li³

et al. 2022

5th International Conference on Computer Information Science and Application Technology (CISAT 2022)

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The temporal convolutional network (TCN) model has the characteristics of strong parallelism and stable gradient in time series processing. The structural depth of the model is related to the input length, convolution kernel and dilation factor. In order to further improve the accuracy of prediction, this paper proposes a software aging prediction framework based on TCN model optimized by grey relational analysis. Collecting available memory data as the input of the framework, determine the length of the input nodes of the TCN model through gray correlation analysis, and then conduct training and prediction, and evaluate the efficiency of the model by checking the average error between the predicted output memory and the actual memory. Then change the length of the input chunk to carry out a comparative experiment, which verifies the effectiveness of the grey relational degree analysis.

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Section: Related Woekmentioning

confidence: 99%

Software aging prediction framework based on optimized TCN model with grey correlation analysis

Wang

Yao²,

Li³

et al. 2022

5th International Conference on Computer Information Science and Application Technology (CISAT 2022)

View full text Add to dashboard Cite

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“…Measurement‐based studies focus on real‐world software systems, where the data of interest are generated and analysed with the purpose of forecasting the resource exhaustion time. The rationale behind measurement‐based studies lies in that performance degradation is significantly related to available computing resources of the computer system [1, 2, 5–7, 10 ]. Measurement‐based studies include both empirical studies and controlled experiments .…”

Section: Motivationmentioning

confidence: 99%

“…This is in contrast to the widely accepted importance of experiment‐based studies [6, 7 ], and has led to the practices where many of the ageing models proposed in the literature are based on some arbitrary assumptions about the root origins and distributions of software ageing. Consequently, most studies treat software ageing as a stochastic phenomenon, and discretise the ageing process in a series of phases or epochs, focusing on the optimal rejuvenation time based on some arbitrary assumptions [6, 8–13 ]. Deterministic models are rarely reported.…”

Section: Introductionmentioning

confidence: 99%

Software ageing process as an evolving dynamic system

Jia

Zhi

2020

IET softw.

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Software ageing is correlated with available computing resources of the computer system. These available resources evolve with time, reflecting the developing mechanism of the ageing process. This study is the first to consider a degrading computer system as an evolving dynamic system. The authors proposed a non‐linear dynamic model of software ageing, where the coefficients are estimated using a dynamic inversion method, and conduct controlled software experiments, where the experimental conditions are controlled to expedite ageing. The model can be used to forecast mutations of resource variables such as the buffer, cache and central processing unit usage, which have a direct impact on software performance. The stability of the model is assessed by the Jacobi matrix method, and the results show that, when part of a resource has become unallocable, the Linux operating system can readjust the resources to keep the system's performance stable. This process repeats until some of the resources are exhausted, when the system will crash or hang. This study provides hints on the ageing mechanism of computer systems, which are rarely reported in the past.

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TTAFPred: Prediction of time to aging failure for software systems based on a two-stream multi-scale features fusion network

Jia,

Yu,

Zhang

et al. 2024

Software Qual J

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Software aging refers to the performance degradation and failure crash phenomena in long-running systems. As a proactive remedy, software rejuvenation can be scheduled timely to mitigate aging effects. Inescapably, how to accurately predict the time to aging failure (TTAF) of software is a prerequisite for implementing effective rejuvenation. However, the characterization of software aging is relatively complicated, leading to the selection of aging indicators case by case, which means that only fitting the variation trend of a single indicator for prediction models to formulate a rejuvenation schedule may be limited. To fill this gap, this paper proposes a novel framework called TTAFPred, which directly constructs the direct mapping relationships between the software aging process considering multiple system indicators and TTAF. Specifically, this framework includes three parts, i.e., data preprocessing, software degradation feature extraction, and TTAF prediction modules. First, the raw data is processed into the input form required by the network. Secondly, a temporal relationship extraction stream integrating bidirectional gated recurrent unit (BiGRU) with attention mechanism is used to extract temporal features from raw inputs. Synchronously, a spatial relationships extraction stream is adopted to extract the spatial features for enhancing the representation ability of degraded features by using the multiscale one-dimensional convolutional neural network (1DCNN) with the residual connection. Then, extracted temporal-spatial features from the two streams are further fused. Finally, two fully-connected layers are constructed to estimate the TTAF. The experiments are performed on two mainstream software systems (OpenStack and Android), and four sets of real run-to-failure data for each software system are collected. The effectiveness of the proposed TTAFPred is verified through extensive experiments with its seven competing models, and the prediction performance can be improved by 9.1%, 8.0%, and 8.0%, respectively, in terms of three evaluation metrics, compared to the best baseline model.

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Novel method to forecast software aging problems

Cited by 4 publications

References 18 publications

Software aging prediction framework based on optimized TCN model with grey correlation analysis

Software aging prediction framework based on optimized TCN model with grey correlation analysis

Software ageing process as an evolving dynamic system

TTAFPred: Prediction of time to aging failure for software systems based on a two-stream multi-scale features fusion network

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