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44 results about "Hybrid kernel" patented technology
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A hybrid kernel is an operating system kernel architecture that attempts to combine aspects and benefits of microkernel and monolithic kernel architectures used in computer operating systems.
Short-term load prediction method based on particle swarm optimization least squares support vector machine
InactiveCN106952183AImprove search abilityImprove search efficiencyForecastingCharacter and pattern recognitionLeast squares support vector machineShort terms
The present invention relates to a short-term load prediction method based on a particle swarm optimization least squares support vector machine. Aiming at the deficiency of a single kernel function least squares support vector machine model, the Gaussian kernel function and the Polynomial kernel function are combined to obtain a new hybrid kernel function so as to improve the learning ability and the generalization ability of the least squares support vector machine model; the particle swarm optimization algorithm based on double populations is employed to optimize parameters of the least squares support vector machine of the hybrid kernel function, the particle swarm optimization algorithm based on double populations has advantages of good global search and local search performances, and a strategy having dynamic accelerated factors is employed so as to greatly increase the variety of particles and prevent the search from being caught in a local extremum. The short-term load prediction method based on the particle swarm optimization least squares support vector machine maximally utilizes information in computation, and in the process of selecting the optimal parameter value, the average mean square error of load data and actual data is employed as the adaptation value of the particle swarm optimization algorithm so as to improve the short-item load prediction accuracy value.
Owner:WUHAN UNIV
Electroencephalographic and electromyographic information automatic intention recognition and upper limb intelligent control method and system
ActiveCN109394476AMotion track real-time adjustmentImprove recognition accuracyDiagnosticsGymnastic exercisingIntent recognitionRisk stroke
The invention relates to an electroencephalographic and electromyographic information automatic intention recognition and upper limb intelligent control method and system, which are used for rehabilitation treatment of the upper limb of a stroke patient, an electroencephalographic and surface electromyographic signal collector collects and processes the electroencephalographic and surface electromyographic signals of the patient in real time, a mixed kernel function formed by weighting a polynomial kernel function and an RBF kernel function weights is used to perform fitting and prediction, soas to more accurately identify and monitor the motion intention of the patient, and judge the corresponding degree of rehabilitation, according to which a corresponding rehabilitation training strategy is adopted. When the rehabilitation degree of the upper limb of the stroke patient is low, passive training control is adopted. When the rehabilitation degree of the upper limb of the stroke patient is high, active, assisted and resistive control modes are adopted. The hybrid kernel function support vector machine model provided by the invention has better learning ability and generalization performance, high prediction accuracy and good control performance, and the prediction result meets the index requirements of a rehabilitation robot for stroke patients.
Owner:上海神添实业有限公司 +1
SAR image classification method based on SVM classifier of mixed nucleus function
InactiveCN101488188AImplement classificationThe classifier is constructed based on the wavelet feature implementationCharacter and pattern recognitionImaging processingTest sample
Owner:XIDIAN UNIV
System and method for hybrid kernel- and user-space incremental and full checkpointing
A system includes a multi-process application that runs. A multi-process application runs on primary hosts and is checkpointed by a checkpointer comprised of at least one of a kernel-mode checkpointer module and one or more user-space interceptors providing at least one of barrier synchronization, checkpointing thread, resource flushing, and an application virtualization space. Checkpoints may be written to storage and the application restored from said stored checkpoint at a later time. Checkpointing may be incremental using Page Table Entry (PTE) pages and Virtual Memory Areas (VMA) information. Checkpointing is transparent to the application and requires no modification to the application, operating system, networking stack or libraries. In an alternate embodiment the kernel-mode checkpointer is built into the kernel.
Owner:IBM CORP
Production method for injection molding machine workpiece production line
InactiveCN102909844ARealize intelligent and fully automated productionRealize automated productionInjection molding machineProduction cycle
The invention discloses a production method for an injection molding machine workpiece production line. A computer control system integrating a system decision, monitoring and learning functions is established for the characteristics of injection molding machine workpiece manufacturing systems of double production lines so as to realize automatic intelligent optimized production; a system control task is finished by a centralized controller and a stimulation optimizer commonly, monitoring of a production system and running of a control instruction are finished by PLC (programmable logic controller) and a production line state monitor; and a value iteration learning thought is realized by a reinforcement learning device through a support vector machine with a hybrid kernel function, so that the stimulation optimizing capability of a system stimulator can be improved by flexibly utilizing offline learning, the total running time of the control system is shortened, the running quality of the control system is improved, the production cycle of the manufacturing system is shortened, and the equipment utilization rate is increased.
Owner:南京塑维网络科技有限公司
Aeroengine gas path fault diagnosis method based on twin support vector machine (SVM)
InactiveCN106295153AHigh precisionImprove performanceArtificial lifeSpecial data processing applicationsAviationParticle swarm optimization
The invention discloses a new method of gas path fault diagnosis of aeroengine based on hybrid particle swarm optimization twin support vector machine (SVM) algorithm. In view of the fact that the gas path fault is the most frequent in the whole aeroengine failure and the strong demand for the intelligent diagnosis method in the field, and TWSVM has the advantages of faster theoretical calculation and better response to the sample imbalance problem, and adopts the TWSVM algorithm to study aeroengine gas path fault diagnosis. In this paper, the hybrid kernel function is introduced to improve the performance of the kernel function, so that the TWSVM algorithm can better balance the generalization ability and good learning ability. In addition, the optimal parameters of TWSVM are optimized by using HPSO, and the fault classification model is obtained. The aeroengine gas path fault diagnosis is realized with high precision.
Owner:NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
Support vector regression machine model selection method
InactiveCN106096646AImprove generalization abilityImprove forecast accuracyCharacter and pattern recognitionModel selectionGenetic algorithm
The invention discloses a support vector regression machine model selection method, and brings forward a novel support vector regression machine model selection method based on a hybrid kernel function and volume Kalman filtering, for solving the problem of model selection of a support vector regression machine. The hybrid kernel function is selected as a kernel function of the support vector machine, a combination coefficient of the hybrid kernel function is embedded into a superparametric state vector composed of a kernel function parameter and a regression parameter, accordingly, the problem of model selection is converted into a problem of state estimation of a nonlinear system, and then based on the high-performance volume Kalman filtering, superparametric estimation is carried out. A simulation experiment shows that the method brought forward by the invention, compared to a volume Kalman filtering support vector regression machine model selection method of a single kernel function and a genetic algorithm, has the following advantages: the generalization capability of a decision regression function obtained through the method is greater, and the prediction precision is higher.
Owner:QUZHOU UNIV
Colorectal cancer prediction method and device based on marker gene and hybrid kernel function SVM
InactiveCN108615555AImprove featuresFast convergenceMedical data miningMedical automated diagnosisDiseaseAlgorithm
The invention discloses a support vector machine classifier construction method and a support vector machine classifier construction device for colorectal cancer prediction. The method comprises the steps of: acquiring health and colorectal cancer sample data and preprocessing the data; determining disease-related feature genes based on the two sets of sample data; utilizing a Gaussian kernel function, a polynomial kernel function and a linear kernel function to construct a hybrid kernel function support vector machine; and optimizing parameters of the hybrid kernel function support vector machine. The support vector machine constructed by adopting the method and the device is more suitable for performing classification based on marker genes and can save time for colorectal cancer judgment.
Owner:SHANDONG NORMAL UNIV
SVDD radar target one-dimensional distance image identification method based on density weight and hybrid kernel function
ActiveCN107576949AImprove generalization abilityGood for global featuresWave based measurement systemsDensity distributionRadar
The invention discloses an SVDD radar target one-dimensional distance image identification method based on density weight and a hybrid kernel function and belongs to the radar target identification field. The K-type kernel function has strong generalization capability, extraction of global characteristics of training data is facilitated, complex index operation of a radial basic kernel function isavoided, and properties of small calculation complexity of a polynomial kernel function and high approximation precision of the radial basic kernel function are realized. The radial basic kernel function has quite good local characteristics, the K-type kernel function and the radial basic kernel function are combined to replace a kernel function of a traditional SVDD algorithm, moreover, a localdensity algorithm based on interception distance is employed to calculate local density between a support vector and training sample data in the high-dimensional kernel characteristic space, accordingto density distribution, the shape of a super-closed ball is adjusted, and identification performance of radar one-dimensional distance image single type targets is effectively improved.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA
Machine learning-based hybrid kernel function indoor positioning method
ActiveCN107703480AHigh positioning accuracyBest weight factorPosition fixationMachine learningData setAlgorithm
A machine learning-based hybrid kernel function indoor positioning method disclosed by the present invention comprises the steps of firstly establishing a fingerprint map library, and taking the fingerprint map library as a training data set; then utilizing a weighted summation method to construct a hybrid kernel function, and using a support vector regression algorithm and a v-folding cross validation method in the machine learning algorithms to train to obtain an optimal weight coefficient and an optimal kernel parameter of the hybrid kernel function; and finally under the premises of the optimal weight coefficient and the optimal kernel parameter, carrying out the offline training learning on the training data set to obtain the fitting functions of an x coordinate and a y coordinate separately, and then utilizing the fitting functions to carry out the online learning on an RSSI value received by a target, thereby obtaining the position coordinate of the target. Compared with the conventional indoor positioning algorithms of a BP neural network algorithm, a k-nearest neighbor algorithm, a linear kernel function algorithm, a polynomial kernel function algorithm and a Gaussian kernel function algorithm, the positioning precision of the algorithm of the present invention is higher.
Owner:广州世炬网络科技有限公司
Random forest classification method used for coronary heart disease data classification and based on kernel extreme learning machine and parallelization
ActiveCN108108762AImprove classification performanceReduce time overheadMedical automated diagnosisCharacter and pattern recognitionLearning machineClassification methods
The invention discloses a random forest classification method used for coronary heart disease data classification and based on a kernel extreme learning machine and parallelization. Sampling with replacement is performed on a coronary heart disease sample set by using a Bootstrap method so that different coronary heart disease data training subsets and test subsets are generated to be used for base classifiers; the kernel function of the hybrid kernel form is used as the kernel function of the kernel extreme learning machine so that the influence of the kernel type on the performance of the classification model can be reduced; model training is performed on the kernel extreme learning machine by using the coronary heart disease data training subsets and performing testing is performed on the base classifiers by using the test subsets, cyclic judgment is performed by using the mode of sorting and particle swarm optimization and the optimized new base classifiers are regenerated and thebase classifiers having poor classification performance are eliminated and substituted so that the objective of enhancing the overall classification performance can be achieved; and the random forestmodel is formed and then the classification result is selected by using a relative majority voting method.
Owner:BEIJING UNIV OF TECH
System and method for hybrid kernel—and user-space incremental and full checkpointing
A system includes a multi-process application that runs on primary hosts and is checkpointed by a checkpointer comprised of a kernel-mode checkpointer module and one or more user-space interceptors providing at least one of barrier synchronization, checkpointing thread, resource flushing, and an application virtualization space. Checkpoints may be written to storage and the application restored from said stored checkpoint at a later time. Checkpointing may be incremental using Page Table Entry (PTE) pages and Virtual Memory Areas (VMA) information. Checkpointing is transparent to the application and requires no modification to the application, operating system, networking stack or libraries. In an alternate embodiment the kernel-mode checkpointer is built into the kernel.
Owner:IBM CORP
Methods of scatter correction of x-ray projection data 2
ActiveUS20110255656A1Reconstruction from projectionMaterial analysis using wave/particle radiationPiercing pointKernel method
A system and method for forming an adjusted estimate of scattered radiation in a radiographic projection of a target object, which incorporates scattered radiation from objects adjacent to the target object, such as a patient table. A piercing point equalization method is disclosed, and a refinement of analytical kernel methods which utilizes hybrid kernels is also disclosed.
Owner:VARIAN MEDICAL SYSTEMS
Three-DOF (Degree of Freedom) hybrid magnetic bearing mixed kernel function support vector machine displacement detection method
The invention discloses a method for realizing displacement self detection of a three-DOF (Degree of Freedom) AC / DC (Alternating Current / Direct Current) hybrid magnetic bearing by utilizing a displacement prediction model of a hybrid kernel function SVM. The method is characterized in that magnetic bearing control current is used as an input sample, radial and axial displacements are used as output samples, sample data are collected, a hybrid kernel function is selected, performance parameters of an SVM are optimized through a PSO (Particle Swarm Optimization), an LS (Least Squares) SVM is trained by utilizing a training sample and the performance parameters, a non-linear prediction model is established, the prediction model is connected with a linear closed-loop controller before being connected to the three-DOF AC / DC hybrid magnetic bearing in series, magnetic bearing displacement closed-loop control is formed with an extended current hysteresis loop three-phase power inverter and a switch power amplifier, and self detection of a three-DOF AC / DC hybrid magnetic bearing displacement-free sensor is realized.
Owner:JIANGSU UNIV
System and method for hybrid kernel and user-space checkpointing using a character device
ActiveUS9164843B1Redundant operation error correctionElectrical componentsVirtualizationOperational system
Owner:PHILIPS NORTH AMERICA LLC
Methods of Scatter Correction of X-Ray projection data 1
ActiveUS20110255655A1Reconstruction from projectionMaterial analysis using wave/particle radiationKernel methodPiercing point
A system and method for forming an adjusted estimate of scattered radiation in a radiographic projection of a target object, which incorporates scattered radiation from objects adjacent to the target object, such as a patient table. A piercing point equalization method is disclosed, and a refinement of analytical kernel methods which utilizes hybrid kernels is also disclosed.
Owner:VARIAN MEDICAL SYSTEMS
Methods of scatter correction of x-ray projection data 2
ActiveUS8199873B2Reconstruction from projectionMaterial analysis using wave/particle radiationPiercing pointKernel method
Owner:VARIAN MEDICAL SYSTEMS
A debris flow prediction method based on PCA and a mixed kernel function LSSVR
ActiveCN109886456ASolving the Curse of Prediction DimensionalityReduce complexityForecastingBiological modelsEquilibrium modelingAlgorithm
The invention discloses a debris flow prediction method based on PCA and a mixed kernel function LSSVR, and the method comprises the steps of firstly, building a debris flow monitoring and early warning system, obtaining an influence factor of an initial debris flow disaster, and carrying out the dimensionality reduction of the obtained initial influence factor through PCA; secondly, constructinga mixed kernel function LSSVR debris flow disaster model by utilizing the initial influence factors after dimension reduction; then, applying the whale algorithm to optimize the established mixed kernel function LSSVR debris flow disaster model, and obtaining the optimized combined model parameters; and finally, reconstructing a mixed kernel function LSSVR mud-rock flow disaster model by using theobtained combined model parameters, and outputting a mud-rock flow occurrence prediction result. According to the method disclosed by the invention, the complexity of a model structure is greatly reduced, and a dimensionality disaster is prevented; a hybrid kernel function mechanism is introduced to balance model learning ability and generalization ability, and the prediction accuracy is improved.
Owner:XI'AN POLYTECHNIC UNIVERSITY
Parkinson's disease diagnosis method based on hybrid kernel function support vector machine model
InactiveCN109102884AReduce medical costsImprove diagnostic efficiencyMedical automated diagnosisCharacter and pattern recognitionDiseaseSupport vector machine
The invention discloses a parkinson's disease diagnosis method based on a hybrid kernel function support vector machine model. The method comprises steps that firstly, acquisition of speech signals for Parkinson patients and healthy people is performed; secondly, feature extraction of the speech signals is performed; thirdly, a hybrid kernel function of the support vector machine model is constructed; fourthly, the intelligent optimization algorithm is utilized to optimize a penalty parameter C, a Gaussian kernel function parameter g in the hybrid kernel function, a Sigmoid kernel function parameter h and a proportional parameter t in the establishment process of the support vector machine model, based on the optimization result, the optimal support vector machine model is established, andlastly, the optimal support vector machine model is utilized to classify and predict the to-be-detected speech, and diagnosis of Parkinson's diseases is achieved. The method is advantaged in that newideas are provided for diagnosis of the Parkinson's diseases, medical cost is reduced, diagnosis efficiency is improved, and accuracy of Parkinson's disease diagnosis is improved.
Owner:NANJING UNIV OF POSTS & TELECOMM
Coal mine gas prediction method
InactiveCN106777445AAvoid false alarmsGuaranteed leak-proof alarmSpecial data processing applicationsData setRegression analysis
The invention discloses a coal mine gas prediction method. The KPCA algorithm is used for identifying a large number. Firstly, two hybrid kernel functions are constructed, a kernel matrix is constructed through a vector method, and a feature vector of the kernel matrix is calculated through kernel principal component analysis, wherein the algorithm is high in recognition rate and calculation speed; according to the algorithm, by means of a group of standard orthonormal bases of a subspace spanned by training samples in a feature space, the KPCA process on a training set is converted into the PCA process in which coordinates of all the kernel training samples under the group of bases are datasets, meanwhile, feature extraction is conducted on the training samples so that nonlinear features of training data can be effectively captured and widely paid attention to and applied in mode recognition and regression analysis. In the KPCA solving process, feature values need to decompose the M*M kernel matrix (M refers to the number of the training samples), when feature extraction is conducted on one sample, people only need to calculate the sample and the kernel functions between the samples forming the group of bases, and experiment results verify that the algorithm is effective.
Owner:胡建东
Expressway traffic event detection method based on hybrid kernel correlation vector machine
PendingCN113313145AImprove detection rateEasy to detectCharacter and pattern recognitionArtificial lifeEmergency rescueGenerative adversarial network
The invention discloses an expressway traffic event detection method based on a hybrid kernel correlation vector machine, and the method comprises the steps of constructing an expressway traffic event detection initial variable set according to the change characteristics of upstream and downstream traffic flow parameters of a traffic event; learning minority class sample information by adopting a conditional generative adversarial network, training a generator to generate minority class supplementary samples, and balancing data distribution; screening key variables out through variable importance measurement of an XGBoost algorithm; establishing a combined kernel function based on a local Gaussian kernel and a global polynomial kernel; taking the key variables as input, and training a multi-kernel relevance vector machine model; and optimizing parameters through an improved fruit fly optimization algorithm to obtain an optimal model. According to the invention, the traffic incident detection rate is improved, the traffic incident occurring on the expressway is detected in time, time is won for road emergency rescue, casualties and property loss in the event are reduced, and meanwhile, technical support is provided for road traffic safety risk early warning.
Owner:SOUTHEAST UNIV
Methods of scatter correction of x-ray projection data 1
ActiveUS8199879B2Reconstruction from projectionMaterial analysis using wave/particle radiationPiercing pointKernel method
Owner:VARIAN MEDICAL SYSTEMS
MMC (modular multi-level converter) sub-module open-circuit fault detection method based on hybrid kernel support tensor machine
The invention provides an MMC (modular multi-level converter) sub-module open-circuit fault detection method based on a hybrid kernel support tensor machine, and the method comprises the steps: takingthe convex combination form of a radial kernel function and a polynomial kernel function as a hybrid kernel function; performing the sub-module open-circuit fault diagnosis of an MMC through a support tensor machine, and performing the training and testing of the collected data through combining with the AC side current and loop current data characteristics obtained during the normal operation and fault operation of the MMC; taking the AC current, a current envelope mean value and the change characteristics of a loop current among three phases as the fault judgment basis, and expressing a fault diagnosis result in a simple mode so as to timely detect the fault, thereby avoiding the accident potential troubles caused by the delayed detection to a system.
Owner:WUHAN UNIV OF SCI & TECH
Air conditioning system sensor fault detection method and device and electronic equipment
PendingCN111898725AOptimization parametersImprove fault detection rateArtificial lifeMathematical modelAir conditioning
The invention discloses an air conditioning system sensor fault detection method and device and electronic equipment. The method comprises the following steps: determining system evaluation indexes, building a target function, and therefore a fitness function is determined; selecting a Gaussian radial basis kernel function and a polynomial kernel function to construct a vertical air conditioning system sensor fault detection hybrid kernel function matrix, and establishing a hybrid kernel function-based KPCA mathematical model for describing the operation state of the air conditioning system and sensor fault detection; and optimizing the hybrid kernel function parameters by using a multi-objective particle swarm algorithm to obtain fault detection optimization of the air conditioning systemsensor. According to the invention, the multi-target particle swarm optimization algorithm is introduced to solve the problem of fault detection optimization of the air conditioning system sensor, the hybrid algorithm is improved to solve the target problem, certain advantages are achieved, and fault detection optimization of the air conditioning system sensor can be achieved.
Owner:XI'AN UNIVERSITY OF ARCHITECTURE AND TECHNOLOGY
SDN (Software Defined Network) flow control method based on fuzzy C-means and hybrid kernel least square support vector machine
ActiveCN114500335AImprove forecast accuracyGood local learning abilityCharacter and pattern recognitionTransmissionInternet trafficLow frequency band
The invention belongs to the field of network flow prediction, and particularly relates to an SDN (Software Defined Network) flow control method based on a fuzzy C mean value and a mixed kernel least square support vector machine, which comprises the following steps of: converting non-stationary SDN flow data into a stationary time sequence component by adopting discrete wavelet transform; processing the stationary time sequence component to obtain amplitude signals of a high frequency band and a low frequency band; clustering the amplitude signals of the high and low frequency bands by adopting a fuzzy C-means algorithm; an optimized self-adaptive mixed kernel least square support vector machine prediction model is adopted to predict the clustered components respectively; reconstructing the prediction results of all the components to obtain a prediction result of the SDN network data traffic; according to the method, a fuzzy C-means algorithm is utilized, a membership mechanism is introduced, time sequence components are divided into a high-frequency low-amplitude component, an intermediate-frequency middle-amplitude component and a low-frequency high-amplitude component according to amplitude-frequency characteristics of the time sequence components, and accurate prediction is provided for subsequent classification prediction.
Owner:CHONGQING UNIV OF POSTS & TELECOMM
An SVM classification method based on a hybrid kernel function
InactiveCN109447178AGood extrapolation abilityImprove learning abilityCharacter and pattern recognitionData setClassification methods
The invention discloses an SVM classification method based on a mixed kernel function, and the method comprises the steps of 1 collecting a data set, analyzing each sample in a collected data set record, distinguishing different attributes of the samples, and determining input and output samples; 2 selecting and constructing a kernel function, and mixing the exponential distribution kernel function with the radial basis kernel function; 3 optimizing parameters in the mixed kernel function; 4 selecting C-SVC model, establishing a support vector machine classification model based on a novel mixed kernel function; and 5 performing classification prediction through the established support vector machine classification model. According to the method, the global performance of an exponential function and the local performance of a radial basis function are fully utilized, the model parameters are optimized by adopting a particle swarm optimization algorithm with Gaussian variation, and the overall performance of the support vector machine is improved. The learning and generalization performance of the global index distribution kernel function is higher than that of other single kernel functions, and the performance of the support vector machine of the novel hybrid kernel function is obviously better than that of the support vector machines of other hybrid kernel functions.
Owner:HUAIBEI NORMAL UNIVERSITY
Production method for injection molding machine workpiece production line
InactiveCN102909844BRealize intelligent and fully automated productionRealize automated productionInjection molding machineProduction cycle
Owner:南京塑维网络科技有限公司
Electric power system short-term load prediction method based on hybrid kernel function adaptive fusion
PendingCN111047090AImprove calculation accuracyImprove forecast accuracyForecastingNeural architecturesAlgorithmCubature kalman filter
The invention belongs to the technical field of power systems. The invention discloses an electric power system short-term load prediction method based on hybrid kernel function adaptive fusion. Kernel functions are selected from the local kernel function library and the global kernel function library respectively, weight variables are allocated to form a mixed kernel function, the weight variables and parameters of the kernel functions are put together to be combined into a new parameter state vector, and a nonlinear parameter estimation model is established; and then estimating a parameter state by using high-order cubature Kalman filtering based on a parameter estimation model, so that a local kernel function and a global kernel function can be adaptively fused, and a trained neural network is used to predict a power load. According to the power system short-term load prediction method based on hybrid kernel function adaptive fusion, the optimal neural network hybrid kernel functionfusion coefficient is selected, and the load prediction precision is improved.
Owner:QUZHOU UNIV
Haploid seed classification using single seed near-infrared spectroscopy
ActiveUS20190079006A1Shorten the timeAmount of timeMaterial analysis by optical meansSortingMultivariate statisticalNir spectra
Methods for sorting haploid maize kernels in haploid induction crosses are provided. A method of sorting haploid kernels can include capturing near-infrared (NIR) spectra and applying a general multivariate statistical model to the acquired NIR spectra to discriminate haploid kernels from diploid-hybrid kernels mixed therein. NIR spectra can be collected on a single kernel using a high-throughput apparatus such that the amount of time required to analyze individual kernels can be significantly reduced in comparison to existing NIR technology and in comparison to traditional manual sorting.
Owner:UNIV OF FLORIDA RES FOUNDATION INC
System and method for hybrid kernel- and user-space incremental and full checkpointing
A system includes a multi-process application that runs. A multi-process application runs on primary hosts and is checkpointed by a checkpointer comprised of at least one of a kernel-mode checkpointer module and one or more user-space interceptors providing at least one of barrier synchronization, checkpointing thread, resource flushing, and an application virtualization space. Checkpoints may be written to storage and the application restored from said stored checkpoint at a later time. Checkpointing may be incremental using Page Table Entry (PTE) pages and Virtual Memory Areas (VMA) information. Checkpointing is transparent to the application and requires no modification to the application, operating system, networking stack or libraries. In an alternate embodiment the kernel-mode checkpointer is built into the kernel.
Owner:IBM CORP
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