[go: up one dir, main page]

WO2021019281A1 - Prédiction d'accord de niveau de service (sla) d'un procédé de composition de caractéristique - Google Patents

Prédiction d'accord de niveau de service (sla) d'un procédé de composition de caractéristique Download PDF

Info

Publication number
WO2021019281A1
WO2021019281A1 PCT/IB2019/056504 IB2019056504W WO2021019281A1 WO 2021019281 A1 WO2021019281 A1 WO 2021019281A1 IB 2019056504 W IB2019056504 W IB 2019056504W WO 2021019281 A1 WO2021019281 A1 WO 2021019281A1
Authority
WO
WIPO (PCT)
Prior art keywords
sla
offline
fcs
list
online
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/IB2019/056504
Other languages
English (en)
Inventor
Catherine Truchan
Chunyan Fu
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Telefonaktiebolaget LM Ericsson AB
Original Assignee
Telefonaktiebolaget LM Ericsson AB
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Telefonaktiebolaget LM Ericsson AB filed Critical Telefonaktiebolaget LM Ericsson AB
Priority to PCT/IB2019/056504 priority Critical patent/WO2021019281A1/fr
Priority to EP19780347.1A priority patent/EP4004820A1/fr
Publication of WO2021019281A1 publication Critical patent/WO2021019281A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5061—Partitioning or combining of resources
    • G06F9/5072—Grid computing

Definitions

  • This disclosure relates to predicting the SLA for microservice-based applications, and in particular to predicting the SLA of the feature composition of microservice-based applications.
  • microservices In the microservices paradigm/architecture, applications are composed of smaller independent services that implement different features. For instance, web services are composed of individual operations such as deposit and withdrawal operations in banking. In telecommunications, these features may include call forwarding, conference calling and call display. There is a need to validate the interaction of these features/microservices, which is particularly challenging due to the high frequency of microservice releases. Features are generally added incrementally to an application and when several features are added, there may be resulting interactions that severely damage the system. In particular, the interaction of features may exacerbate the application performance.
  • microservices One difficulty with the interaction of features (microservices) is that such interaction may negatively impact the application performance.
  • CD Continuous Delivery
  • CI Continuous Integration
  • GA Genetic algorithm
  • GA has also been used for optimizing the performance of service composition in a cloud environment.
  • GA has been used for optimizing both service placement on virtual machines (VMs) and service composition graphs in order to maximize the overall quality of service (QoS) of composed service.
  • VMs virtual machines
  • QoS quality of service
  • a method is disclosed to predict the SLA of applications composed of several microservices using Reinforcement Learning (RL)-based Genetic Algorithm (GA).
  • the method includes the utilization of an SLA Prediction Trainer (SPT) that generates a prediction model for the SLA conformance of microservices-based Feature Composition (FC).
  • SPT SLA Prediction Trainer
  • This method improves SLA prediction by combining the offline SLA Conformance Testing (CT) results with the online SLA Conformance Results.
  • the method defines a set of valid Feature Compositions (FCs) using GA selection, crossover and mutation operations augmented by composition policies, maintains a repository of valid FCs, executes offline CTs for all valid FCs, aggregates the FC testing results and sends these results to the SLA Prediction Trainer (SPT), generates a prediction model which updates the Online SLA Predictor (OSP) accordingly.
  • the method includes utilizing an online microservice-based SLA Predictor that predicts SLA conformation of microservices-based Feature Composition (FC).
  • the method improves SLA prediction via input from SPT, feeds the SPT with FC’s offline SLA conformance results and actual resources required which are used for training the SPT to achieve better prediction accuracy, and the method also obtains online input (actual resources and Service Level Indicator (SLI)) from a system monitor.
  • SLI Service Level Indicator
  • a method to validate Feature Composition can be implemented by creating an offline list of valid Feature Compositions (FCs), testing offline the list of valid FCs to determine offline SLA conformance results for each of the FCs of the list of valid FCs, providing the offline SLA conformance results to an offline SLA predictor trainer (SPT) to be used as training data, training the SPT using the training data to create an SLA prediction model, providing the SLA prediction model to one or more online SLA predictors to determine an online SLA prediction result for a real-time requested FC and providing the online SLA conformance result to the offline Feature Composition Generator (FCG), wherein the FCG uses the online SLA conformance result in the updating of the offline list of Feature Compositions (FCs).
  • FCs offline SLA predictor trainer
  • the offline SLA conformance results can be defined as an offline list of SLA compliant FCs and an offline list of SLA non-compliant FCs.
  • the creation of the offline list of valid FCs may include inputting a list of FCs into an FCG.
  • the FCG may utilize a GA to create a list of FCs and applying relevant composition policies to the list of FCs to create the list of valid FCs.
  • the utilization of the GA by the FCG may include performing the GA operations of selection, crossover and/or mutation on the list of FCs.
  • each of the FCs in the offline list of valid FCs is defined as including a feature set, associated configurations, required resources and service level indicators.
  • the testing offline of the list of valid FC includes inputting the list of valid FCs into a Composition Test Manager (CTM), providing, by the CTM, the list of valid FCs to one or more offline Composition Testers (CT) microservices and testing each of the FCs of the list of valid FCs by the one or more CTs to determine offline SLA conformance results for each of the FCs.
  • CTM Composition Test Manager
  • CT Composition Testers
  • FC can be implemented by further including the providing, by the CTM, the offline SLA conformance results for each of the FCs to the FCG and updating, by the FCG, the list of valid FCs based on the offline SLA conformance results.
  • These aspects may also include training the SPT using a machine learning algorithm using the training data to create a prediction model.
  • the machine learning algorithm may be a Recurrent Neural Network (RNN) algorithm.
  • RNN Recurrent Neural Network
  • the method to validate Feature Composition can be implemented by further including allowing or prohibiting the real-time requested FC based on the online SLA conformance prediction provided by the SLA Predictor and the current available resources provided by the System Monitor.
  • the online SLA conformance prediction is tested by the Feature Composition Execution. And based on the execution, the System SLA Analytics will provide the online SLA conformance result that may include an SLA fulfillment status for the real-time requested FC, the actual SLIs and the actual resources used by the real-time requested FC.
  • the FCG may perform the GA operation of mutation on the list of FCs to include actual service level indicators and actual resources during the updating of the list of FCs.
  • the FCG may perform the GA operation of mutation once the difference between the required resources and service level indicators with the actual resources and actual service level indicators meets an established threshold.
  • FIG. 1 is a schematic diagram of an embodiment of a Feature Composition SLA prediction system, according to various exemplary embodiments of the present disclosure.
  • FIG. 2 illustrates an exemplary Feature Composition, according to various exemplary embodiments of the present disclosure.
  • FIG. 3 illustrates an exemplary Feature Composition one-point crossover yielding valid offspring, according to various exemplary embodiments of the present disclosure.
  • FIG. 4 illustrates an exemplary Feature Composition two-point crossover yielding invalid offspring, according to various exemplary embodiments of the present disclosure.
  • FIG. 5 illustrates an exemplary Feature Composition mutation, according to various exemplary embodiments of the present disclosure.
  • FIG. 6 illustrates an exemplary process of offline Feature Composition SLA prediction, according to various exemplary embodiments of the present disclosure.
  • FIG. 7 illustrates an exemplary process of offline Feature Composition mutation, according to various exemplary embodiments of the present disclosure.
  • the embodiments reside primarily in combinations of apparatus components and processing steps related to a real-time SLA predictor to validate the Feature Compositions (FCs). Accordingly, components have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.
  • relational terms such as“first” and“second,”“top” and “bottom,” and the like, may be used solely to distinguish one entity or element from another entity or element without necessarily requiring or implying any physical or logical relationship or order between such entities or elements.
  • the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the concepts described herein.
  • the singular forms“a”,“an” and“the” are intended to include the plural forms as well, unless the context clearly indicates otherwise.
  • the joining term,“in communication with” and the like may be used to indicate electrical or data communication, which may be accomplished by physical contact, induction, electromagnetic radiation, radio signaling, infrared signaling or optical signaling, for example.
  • electrical or data communication may be accomplished by physical contact, induction, electromagnetic radiation, radio signaling, infrared signaling or optical signaling, for example.
  • the term“coupled,”“connected,” and the like may be used herein to indicate a connection, although not necessarily directly, and may include wired and/or wireless connections.
  • W CDMA Wide Band Code Division Multiple Access
  • WiMax Worldwide Interoperability for Microwave Access
  • UMB Ultra Mobile Broadband
  • GSM Global System for Mobile Communications
  • functions described herein as being performed by a wireless device or a network node may be distributed over a plurality of wireless devices and/or network nodes.
  • the functions of the network node and wireless device described herein are not limited to performance by a single physical device and, in fact, can be distributed among several physical devices.
  • an SLA prediction model for predicting the SLA for microservice-based applications is disclosed. These applications are composed of loosely coupled services and their interaction may negatively impact the overall performance of the service.
  • the inventive methods and systems are based on a Reinforcement Learning (RL) based Genetic Algorithm (GA) in order to handle a large number of microservices.
  • RL Reinforcement Learning
  • GA Genetic Algorithm
  • These methods and systems are composed of both an offline and online portion working in conjunction.
  • the offline portion tests the offline SLA conformance results for the selected feature sets and these results serve as training data to create an SLA prediction model.
  • the resulting prediction model is then fed into one or more online SLA predictors.
  • the online SLA conformance results are fed back to the offline portion which may cause the feature sets to mutate, update the SLA conformance testing and improve the offline SLA prediction.
  • the methods and systems of an SLA prediction model as disclosed herein provide various advantages over the prior art.
  • One such advantage includes the GA and reinforcement learning being combined for a long-lived learning mechanism for Feature Compositions (FCs) validation.
  • the reinforcement learning modifies the GA mutation process from a randomly-triggered operation to a feedback-triggered operation which avoids the cost of testing a large number of randomly composed features.
  • the GA is slow to achieve optimal FCs and therefore, to improve the GA performance the methods and systems disclosed herein executes microservice- based offline FC testers in parallel and uses the execution history as input to accelerate the GA evolution. Further, the offline and online feature composition testing are combined. The offline portion accelerates the FC selections while the online portion provides feedback on how the FCs actually perform.
  • the feature configuration, resource usage and service level indicators are added as genes to the Feature Composition (FC) chromosomes to increase the accuracy of the prediction.
  • the GA is selected since it can handle large scale problems and is more scalable than other approaches used to do SLA prediction.
  • relevant feature composition policies are applied to reduce the dimension of the FC set to be tested and predicted. The FC set would otherwise include all possible combinations of the existing features.
  • FIG. 1 a schematic illustration of an embodiment of a system for the prediction of feature composition SLA prediction and is composed of two parts that run in parallel, namely, an offline 101 portion and an online portion 103.
  • the system utilizes a reinforcement learning (RL) based genetic algorithm (GA) for validating new and existing service compositions.
  • RL reinforcement learning
  • GA genetic algorithm
  • the offline portion 101 is responsible for creating and testing an offline subset list of valid feature compositions (FCs) 152 and for the training of an offline SLA predictor trainer (SPT) 116 based on the offline subset list of valid feature compositions 152 post-testing, whereby the SPT 116 creates an offline SLA prediction model 160.
  • the online portion 103 is responsible for evaluating an online SLA prediction result 164 for a real-time requested FC 162 based on the offline SLA prediction model 160 and for providing the online SLA conformance result 156 to the offline portion 101 for inclusion with further creating, testing and training.
  • the offline portion 101 includes an offline feature composition generator (FCG) 102 that is configured to create an offline list of valid feature compositions (FCs) 105.
  • FCG offline feature composition generator
  • the offline list of valid FC 105 is stored in the Feature Composition (FC) Repository 114.
  • the FC Repository 114 is a storage that maintains relative information for FC, e.g. FC population, which includes the associated configurations; SLI, e.g. number of requests per second, latency, throughput, etc.; and expected resources, e.g. compute, storage, as well as the resulting SLA status, e.g. comply or violate.
  • FC is based on a feature set, which includes the added feature configuration 150 which is composed of a feature, its configuration as well as the required resources, as well as any relevant composition policies 110 and system SLA requirements 112.
  • composition policies 112 serve to reduce the list of valid FCs 152 and are defined by the operator. This allows the list of valid FCs 152 to be shortened without affecting the performance of the system 100. For example, for a set of features fl, f2, ... fh, if f2 and f4 are mutually exclusive, there is no need to test and predict SLA conformance for FCs that include both f2 and f4.
  • the FCG initially creates the list, or population, of FCs by applying a genetic algorithm (GA) and which is then restricted by the composition policies 112.
  • GA genetic algorithm
  • the FCG may perform the GA operations of selection, crossover and mutation.
  • the resulting list of valid FCs 152 is defined as a set of features, configurations, expected resources and service level indicators, e.g. number of requests per second, latency, throughput, etc.
  • the FCG inputs an offline subset list of valid FCs 152 to a composition test manager (CTM) 104 which in turn initiates the testing of the offline list of valid FCs 152 by one or more composition testers (CT) 106, 108.
  • This list is not necessarily the entire list, or population, of valid FCs, but new or modified FCs created due to the GA operations of crossover or mutation. For example, if a new feature is added or FCs mutated due to online results, those new FCs would be tested but the remaining unaffected FCs would not.
  • the inclusion of two CTs 106, 108 is illustrative and not meant to be limiting. Those skilled in the art will recognize that multiple CTs may be utilized and are within the scope of the present invention.
  • the FCG 102 After receiving the offline SLA conformance results 158 from the CTM 104, the FCG 102 is configured to provide the offline SLA conformance results 158 to an offline SLA predictor trainer (SPT) 116 to be used as training data.
  • SPT offline SLA predictor trainer
  • the SPT 116 is trained and creates an SLA prediction model 160.
  • a machine leaning algorithm such as, but not limited to, Recurrent Neural Network (RNN), can be used to train the SPT.
  • RNN Recurrent Neural Network
  • the testing resources are released once the testing is complete and the required training resources decrease as the GA model matures with a better SLA prediction accuracy for the FCs.
  • the SPT 116 is configured to provide the SLA prediction model 160 to one or more online SLA predictors 118 located within the online portion 103 to determine an online SLA conformance result 156 for a real-time requested FC SLA prediction 162.
  • the online portion 103 includes one or more SLA predictors 118 that are configured to evaluate whether a real-time online FC request 162 would be comply or not to the SLA based on the offline SLA prediction model 160 and the current available resources 167. If the SLA predictor 118 determines the online FC request 162 would result in an SLA violation, it will reject 122 the FC request 162. If the SLA predictor 118 determines the FC request 162 would result in SLA compliance 164, the FC request will be allowed.
  • a feature composition execution 124 Upon allowance, a feature composition execution 124 will execute the FC request.
  • a system monitor 120 provides online data 166, e.g. the current, or actual, resource usage for the allowed FC request.
  • an online system SLA analytics 126 Upon feature composition execution completion 124, an online system SLA analytics 126 will report an online SLA conformance result 156, that includes an SLA fulfillment status for the real-time requested FC, together with the online data 166, e.g. actual SLI (slF) and actual resources (r’) used by the FC request, to the offline FCG 102.
  • the FCG 102 uses the online SLA conformance results 156 and online SLA data 166 in the updating of the offline list of valid FCs 152.
  • the FCG 102 may perform the GA operation of mutation on the FCs based on both the offline test results and online execution results. For example, for an SLA violation sample, the system SLA analytics 126 notifies the FCG 102 with the actual resources (r’), actual SLI (slF) and SLA result. The FCG 102 will determine if mutation is required and if so, create a new chromosome with the actual values. The FCs are modified/mutated accordingly, and these new FCs will trigger additional offline testing by the CTs 106, 108.
  • the FCG 102 performs the GA operation of mutation once the difference between the required resources and service level indicators with the actual resources and actual service level indicators meets established thresholds.
  • the thresholds are set for the actual resources and SLI for a particular FC in order to avoid continual mutation of FCs by FCG 102.
  • the number of requests per second may be set to 100 requests per second plus/minus 3 requests. In such a situation, if the actual value is less than 97 or greater than 103, then the mutation operation takes place. However, if the actual value is between 97 and 103, the mutation operation will not take place.
  • the online portion 103 includes an FC manager that will not enable FCs unless they have been tested offline.
  • the Feature Composition Generator 102 utilizes a Genetic Algorithm (GA).
  • GA Genetic Algorithm
  • a GA is a process inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection.
  • EA evolutionary algorithms
  • a population of candidate solutions called individuals, creatures, or phenotypes
  • Each candidate solution has a set of properties (its chromosomes or genotype) which can be mutated and altered.
  • the evolution usually starts from a population of individuals, and is an iterative process, with the population in each iteration called a generation.
  • the fitness of every individual in the population is evaluated; the fitness is usually the value of the objective function in the optimization problem being solved.
  • the more fit individuals are stochastically selected from the current population, and each individual's genome is modified, through repetitive application of the mutation, crossover, inversion and selection operators, to form a new generation.
  • the new generation of candidate solutions is then used in the next iteration of the algorithm.
  • the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population.
  • the FCG 102 utilizes the Genetic Algorithm (GA) where genes are defined as the features with associated configurations and required resources as well as the service level indicators (SLI); and the chromosomes are the resulting FCs. These are of variable length but are bound by the number of features/microservices per FC.
  • the population is a set of chromosomes, that is, the FC repository. This repository may grow as new features are added, and new FCs defined (i.e. add new features to the existing FCs). The population remains relatively constant since it is restricted by the composition policies.
  • the FCG 102 performs the FC selection, crossover and mutation operations.
  • the crossover serves to generate new offspring in the FC population.
  • the crossover operation is not randomly chosen but is restricted by the composition policies defined by an operator.
  • the mutations occur when the FCs are transformed with the actual resources and actual service level indicators for actual feature with actual configuration. These are existing FCs updated with offline CTs and real-time/online results.
  • the fitness function is the resulting accuracy of the offline SLA prediction.
  • the stopping criteria is when the accuracy for the offline FC SLA prediction is deemed acceptable (i.e. SLA conformance).
  • FCs Feature Compositions
  • the FCs are formed based on a combination of microservice/features, resources and SLI, in view of relative composition policies.
  • FIG. 3 provides an example of a one-point crossover operation of a pair of parent FCs.
  • the rightmost part/genes are swapped between the FC parents FCi and FC2 to produce the new FCi and FC2 offspring.
  • the parent FCi originally includes A2, B7, C3, R4 and Si as genes and the parent FC2 originally includes Ai, B2, Cs, Ri and S7 as genes.
  • offspring FCi includes A2, B7, Cs, Ri and S7 as genes and the offspring FC2 includes Ai, B2, C3, R4 and Si as genes.
  • the use of a one-point crossover operation is illustrative and not meant to be limiting as there are several types of crossover operators, e.g. one-point, multi-point, uniform, etc. Those skilled in the art will recognize the other types of crossover operation maybe used and are within the scope of the present invention.
  • the resulting offspring FCs are evaluated in light of relevant composition policies.
  • a composition policy restricts having feature A2 with B2.
  • the middle part/genes are swapped, between the FC parents FCi and FC2 to produce the new FCi and FC2 offspring.
  • the parent FCi originally includes A2, B7, C3, R4 and Si as genes and the parent FC2 originally includes Ai, B2, Cs, Ri and S7 as genes.
  • offspring FCi includes A2, B2, Cs, R4 and Si as genes and the offspring FC2 includes Ai, B7, C3, Ri and S7 as genes.
  • FCi and FC2 are compared to the composition policy, FCi is determined to be invalid since A2 and B2 cannot be combined.
  • the mutation of FCs is performed.
  • simple replacement of one or more genes of a FC with genes obtained offline and/or online such as the replacement of the estimated resources and SLI with the actual resources and SLI values obtained from the offline CTs and online SLA Analytics.
  • FIG. 5 provides an example of a mutation operation of a FC.
  • the original FC includes A2, B7, C3, R4 and Si as genes.
  • the resulting FC includes A2, B7, C3, R3 and Ss as genes.
  • the use of a simple replacement mutation operation is illustrative and not meant to be limiting as there are several types of crossover operators, e.g.
  • FIG. 6 illustrates an exemplary offline FC SLA prediction procedure.
  • the FC SLA prediction procedure may be utilized when there is an addition of a new feature/microservice to an existing service.
  • the operations shown in the exemplary procedure of FIG. 6 are labelled numerically, but this numbering is used only to facilitate clarity in the following description. Moreover, the numerical order is merely exemplary, and the order of the various operations can be rearranged in certain embodiments. Any dashed lines indicate optional operations whose performance may depend on one or more conditions.
  • an add feature request is sent to the FCG.
  • the request includes the added feature along with any associated configuration and required resources.
  • the FCG selects FCs based of the FC composition policies and adds the new feature (i.e. gene) with required resources to the selected FCs. This may also initiate crossover procedure of selected FCs.
  • the new FCs are stored in the FC repository.
  • the FCG sends a test FC request the Composition Test Manager (CTM) for the new offspring FCs.
  • CTM defines the FC tests to be executed based on the service level indicators (SLI) and feature resources.
  • Operation 2 refers to the first test FC request sent relating to the first new offspring FC.
  • Operation 3 refers to the N th test FC request relating to the N th new offspring FC, in the event there are two or more new offspring FCs.
  • the test FC requests initiate the testing of the new offspring FCs by offline Composition Testers (CTs).
  • CTs test the offline SLA conformance, e.g. throughput, of these FCs.
  • the offline SLA conformance results may be either an SLA conformance, i.e. SLA compliant, or an SLA violation, i.e.
  • the SLIs may specify performance metrics such as throughput or latency.
  • Operation 4 refers to the request to initiate the testing of the first new offspring FC by a first CTi.
  • Operation 5 refers to the request to initiate the testing of the N th new offspring FC by a Nth CTN, in the event there are two or more new offspring FCs.
  • the offline SLA conformance results for each of the new offline FCs are sent to the CTM from the CTs, e.g. CTi ... CTN.
  • the offline SLA conformance results for each of the new offline FCs are sent to the FCG from the CTM.
  • the FCG updates the FCs in the repository with offline SLA conformance results. These results are feed to the SPT which in turn, updates the online SLA predictor.
  • the resulting offline SLA conformance result is provided to the SLA Prediction Trainer (SPT) to serve as training data for the SPT.
  • SPT SLA Prediction Trainer
  • the SPT leams the FCs.
  • a machine learning algorithm is used to train the SPT.
  • a user Following the update of the online SLA predictor, a user’s request for execution of an application/service will be translated into a prediction request sent to the SLA predictor for a Feature Composition of microservices (e.g. FCi).
  • a Feature Composition of microservices e.g. FCi
  • FCi SLA Results and FC4_SLA_Results the training data
  • FCi SLA Results and FC4_SLA_Results the current actual resources and SLI
  • the SLA Predictor predicts the probability of SLA violation. If no violation is predicted, the service may execute.
  • the Service executes, and the Systems SLA Analytics gathers the actual resources, SLI and SLA conformance results. It sends this information back to the FCG which may in turn, decide to mutate the FC (e.g. FCi). This initiates new CTs that generate new training data for the SLA Prediction Trainer.
  • FCi mutate the FC
  • FIG. 7 illustrates an exemplary FC mutation procedure.
  • the FC mutation procedure may be utilized when online SLA conformance results differ from offline results.
  • the operations shown in the exemplary procedure of FIG. 7 are labelled numerically, but this numbering is used only to facilitate clarity in the following description. Moreover, the numerical order is merely exemplary, and the order of the various operations can be rearranged in certain embodiments. Any dashed lines indicate optional operations whose performance may depend on one or more conditions.
  • the online system SLA analytics sends the online SLA conformance results for a real-time requested FC to the FCG.
  • the online SLA conformance results may include the actual configuration, actual resources and actual SLI used by the real-time requested FC. This may initiate a mutation of the FC by the FCG.
  • operation 1 includes threshold values that are used to determine whether a mutation is needed or not required in order to avoid continuous updating/mutating offline FCs due to slight variations in the actual parameters and SLA conformance. In such embodiments, a mutation is not performed when the variation, or difference, between the offline SLA conformance results and the online SLA conformance results are minimal.
  • FCG sends a test FC request to the Composition Test Manager (CTM) for the new mutated FC.
  • CTM Composition Test Manager
  • the CTM will then define the FC tests to be executed based on the service level indicators (SLI) and feature resources for the mutated FC.
  • SLI service level indicators
  • the concepts described herein may be embodied as a method, data processing system, and/or computer program product. Accordingly, the concepts described herein may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects all generally referred to herein as a “circuit” or“module.” Furthermore, the disclosure may take the form of a computer program product on a tangible computer usable storage medium having computer program code embodied in the medium that can be executed by a computer. Any suitable tangible computer readable medium may be utilized including hard disks, CD-ROMs, electronic storage devices, optical storage devices, or magnetic storage devices.
  • These computer program instructions may also be stored in a computer readable memory or storage medium that can direct a computer or other
  • the computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
  • Computer program code for carrying out operations of the concepts described herein may be written in an object-oriented programming language such as Java® or C++.
  • the computer program code for carrying out operations of the disclosure may also be written in conventional procedural programming languages, such as the "C" programming language.
  • Machine learning models can be implemented using for example, R or Python libraries.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer.
  • the remote computer may be connected to the user's computer through a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
  • LAN local area network
  • WAN wide area network
  • Internet Service Provider for example, AT&T, MCI, Sprint, EarthLink, MSN, GTE, etc.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Software Systems (AREA)
  • Biophysics (AREA)
  • General Engineering & Computer Science (AREA)
  • Evolutionary Computation (AREA)
  • Mathematical Physics (AREA)
  • Artificial Intelligence (AREA)
  • General Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Evolutionary Biology (AREA)
  • Biomedical Technology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Physiology (AREA)
  • Genetics & Genomics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

L'invention concerne un procédé de validation de composition de caractéristique (FC). Selon un aspect, le procédé comprend les étapes consistant à : utiliser un générateur de composition de caractéristique (FCG) hors ligne qui utilise un algorithme génétique (GA) pour créer une liste hors ligne de compositions de caractéristiques (FC) valides ; tester hors ligne la liste de FC valides de façon à déterminer des résultats de conformité de SLA hors ligne pour chacune des FC de la liste de FC valides ; communiquer à un dispositif de formation de prédicteur de SLA (SPT) hors ligne les résultats de conformité de SLA hors ligne devant être utilisés à titre de données de formation ; former le SPT en utilisant les données de formation de façon à créer un modèle de prédiction de SLA ; communiquer le modèle de prédiction de SLA à un ou plusieurs prédicteurs de SLA en ligne de façon à déterminer un résultat de prédiction de SLA en ligne pour une FC demandée en temps réel ; et communiquer le résultat de conformité de SLA en ligne au FCG hors ligne, le FCG utilisant le résultat de conformité de SLA en ligne lors de la mise à jour de la liste hors ligne de compositions de caractéristiques (FC).
PCT/IB2019/056504 2019-07-30 2019-07-30 Prédiction d'accord de niveau de service (sla) d'un procédé de composition de caractéristique Ceased WO2021019281A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
PCT/IB2019/056504 WO2021019281A1 (fr) 2019-07-30 2019-07-30 Prédiction d'accord de niveau de service (sla) d'un procédé de composition de caractéristique
EP19780347.1A EP4004820A1 (fr) 2019-07-30 2019-07-30 Prédiction d'accord de niveau de service (sla) d'un procédé de composition de caractéristique

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/IB2019/056504 WO2021019281A1 (fr) 2019-07-30 2019-07-30 Prédiction d'accord de niveau de service (sla) d'un procédé de composition de caractéristique

Publications (1)

Publication Number Publication Date
WO2021019281A1 true WO2021019281A1 (fr) 2021-02-04

Family

ID=68109386

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/IB2019/056504 Ceased WO2021019281A1 (fr) 2019-07-30 2019-07-30 Prédiction d'accord de niveau de service (sla) d'un procédé de composition de caractéristique

Country Status (2)

Country Link
EP (1) EP4004820A1 (fr)
WO (1) WO2021019281A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114356564A (zh) * 2021-12-29 2022-04-15 四川大学 一种集成服务资源的系统

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170237792A1 (en) * 2016-02-15 2017-08-17 NETFLIX, Inc, Feature Generation for Online/Offline Machine Learning
EP3340052A1 (fr) * 2016-12-22 2018-06-27 Juniper Networks, Inc. Mise à l'échelle automatique d'applications de microservices
US20180300630A1 (en) * 2017-04-17 2018-10-18 SparkCognition, Inc. Cooperative execution of a genetic algorithm with an efficient training algorithm for data-driven model creation
US20180314938A1 (en) * 2017-04-28 2018-11-01 SparkCognition, Inc. Pre-processing for data-driven model creation

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170237792A1 (en) * 2016-02-15 2017-08-17 NETFLIX, Inc, Feature Generation for Online/Offline Machine Learning
EP3340052A1 (fr) * 2016-12-22 2018-06-27 Juniper Networks, Inc. Mise à l'échelle automatique d'applications de microservices
US20180300630A1 (en) * 2017-04-17 2018-10-18 SparkCognition, Inc. Cooperative execution of a genetic algorithm with an efficient training algorithm for data-driven model creation
US20180314938A1 (en) * 2017-04-28 2018-11-01 SparkCognition, Inc. Pre-processing for data-driven model creation

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114356564A (zh) * 2021-12-29 2022-04-15 四川大学 一种集成服务资源的系统

Also Published As

Publication number Publication date
EP4004820A1 (fr) 2022-06-01

Similar Documents

Publication Publication Date Title
Han et al. Slice as an evolutionary service: Genetic optimization for inter-slice resource management in 5G networks
Andrade et al. The multi-parent biased random-key genetic algorithm with implicit path-relinking and its real-world applications
US11347631B1 (en) Method, apparatus, and computer program product for predictive API test suite selection
Yala et al. Latency and availability driven VNF placement in a MEC-NFV environment
CN104461728B (zh) 迁移事件调度管理的计算机系统、介质和方法
CN110024340B (zh) 经由使用预测在可见性网络中进行近似均匀负载平衡
US11102081B1 (en) Quantitative network testing framework for 5G and subsequent generation networks
López-Pires et al. Many-objective virtual machine placement
US20190180039A1 (en) Systems and Methods for Dynamic Application Management
CN110796399B (zh) 基于区块链的资源分配方法以及装置
Tiwari et al. Performance assessment of the hybrid archive-based micro genetic algorithm (AMGA) on the CEC09 test problems
Pascual et al. Self-adaptation of mobile systems driven by the common variability language
US11915107B2 (en) Software build management using machine learning model
CN111340453A (zh) 联邦学习开发方法、装置、设备及存储介质
Saber et al. A hybrid algorithm for multi-objective test case selection
Shi et al. Location-aware and budget-constrained application replication and deployment in multi-cloud environment
Ouedraogo et al. A cost-effective approach for end-to-end QoS management in NFV-enabled IoT platforms
Pereira et al. A zero-shot learning approach for task allocation optimization in cyber-physical systems
WO2021019281A1 (fr) Prédiction d'accord de niveau de service (sla) d'un procédé de composition de caractéristique
US11354218B2 (en) Generation of optimal program variation
US20160283566A1 (en) Analyzing sorted mobile application operational state sequences based on sequence metrics
Javed et al. Trustworthy reputation for federated learning in o-ran using blockchain and smart contracts
Liang et al. Rlf: Directed fuzzing based on deep reinforcement learning
US8498957B2 (en) Optimal multi-factor evaluation in computing systems
CN115150092B (zh) 业务子链的创建方法、装置、电子设备及计算机存储介质

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19780347

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 2019780347

Country of ref document: EP

Effective date: 20220228