40 results about "Software aging" patented technology

A method of operating a node of a computer network which uses a plurality of servers, by determining that one of the servers has degraded health due to software aging, assigning tasks to the other servers while reducing workload at the first server, rejuvenating the first server once its workload has terminated and, after rejuvenation, assigning tasks to the first server. The servers are clustered to provide service based on a single server address (TCP / IP). The node may include a gateway interface which receives the server requests and passes them on to a dispatcher at the node. Tasks are assigned in response to health-related messages sent by the servers and received by a workload monitor agent of the dispatcher.

A system and method is provided that rejuvenates a software application to reduce the effects of software aging. An active replica corresponding to a software application is identified. If rejuvenation of the software application is appropriate, a new replica is created and state information is transferred from the active replica to the new replica. In addition, client requests are redirected to the new replica. After the state data has been transferred and requests have been redirected, the active replica is terminated. Once the active replica has been terminated, the new replica becomes the active replica. When rejuvenation is again proper, another new replica is created and the state data is transferred from the new active replica to the new replica and requests are redirected to the new replica. This process repeats whenever rejuvenation of the application is needed.

A software program is configured in accordance with a software aging process such that one or more files generated by the program are at least partially encrypted using a cryptographic key associated with a current time interval for which the files are generated. The cryptographic key may be a symmetric key used for both encryption and decryption operations, such that cryptographic key used for encryption in a given interval is also required to decrypt files encrypted during that interval. Periodic updates of the software program are provided to a legitimate user of the software program, with a given one of the updates including a different cryptographic key associated with a subsequent time interval. The cryptographic key associated with a particular one of the time intervals may be common to each of a set of legitimate copies of the software program that have received the corresponding version of the periodic update. The variation of the cryptographic keys from one interval to another discourages unauthorized use of the software program by deliberately requiring both legitimate and illegitimate users to request unusually frequent updates, thereby substantially increasing the tracing and prosecution risks borne by software pirates.

An approach is provided that rejuvenates a software application to reduce the effects of software aging. An active replica corresponding to a software application is identified. If rejuvenation of the software application is appropriate, a new replica is created and state information is transferred from the active replica to the new replica. In addition, client requests are redirected to the new replica. After the state data has been transferred and requests have been redirected, the active replica is terminated. Once the active replica has been terminated, the new replica becomes the active replica. When rejuvenation is again proper, another new replica is created and the state data is transferred from the new active replica to the new replica and requests are redirected to the new replica. This process repeats whenever rejuvenation of the application is needed.

Load test is executed with an appropriate frequency which does not lead to a decrease in software development efficiency and a decrease in the precision of software aging detection. Load test of a version of software under test is executed in accordance with an execution criterion, presence or absence of a software aging problem is detected by comparing a test result of the load test with a test result of load test of a previous version of the software to be compared, and frequency of execution of subsequent load test is adjusted by changing the execution criterion based on a result of the detection.

The invention relates to a system re-graduating method of group environment applied server which tests weather the current server node is aging by aging formation, if it is aging, it sets the node as testing point, ascertains the current server node condition information of the EJB by the condition replicating operation and replicates the node condition to the backup node of anther server; the server calls the local operating system Windows APIí¬í¬ExitWindowsEx to restart the server node which comes into the group after restarting and in step with the other nodes and replicates the new condition of the current backup node to the local node; the applied server accepts the EJB application.

The invention discloses a software aging prediction method and device based on multi-model comparison, and belongs to the field of software aging, and the method comprises the steps: collecting agingindexes from a target software system, and processing the aging indexes into time sequence data as the pre-input of a model; for the aging data scale, designing an aging prediction model comprising machine learning and a neural network, calculating the prediction error of each model, and selecting the model with the minimum error as a candidate model; and calculating whether significant differences exist between the model and other models, and if the differences are obvious, selecting the model as a final aging prediction model. According to the method, the problem that the prediction result of a single model may influence decision making is solved, a user can automatically select a suitable model according to aging data features and prediction errors, active maintenance measures or earlyor late execution is avoided, and the influence on software reliability is reduced. More models can be expanded, and an optimal prediction model can be selected for different aging data scales to helpsystem operation and maintenance.

The invention discloses a software aging detection method, and the software aging detection method comprises the following steps: acquiring the total usage amount of piles corresponding to test software and target software at different moments, wherein the test software simulates the operation of the target software, and the test software is injected into a memory leak hole; determining a divergence value corresponding to each moment according to a first total usage amount corresponding to the test software and a second total usage amount corresponding to the target software, wherein the divergence value is a difference amount between the first total usage amount and the second total usage amount at the same moment; when each divergence value is less than a preset threshold, judging that the target software is not aged. The invention further discloses a software aging detection device and a computer readable storage medium. According to the method, the aging of the software is accurately determined.

The invention discloses a global optimization method based on the maintenance charge and for a two-tier software aging phenomenon. The method includes the following steps that the consumed memory Xi of an application tier within the time interval and the consumed memory Yi of an operation system tier within the time interval are monitored by a monitor; a two-tier software aging analysis model is built based on the updating process; the optimum value of an alarm threshold value of the operation system tier, the optimum value of the fatigue threshold value of the operation system tier and the optimum value of the alarm threshold value of the application tier are obtained according to the two-tier software aging analysis model; and the available maximum value of a two-tier software system is obtained. By means of the method, the overall availability can be improved effectively for a regeneration strategy method of the two-tier software.

The invention discloses a cloud server aging prediction method based on time series clustering and deep learning LSTM. The method comprises the following steps of extracting performance resource timeseries data on a cloud server, wherein the performance resource time series data comprises CPU idle rate data and system available memory data; preprocessing and decomposing wavelet packets on the time series data; and carrying out K-means clustering on the preprocessed data, counting a clustering center with the highest occurrence frequency, constructing a deep learning LSTM model by utilizing aclustering result, and predicting a software aging trend according to a time sequence prediction value of the LSTM model. Wavelet packet decomposition is utilized to overcome the limitation that a traditional prediction method is relatively low in prediction accuracy of non-stationary time series data and time series data with relatively large fluctuation, so that the software aging trend can be predicted more accurately. The problem of how to regenerate the software at the optimal time point for the aging phenomenon of the cloud server software is solved.

The invention provides a virtual machine working queue and redundant queue updating method for different aging scenes, and relates to the technical field of cloud computing. The method comprises the following steps: firstly, dividing different software aging scenes according to the survival time and load fluctuation conditions of the virtual machines, and then dynamically adjusting the number andsequence of working virtual machine copies by adopting a ridge regression-based virtual machine working queue dynamic updating method; and finally, dynamically updating the redundancy queue of the virtual machine based on the binary decision diagram. According to the virtual machine working queue and redundant queue updating method for different aging scenes, the service quality and the resource cost of the virtual machine are balanced through selection and switching strategies, the service quality of the system is guaranteed, and even if the working virtual machine has service failure, the redundant virtual machine can be switched in a short time to completely replace the service failure virtual machine.

The invention discloses a cross-project software aging prediction method, which comprises the following steps of: preprocessing data in a source project and a target project, reducing edge distribution and condition distribution difference by adopting joint distribution domain adaptation, and relieving a class imbalance problem by adopting an undersampling method and an improved subclass discriminant analysis method; and finally, using a machine learning classifier (logistic regression and the like) to perform prediction. According to the method, the condition distribution difference between the source project and the target project of the software aging defect data set is considered, and an improved subclass discriminant analysis method and the like are further adopted to relieve the extremely serious class imbalance problem. The problem that a traditional cross-project software aging defect prediction method is not high in precision and robustness is solved, developers are helped tofind and remove software aging related defects in the development test stage, and losses caused by the software aging problem are avoided. The feasibility of the method is verified on real software, and the method can be popularized to other software to predict software aging related defects.

The invention provides a method for predicting different types of business concurrency of a virtual machine, and relates to the technical field of cloud computing. A method for predicting different types of business concurrency of a virtual machine comprises the following steps: firstly, acquiring historical business concurrency of the virtual machine, preprocessing the historical business concurrency, and then judging the type of the virtual machine service concurrency based on improved 1 nearest neighbor-dynamic time adjustment method 1NN-DTW; and finally fitting the business concurrency without periodical change by adopting a classification regression tree. Fitting business concurrency with periodic change by adopting Fourier series FS and a classification regression tree CART; according to the method for predicting the concurrency of different types of services of the virtual machine, the concurrency of each service of the virtual machine is predicted, a basis can be provided for the next step of increase or decrease of the virtual machine, and meanwhile, the software aging condition of the virtual machine can be accurately estimated, so that the purpose of improving the performance and reliability of the working virtual machine is achieved.

The invention discloses a passage logic software aging test platform comprising multiple supports, an annular transmission mechanism, simulation mechanisms and a detection mechanism. A transmission belt is used as the annular transmission mechanism. Ticket cards and paperboards simulating passengers are fixed on the transmission belt. The condition of passage through a channel of the passengers is simulated by rotation of the transmission belt. Various passage conditions are simulated by the number and the position of the ticket cards and the paperboards. Different passage speed is simulated by a speed adjustable motor. The platform is fixed on the aluminum section supports, and the bottom part is provided with truckles so that movement is convenient. The whole passage logic software aging test platform has advantages of being simple in structure, low in cost and stable and reliable in working.

The invention discloses a cloud server resource performance prediction method using an ARIMA-RNN combined model, and the method comprises the steps: firstly carrying out the preprocessing of sequencedata, and enabling the original sequence data to be mapped to [-1, 1]; determining an ARIMA model, and then training, predicting and storing existing data; determining an RNN model structure, and training the RNN model by using the existing data and the prediction result of the ARIMA model on the existing data; and inputting the prediction result of the ARIMA model for the moment t data and the data of the moments t1, t2,..., tn into the RNN model to predict the data of the moment t. According to the method, the limitation that the ARIMA model is relatively low in prediction precision for datawith relatively large fluctuation is overcome, the problems of low convergence rate and instability of the RNN model are solved, finally, prediction and analysis of performance parameters of the cloud server system are realized, and the software aging phenomenon is predicted more accurately.

The invention discloses an in-project software aging prediction method based on Active Learning, and the method comprises the steps: collecting the static measurement of a code in software, selectinga sample through Active Learning, carrying out the labeling of the sample, taking the sample as a training set, and predicting the remaining samples without class labels; and adopting active Learningfor sample selection and manual labeling, and forming a training set. An oversampling and undersampling combined method is adopted to relieve the class imbalance problem, and a machine learning classifier is used for prediction. According to the method, few software aging defect data set samples are considered, time and labor are consumed for collection, the problem of polar imbalance is relievedby adopting an undersampling and oversampling combined method, developers are helped to discover and remove software aging related defects in the development and test stage, and losses caused by the software aging problem are avoided. The feasibility of the method is verified on real software, and the method can be popularized to other software to predict software aging related defects.

The present invention belongs to the field of computer network transmission, and particularly relates to a flow table aging control method, system, device and readable storage medium. The method includes: constructing a flow link table according to the sequence of data flow generation, and establishing a corresponding connection between the flow link table and the corresponding elements in the corresponding record table; starting from the head of the flow link table, judging whether the generation time of the data flow in the flow link table is greater than a predetermined time; in response to the generation time of the corresponding data flow being greater than the predetermined time, clearing the data flow from the corresponding record table and the flow link table. Through the flow table aging control method proposed by the present invention, the access order of the flow table is sorted by the created flow link table, so that the aging detection is targeted, avoiding a large number of invalid polling operations of the CPU on the SOC, significantly reducing the CPU consumption, without frequent reading and writing of the CT table, and at the same time releasing the bandwidth occupation of PCIe and DDR, supporting automatic aging and software aging of the hardware, being flexible and friendly, and allowing users to choose by themselves.

The invention discloses a method for predicting the resource performance of a cloud server based on an LSTM-ACO model. The method comprises the steps of firstly carrying out the preprocessing of time sequence data, and mapping original sequence data to a [0, 1] interval; then determining an LSTM model, training and predicting existing data, and optimizing the LSTM model by using an ant colony algorithm; and finally, inputting the prediction result of the LSTM model for the data of the moment t and the data of the moments t-1, t-2,..., t-n into the LSTM-ACO model, and predicting the data of the moment t. According to the method for predicting the resource performance of the cloud server based on the LSTM-ACO model, the problem that a traditional prediction method is not high in precision in the prediction process is solved, the LSTM parameters are optimized through ACO, the problem that the model is caught in a local optimal solution is avoided, and the prediction convergence speed is increased; and finally, cloud server resource and performance prediction is realized, and the software aging phenomenon is predicted more accurately.

The invention relates to a software aging exception behavior classification method and system. The method comprises the steps of collecting a running index of a server in a defined sliding window; determining server running exception nodes of the sliding window; and classifying exception behaviors of the server running exception nodes. Through a new research approach for describing software agingbehaviors for server running exception behaviors, software aging is detected by adopting an exception point detection method in combination with the sliding window.

The invention discloses a load-related software aging detection method. The method comprises the following steps: S1, pressurizing to-be-detected software by utilizing a load generator; S2, periodically collecting a plurality of aging variables by utilizing a monitoring tool, simplifying the key variables by adopting a dimension reduction method, performing linear estimation on an aging trend by reducing multicollinearity, analyzing the aging variables, and screening out a monitoring index with the highest correlation; s3, establishing an automatic time sequence analysis model, and analyzing the screened monitoring indexes to obtain a time sequence curve signal; and S4, constructing a deviation graph according to the time sequence curve signal, and judging whether an aging phenomenon exists or not according to a deviation value and a change rule. The software aging detection method solves the problem that a traditional software aging detection method based on trend detection is high infalse alarm rate, facilitates developers to detect the software aging problem in the development stage, and avoids loss caused by the software aging problem.

The invention discloses an unsupervised software aging detection method based on a data flow local outlier factor, which abandons the unlike binary attribute of the detection result of the existing algorithm, and utilizes the local outlier factor of the flow data to represent the aging degree of a software system at each moment. Therefore, the aging degree of the software system at each moment can be visually displayed through a specific numerical value. Meanwhile, the influence of external environment factors of the software system on aging state detection of the software system is also considered. Therefore, when the local outlier coefficient of the streaming data is calculated, the attributes of the monitoring data are divided into inherent attributes and environment attributes. Secondly, performing nearest neighbor search on the tested data by using the environmental parameters, and calculating a local outlier coefficient of the tested data by using the intrinsic attribute parameters; compared with a supervised detection algorithm, the method has the advantages that rules are directly searched in the data set without marking the training data set, and the method belongs to the category of unsupervised learning.

The present invention provides a method and system for detecting elderly-friendly defects in mobile software based on a large language model. The method includes: generating initial test cases using depth-first search; executing the generated test cases and obtaining runtime information; generating prompt words based on motion information; generating mutation operations based on the large language model; performing event mutations on the initial test cases and executing them; performing component mutations on the initial test cases and executing them; and automatically identifying triggered elderly-friendly defects. This invention solves the technical problems of difficulty in generating efficient test cases to trigger elderly-friendly defects in mobile applications and difficulty in automatically identifying and detecting such defects.

The invention provides a software aging streaming monitoring system, which belongs to the research field of distributed systems. For the host nodes in the existing distributed system, the data collection module is designed to collect the memory information of each host node and upload the information to the message queue module. Then, according to the memory data in the message queue module, the real-time exception discovery module uses the bucket algorithm to judge whether the memory in each host node is overloaded, and the software aging prediction module predicts the failure and abnormal time of each host node. Finally, the operation and maintenance personnel obtain the data processing results from the display module. The invention solves the problem of high false alarm rate and missing alarm rate of abnormal early warning based on the threshold of traditional distributed system monitoring tools, and solves the problem that the traditional software aging analysis method is difficult to cope with the daily TB-level streaming data of the distributed system.

The invention relates to a software aging prediction method and device based on feature reconstruction and tag guidance. The method comprises the following steps: acquiring a source code of target software and an aging tag sample corresponding to the source code; extracting a metric feature and a graph structure feature from the source code, and reconstructing the metric feature and the graph structure feature into a feature image; based on the feature image, obtaining aging feature representation of the source code through self-training of contrast learning optimization; generating a pseudo tag for an unlabeled code unit in the source code by using tag propagation assisted by a buffer area and the aged tag sample; constructing a data set based on the source code, the aged tag sample and the pseudo tag; and based on the aging feature representation and the data set, training a prediction model and performing aging prediction on the target software. Through feature reconstruction and buffer area assisted label propagation, the stability and reliability of the pseudo label are improved, and the problem that the quality of the pseudo label is poor in the initial training stage of a traditional self-training method is solved.

The invention provides a virtual machine software aging prediction method based on AdaBoost-Elman, and relates to the technical field of cloud computing. This method first sets the level for evaluating the aging degree of the virtual machine software, and trains the software aging index prediction model of the virtual machine and the reference prediction model of the non-aging virtual machine; In the software aging index prediction model of the virtual machine and the non-aging virtual machine reference prediction model, the software aging index prediction result of the virtual machine and the reference prediction result of the non-aging virtual machine are output; finally, according to the software aging index prediction result of the virtual machine and the non-aging virtual machine Evaluate the software aging trend of the virtual machine based on the reference prediction results of the virtual machine. The method of the invention can predict the software aging index of the current working virtual machine, and compare it with the non-aging virtual machine, so as to obtain the software aging degree of the virtual machine in the next period, and take preventive measures in advance.

The invention discloses a software aging prediction method and device based on multi-model comparison, belonging to the field of software aging, collecting aging indicators from target software systems, processing them into time series data as pre-input of models; aiming at aging data scale, designing Including the aging prediction model of machine learning and neural network, calculate the prediction error of each model, select the model with the smallest error as the candidate model; calculate whether there is a significant difference between the model and other models, if the difference is obvious, select the model for the final aging prediction model. The invention solves the problem that the prediction result of a single model may affect the decision-making, and the user can automatically select a suitable model according to the characteristics of aging data and prediction errors, avoiding the early or late implementation of proactive maintenance measures and reducing the reliability of the software. sexual influence. More models can be expanded, and the optimal prediction model can be selected for different aging data scales to help system operation and maintenance.

The invention provides a software residual life prediction model generation method, a prediction method and a storage medium, and belongs to the technical field of software aging and regeneration, and the generation method comprises the steps: obtaining failure field data during the software operation period, and carrying out the preprocessing of the failure field data, and obtaining a training sample; training a preset network model based on the training sample to obtain a software residual life prediction model; the network model comprises the following steps: an embedded encoding layer encodes training samples to obtain high-order representation with a unified dimension; the multi-scale image convolution model converts high-order representation into multi-scale spatial feature representation; the frequency domain window fusion model obtains frequency domain and time domain fused high-dimensional feature representation based on multi-scale spatial feature representation; and the multi-scale convolution predictor predicts the residual life of the software based on the high-dimensional features. According to the method, the problem that the software restart decision is not accurate due to the fact that an existing method has defects in the aspects of extracting a complex aging factor relation, modeling a multi-scale aging mode and the like can be solved.

The invention provides a method for predicting concurrent business of different types of virtual machines, and relates to the technical field of cloud computing. A business concurrency prediction method for different types of virtual machines. Firstly, the historical business concurrency of virtual machines is collected and preprocessed, and then the virtual machine business concurrency is judged based on the improved 1nearest neighbor-dynamic time adjustment method 1NN-DTW type; finally, the classification regression tree is used to fit the business concurrency without periodic changes; the Fourier series FS and classification regression tree CART are used to fit the business concurrency with periodic changes; the invention provides different types of virtual machines The business concurrency prediction method based on this method predicts the concurrency of each business of the virtual machine, which can provide a basis for the increase or decrease of the virtual machine in the next step, and at the same time help to accurately estimate the software aging status of the virtual machine, so as to improve the performance of the virtual machine. performance and reliability purposes.

The application discloses a kind of software aging defects of Android application-oriented black box detection method.The method is without obtaining application source code, by executing engine drive to-be-tested Android application, and the runtime index such as memory, communication, interface object, file and computing resource is collected;Utilize the reinforcement learning strategy of resource perception reward function guidance and preferentially explore the application state that resource change is remarkable;After reaching new state, based on the metamorphosis relationship of resource invariability, the event sequence of repeated execution is generated, and the trend analysis is carried out to resource use time sequence, to identify the aging related defects of continuous growth.The application can locate defect trigger state, event sequence and abnormal resource index in a short time, improve the efficiency, reproducibility and diagnosability of Android application aging defect detection.

Load test is executed with an appropriate frequency which does not lead to a decrease in software development efficiency and a decrease in the precision of software aging detection. Load test of a version of software under test is executed in accordance with an execution criterion, presence or absence of a software aging problem is detected by comparing a test result of the load test with a test result of load test of a previous version of the software to be compared, and frequency of execution of subsequent load test is adjusted by changing the execution criterion based on a result of the detection.