UTILIZING A MACHINE LEARNING MODEL TO MIGRATE A SYSTEM TO A CLOUD COMPUTING ENVIRONMENT

A device may receive logs and files associated with a system to be migrated to a cloud computing environment, and may determine workload data associated of the system. The device may derive a data lineage for source data and target data, and may assess a utilization pattern of the system. The device may process the workload data, the data lineage, and data identifying utilization of a distributed computing feature of the system, with a model, to label utilization features and to recommend a cloud architecture. The device may process the workload data, the data lineage, and the data identifying utilization, with a natural language processing model, to determine a cost of migrating the system. The device may process the labelled utilization features, the cloud architecture, and the cost, with a Q-matrix model, to determine migration actions for migrating the system, and may perform actions based on the migration actions.

BACKGROUND

A system, such as a legacy statistical analysis system (SAS), may operate more efficiently in a cloud computing environment, may be terminated due to being obsolete, and/or the like.

SUMMARY

Some implementations described herein relate to a method. The method may include receiving logs and files associated with a system to be migrated to a cloud computing environment, and determining, based on the logs and the files, workload data identifying workload patterns of the system. The method may include deriving a data lineage for source data and target data included the logs and the files, and assessing a utilization pattern of the system based on the logs and the files to determine whether a distributed computing feature of the system is being utilized. The method may include processing the workload data, the data lineage, and data identifying utilization of the distributed computing feature, with a machine learning model, to label utilization features of the system and to recommend a cloud architecture. The method may include processing the workload data, the data lineage, and the data identifying utilization of the distributed computing feature, with a natural language processing model, to determine a cost of migrating the system to the cloud computing environment. The method may include processing the labelled utilization features, the cloud architecture, and the cost, with a Q-matrix model, to determine migration actions for migrating the system to the cloud computing environment, and performing one or more actions based on the migration actions.

Some implementations described herein relate to a device. The device may include one or more memories and one or more processors coupled to the one or more memories. The one or more processors may be configured to receive logs and files associated with a system to be migrated to a cloud computing environment, and determine, based on the logs and the files, workload data identifying workload patterns of the system. The one or more processors may be configured to derive a data lineage for source data and target data included the logs and the files, and assess a utilization pattern of the system based on the logs and the files to determine whether a distributed computing feature of the system is being utilized. The one or more processors may be configured to train a machine learning model with a training dataset to generate a trained machine learning model, and process the workload data, the data lineage, and data identifying utilization of the distributed computing feature, with the trained machine learning model, to label utilization features of the system and to recommend a cloud architecture. The one or more processors may be configured to process the workload data, the data lineage, and the data identifying utilization of the distributed computing feature, with a natural language processing model, to determine a cost of migrating the system to the cloud computing environment. The one or more processors may be configured to process the labelled utilization features, the cloud architecture, and the cost, with a Q-matrix model, to determine migration actions for migrating the system to the cloud computing environment, and perform one or more actions based on the migration actions.

Some implementations described herein relate to a non-transitory computer-readable medium that stores a set of instructions for a device. The set of instructions, when executed by one or more processors of the device, may cause the device to determine, based on logs and files associated with a system to be migrated to a cloud computing environment, workload data identifying workload patterns of the system, and derive a data lineage for source data and target data included the logs and the files. The set of instructions, when executed by one or more processors of the device, may cause the device to assess a utilization pattern of the system based on the logs and the files to determine whether a distributed computing feature of the system is being utilized, and process the workload data, the data lineage, and data identifying utilization of the distributed computing feature, with a machine learning model, to label utilization features of the system and to recommend a cloud architecture. The set of instructions, when executed by one or more processors of the device, may cause the device to process the workload data, the data lineage, and the data identifying utilization of the distributed computing feature, with a natural language processing model, to determine a cost of migrating the system to the cloud computing environment. The set of instructions, when executed by one or more processors of the device, may cause the device to process the labelled utilization features, the cloud architecture, and the cost, with a Q-matrix model, to determine migration actions for migrating the system to the cloud computing environment, and perform one or more actions based on the migration actions.

DETAILED DESCRIPTION

Determining whether a system can be viably migrated to a cloud computing environment requires an assessment that is manually impossible to perform due to lack of transparency of usage by various users and unavailability of centralized repository where system artifacts are hosted. The system may have been executing for years, may have no centralized governance, and may include an outdated infrastructure with resources requiring various skillsets resulting in high capital and operational expenditures. Furthermore, comparing a cost of operating the system and a value delivered by the system may be impossible since the system may perform data processing rather than generate insights. The system may be difficult or impossible to monitor due to a distributed nature of the system, the lack of centralized governance, and system developers moving into different roles or jobs. Therefore, current techniques for assessing a system and/or migrating a system to a cloud computing environment consume computing resources (e.g., processing resources, memory resources, communication resources, and/or the like), networking resources, and/or the like associated with failing to properly assess whether the system can be viably migrated to a cloud computing environment, spending time and money and other resources on migration of a non-functional system to the cloud computing environment, attempting and failing to migrate the system to the cloud computing environment, and/or the like.

Some implementations described herein relate to a migration system that utilizes a machine learning model to migrate a system to a cloud computing environment. For example, the migration system may receive logs and files associated with a system to be migrated to a cloud computing environment, and may determine, based on the logs and the files, workload data identifying workload patterns of the system. The migration system may derive a data lineage for source data and target data included the logs and the files, and may assess a utilization pattern of the system based on the logs and the files to determine whether a distributed computing feature of the system is being utilized. The migration system may train a machine learning model with a training dataset to generate a trained machine learning model, and may process the workload data, the data lineage, and data identifying utilization of the distributed computing feature, with the trained machine learning model, to label utilization features of the system and to recommend a cloud architecture. The migration system may process the workload data, the data lineage, and the data identifying utilization of the distributed computing feature, with a natural language processing model, to determine a cost of migrating the system to the cloud computing environment. The migration system may process the labelled utilization features, the cloud architecture, and the cost, with a Q-matrix model, to determine migration actions for migrating the system to the cloud computing environment, and may perform one or more actions based on the migration actions.

In this way, the migration system utilizes a machine learning model to migrate a system to a cloud computing environment. The migration system may analyze artifacts of the system across dimensions, such as workload, complexity, lineage, consumption, utilization, and/or the like, and may generate insights into an overall utilization of the system, resource consumption by the system, penetration of the system within and across organizations, distribution of system workloads, and/or the like. The migration system may provide a detailed mapping of system constructs to system workload (e.g., data management, reporting, analytics, machine learning, and/or the like), and may determine an architecture for the cloud computing environment. The migration system may generate a roadmap for migrating the system to the cloud computing environment, and may migrate the system to the architecture of the cloud computing environment based on the roadmap. This, in turn, conserves computing resources, networking resources, and/or the like that would otherwise have been consumed in failing to accurately assess whether the system can be viably migrated to a cloud computing environment, spending time and money on migration of a non-functional system to the cloud computing environment, attempting and failing to migrate the system to the cloud computing environment, and/or the like.

FIGS.1A-1Hare diagrams of an example100associated with utilizing a machine learning model to migrate a system to a cloud computing environment. As shown inFIGS.1A-1H, example100includes a migration system associated with a server device. The migration system may include a system that utilizes a machine learning model to migrate a system to a cloud computing environment. Further details of the migration system and the server device are provided elsewhere herein.

As shown inFIG.1A, and by reference number105, the migration system may receive logs and files associated with a system to be migrated to a cloud computing environment. For example, a system, such as a legacy SAS, may operate more efficiently in a cloud computing environment, may be terminated due to being obsolete, and/or the like. The migration system may determine whether the system may be migrated to a cloud computing environment and function correctly in the cloud computing environment. The migration system may receive the logs and the files associated with the system from one or more server devices executing the system, one or more data structures storing the system, and/or the like. In some implementations, the migration system may continuously receive the logs and the files from the server device, may periodically receive the logs and the files from the server device, may receive the logs and the files based on requesting the logs and the files from the server device, and/or the like.

The logs may include structured data, semi-structured data, and unstructured data associated with the system. In some implementations, the logs may include load sharing facility (LSF) logs, workspace server logs, store process logs, batch server logs, and/or the like. The files may include execution log files associated with the system. In some implementations, the files may include data identifying file names, file paths, execution times of the files, users of the files, and/or the like. In some implementations, the migration system may merge the logs and the files together based on path levels associated with the logs and the files.

As further shown inFIG.1A, and by reference number110, the migration system may determine, based on the logs and the files, workload data identifying workload patterns of the system. For example, when determining the workload data, the migration system may parse the logs and the files to identify procedures and functions performed by the system, and may determine the workload data identifying the workload patterns of the system based on the procedures and the functions performed by the system. In some implementations, the logs and the files may include names of the procedures and the functions performed by the system, and the migration system may parse the logs and the files to identify the names of the procedures and the functions performed by the system. The migration system may identify, in the parsed logs and the parsed files, workload patterns associated with the procedures and the function performed by the system, and may determine the workload data based on the workload patterns associated with the procedures and the function performed by the system.

As further shown inFIG.1A, and by reference number115, the migration system may store the logs, the files, and the workload data in a data structure associated with the migration system. For example, the migration system may maintain a data structure (e.g., a database, a table, a list, and/or the like), such as an analytical base table that stores semi-structured data and unstructured data (e.g., the logs and the files), structured data (e.g., the workload data) derived from the semi-structured data and the unstructured data, and/or the like. The migration system may utilize the analytical base table to determine a migration recommendation. In some implementations, the migration system may store the logs, the files, and the workload data in the analytical base table.

As shown inFIG.1B, and by reference number120, the migration system may derive a data lineage for source data and target data included the logs and the files. For example, when deriving the data lineage for the source data and the target data included the logs and the files, the migration system may parse the logs and the files to identify the source data and the target data, and may derive the data lineage based on the source data and the target data. In some implementations, the source data and the target data may be associated with an execution process of the system, and migration system may determine the data lineage based on the execution process of the system.

As further shown inFIG.1B, and by reference number125, the migration system may store the data lineage in the data structure. For example, the migration system may utilize the data structure (e.g., the analytical base table) to store structured data (e.g., the data lineage) derived from the semi-structured data and the unstructured data (e.g., the logs and the files). In some implementations, the migration system may store the data lineage in the analytical base table.

As shown inFIG.1C, and by reference number130, the migration system may assess a utilization pattern of the system based on the logs and the files to determine whether a distributed computing feature of the system is being utilized. For example, when assessing the utilization pattern of the system based on the logs and the files to determine whether the distributed computing feature of the system is being utilized, the migration system may parse the logs and the files to identify the utilization pattern of the system, and may determine whether the distributed computing feature of the system is being utilized based on the utilization pattern. In some implementations, the migration system may determine, based on the logs and the files, whether the system is optimally utilizing the distributed computing feature. The distributed computing feature may cause the system to distribute tasks among multiple computers on a network to enable workload balancing, accelerated processing, job scheduling, and/or the like.

As further shown inFIG.1C, and by reference number135, the migration system may store data identifying utilization of the distributed computing feature in the data structure. For example, the migration system may utilize the data structure (e.g., the analytical base table) to store structured data (e.g., the data identifying utilization of the distributed computing feature) derived from the semi-structured data and the unstructured data (e.g., the logs and the files). In some implementations, the migration system may store the data identifying utilization of the distributed computing feature in the analytical base table.

As shown inFIG.1D, and by reference number140, the migration system may process the workload data, the data lineage, and the data identifying utilization of the distributed computing feature, with a machine learning model, to label utilization features of the system and to recommend a cloud architecture. For example, the migration system may include a machine learning that automatically labels utilization features of the system (e.g., which may prevent logging shortcomings) and provides insights for a cloud architecture of the cloud computing environment. In some implementations, the migration system may train the machine learning model with a training dataset (e.g., to generate a trained machine learning model) and prior to processing the workload data, the data lineage, and data identifying utilization of the distributed computing feature with the machine learning model, and may test the machine learning model with a test dataset and prior to processing the workload data, the data lineage, and data identifying utilization of the distributed computing feature with the machine learning model. Further details of training and testing the machine learning model are provided below in connection withFIGS.1E and2. In some implementations, the migration system may process the workload data, the data lineage, and the data identifying utilization of the distributed computing feature, with the trained machine learning model, to label the utilization features of the system and to recommend the cloud architecture.

In some implementations, the machine learning model may include a linear regression model or a reinforcement model. A linear regression model may include a model that assumes a linear relationship between input variables (x) and a single output variable (y). More specifically, that the output variable can be calculated from a linear combination of the input variables. When there is a single input variable, the model is referred to as a simple linear regression model. When there are multiple input variables, the model is referred to as a multiple linear regression model. Different techniques can be used to prepare or train the linear regression model from data, such as ordinary least squares. A linear regression model prepared from ordinary least squares may be referred to as an ordinary least squares linear regression model or a least squares regression model. A reinforcement model may include a machine learning model associated with how intelligent agents ought to take actions in an environment in order to maximize a notion of a cumulative reward. A reinforcement model differs from a supervised learning model since the reinforcement model does not require labelled input/output pairs and does not require sub-optimal actions to be explicitly corrected. Instead, the reinforcement model attempts to identify a balance between exploration (of uncharted territory) and exploitation (of current knowledge).

FIG.1Edepicts an example process of training the machine learning model to label utilization features of the system and to recommend a cloud architecture. Further details of training the machine learning model are provided below in connection withFIG.2. As shown inFIG.1E, the migration system may include a dataset (e.g., the analytical base table) that the migration system utilizes to generate a training dataset and a test dataset. As shown at step1, the migration system may cause a portion of data from the dataset (e.g., less than five percent) to be labelled for the training dataset. A shown at step2, the migration system may utilize the remaining data of the dataset (e.g., the unlabeled data) for the test dataset. As shown at step3, the migration system may train and/or retrain the machine learning model based on the training dataset and to generate a trained machine learning model. As shown at step4, the migration system may process the test dataset, with the trained machine learning model, to obtain predictions for the test dataset.

As shown at step5ofFIG.1E, the migration system may perform a query strategy for determining whether to accept or reject the predictions obtained for the test dataset. In some implementations, the query strategy may include a Gaussian process-based query strategy. The query strategy may accept predictions when the predictions have high confidence levels (e.g., low generalization errors), and may reject predictions when the predictions have low confidence levels (e.g., high generalization errors). The migration system may provide the accepted predictions to the training dataset, which may improve the training dataset and performance of the trained machine learning model. The migration system may provide the rejected predictions to the test dataset.

As shown inFIG.1F, and by reference number145, the migration system may process the workload data, the data lineage, and the data identifying utilization of the distributed computing feature, with a natural language processing model, to determine a cost of migrating the system to the cloud computing environment. For example, when processing the workload data, the data lineage, and the data identifying utilization of the distributed computing feature, with the natural language processing model, to determine the cost of migrating the system to the cloud computing environment, the migration system may identify products utilized by the system based on the workload data, the data lineage, and the data identifying utilization of the distributed computing feature. The migration system may generate a mapping of the products utilized by the system and products available in the cloud computing environment, and may determine the cost (e.g., a bill of material) of migrating the system to the cloud computing environment based on the mapping.

In some implementations, the migration system may utilize the natural language processing model to analyze the workload data, the data lineage, and the data identifying utilization of the distributed computing feature to identify the products utilized by the system, and to map the products utilized by the system with the products available in the cloud computing environment. The migration system may utilize the mapping of the products utilized by the system and the products available in the cloud computing environment to determine the cost of migrating the system to the cloud computing environment, recommendations for removal of unutilized products from the migration (e.g., which may reduce licensing costs), recommendations for addition of products for the migration, and/or the like.

As shown inFIG.1G, and by reference number150, the migration system may process the labelled utilization features, the cloud architecture, and the cost, with a Q-matrix model, to determine migration actions for migrating the system to the cloud computing environment. For example, when processing the labelled utilization features, the cloud architecture, and the cost, with the Q-matrix model, to determine the migration actions, the migration system may assign complexity scores to the labelled utilization features (e.g., system files) based on the cloud architecture and the cost, and may assign actions based on the complexity scores assigned to the labelled utilization features. The migration system may create a reward matrix based on the complexity scores assigned to the labelled utilization features and based on the assigned actions. When creating the reward matrix, the migration system may create a bucket of a complexity range based on the complexity scores, may calculate weights for different steps in the migration process, and may create the reward matrix based transitions of the steps. The migration system may create, based on the reward matrix, a Q-matrix that maps the assigned actions and states.

When creating the Q-matrix, the migration system may map the assigned actions to migration actions, may create the Q-matrix with the mapping of the migration actions and the states, and may train the Q-matrix. The migration system may utilize the Q-matrix to determine the migration actions. A Q-matrix is a square matrix whose associated linear complementarity problem LCP(M, q) has a solution for every vector q.

As shown inFIG.1H, and by reference number155, the migration system may perform one or more actions based on the migration actions. In some implementations, performing the one or more actions includes the migration system providing the migration actions for display. For example, the migration system may provide the migration actions for display to a user of the migration system. The user may then manage the migration actions based on the display. In this way, the migration system conserves computing resources, networking resources, and/or the like that would otherwise have been consumed in failing to accurately assess whether the system can be viably migrated to a cloud computing environment.

In some implementations, performing the one or more actions includes the migration system generating a sizing template based on the migration actions and creating an architecture for the cloud computing environment based on the sizing template. For example, the migration system may generate the sizing template based on the migration actions, the workload data, the data lineage, and the data identifying utilization of the distributed computing feature. The migration system may utilize the sizing template to create the architecture (e.g., a blueprint) for the cloud computing environment and data identifying integration with various external services, applications, databases, and/or the like. In this way, the migration system conserves computing resources, networking resources, and/or the like that would otherwise have been consumed in spending time and money on migration of a non-functional system to the cloud computing environment.

In some implementations, performing the one or more actions includes the migration system determining complexities associated with migrating the system based on the migration actions and generating a migration roadmap based on the complexities. For example, the migration system may populate a template with the determined complexities (e.g., determined by the Q-matrix model) associated with migrating the system, and may utilize the template to identify inventor and effort estimates and to generate the migration roadmap, a team construct, and a timeline migrating the system to the cloud computing environment. In this way, the migration system conserves computing resources, networking resources, and/or the like that would otherwise have been consumed in attempting and failing to migrate the system to the cloud computing environment.

In some implementations, performing the one or more actions includes the migration system determining data groups of the system based on the migration actions and migrating data of the system to the cloud computing environment based on the data groups. For example, the migration system may determine the data groups of the system based on workload type, compatibility, dependencies, and/or the like associated with the data of the system. The migration system may automatically or semi-automatically migrate the data of the system to the cloud computing environment based the data groups, and may validate the migration of the data to the cloud computing environment. In this way, the migration system conserves computing resources, networking resources, and/or the like that would otherwise have been consumed in failing to accurately assess whether the system can be viably migrated to a cloud computing environment.

In some implementations, performing the one or more actions includes the migration system receiving a change to one of the migration actions and modifying the one of the migration actions based on the change. For example, the migration system may receive a change to one of the migration actions from a user of the migration system. The change may indicate that only a portion of a product of the system is to be migrated rather than the entire product. The migration system may update the migration action so that only the portion of the product is migrated during the migration of the system to the cloud computing environment. In this way, the migration system conserves computing resources, networking resources, and/or the like that would otherwise have been consumed in spending time and money on migration of a non-functional system to the cloud computing environment, attempting and failing to migrate the system to the cloud computing environment, and/or the like.

In some implementations, performing the one or more actions includes the migration system retraining the machine learning model based on the migration actions. For example, the migration system may utilize the migration actions as additional training data for retraining the machine learning model, thereby increasing the quantity of training data available for training the machine learning model. Accordingly, the migration system may conserve computing resources associated with identifying, obtaining, and/or generating historical data for training the machine learning model relative to other systems for identifying, obtaining, and/or generating historical data for training machine learning models.

In this way, the migration system utilizes a machine learning model to migrate a system to a cloud computing environment. The migration system may analyze artifacts of the system across dimensions, such as workload, complexity, lineage, consumption, utilization, and/or the like, and may generate insights into an overall utilization of the system, resource consumption by the system, penetration of the system within and across organizations, distribution of system workloads, and/or the like. The migration system may provide a detailed mapping of system constructs to system workload (e.g., data management, reporting, analytics, machine learning, and/or the like), and may determine an architecture for the cloud computing environment. The migration system may generate a roadmap for migrating the system to the cloud computing environment, and may migrate the system to the architecture of the cloud computing environment based on the roadmap. This, in turn, conserves computing resources, networking resources, and/or the like that would otherwise have been consumed in failing to accurately assess whether the system can be viably migrated to a cloud computing environment, spending time and money on migration of a non-functional system to the cloud computing environment, attempting and failing to migrate the system to the cloud computing environment, and/or the like. Furthermore, the migrations system may significantly reduce a time required (e.g., by three to four months) for planning a migration and migration a system to a cloud computing environment, and may significantly reduce (e.g., by more than ninety percent) a workload of an entity post migration and transition to the cloud computing environment.

As indicated above,FIGS.1A-1Hare provided as an example. Other examples may differ from what is described with regard toFIGS.1A-1H. The number and arrangement of devices shown inFIGS.1A-1Hare provided as an example. In practice, there may be additional devices, fewer devices, different devices, or differently arranged devices than those shown inFIGS.1A-1H. Furthermore, two or more devices shown inFIGS.1A-1Hmay be implemented within a single device, or a single device shown inFIGS.1A-1Hmay be implemented as multiple, distributed devices. Additionally, or alternatively, a set of devices (e.g., one or more devices) shown inFIGS.1A-1Hmay perform one or more functions described as being performed by another set of devices shown inFIGS.1A-1H.

FIG.2is a diagram illustrating an example200of training and using a machine learning model for labeling utilization features of the system and recommending a cloud architecture. The machine learning model training and usage described herein may be performed using a machine learning system. The machine learning system may include or may be included in a computing device, a server, a cloud computing environment, and/or the like, such as the migration system described in more detail elsewhere herein.

As shown by reference number205, a machine learning model may be trained using a set of observations. The set of observations may be obtained from historical data, such as data gathered during one or more processes described herein. In some implementations, the machine learning system may receive the set of observations (e.g., as input) from the migration system, as described elsewhere herein.

As an example, a feature set for a set of observations may include a first feature of workload data, a second feature of data lineage, a third feature of distributed computing data, and so on. As shown, for a first observation, the first feature may have a value of workload data1, the second feature may have a value of data lineage1, the third feature may have a value of distributed computing data1, and so on. These features and feature values are provided as examples and may differ in other examples.

As shown by reference number215, the set of observations may be associated with a target variable. The target variable may represent a variable having a numeric value, may represent a variable having a numeric value that falls within a range of values or has some discrete possible values, may represent a variable that is selectable from one of multiple options (e.g., one of multiple classes, classifications, labels, and/or the like), may represent a variable having a Boolean value, and/or the like. A target variable may be associated with a target variable value, and a target variable value may be specific to an observation. In example200, the target variable may be labelled utilization features and may include a value of labelled utilization features 1 for the first observation.

As shown by reference number220, the machine learning system may train a machine learning model using the set of observations and using one or more machine learning algorithms, such as a regression algorithm, a decision tree algorithm, a neural network algorithm, a k-nearest neighbor algorithm, a support vector machine algorithm, and/or the like. After training, the machine learning system may store the machine learning model as a trained machine learning model225to be used to analyze new observations.

As shown by reference number230, the machine learning system may apply the trained machine learning model225to a new observation, such as by receiving a new observation and inputting the new observation to the trained machine learning model225. As shown, the new observation may include a first feature of workload data X, a second feature of data lineage Y, a third feature of distributed computing data Z, and so on, as an example. The machine learning system may apply the trained machine learning model225to the new observation to generate an output (e.g., a result). The type of output may depend on the type of machine learning model and/or the type of machine learning task being performed. For example, the output may include a predicted value of a target variable, such as when supervised learning is employed. Additionally, or alternatively, the output may include information that identifies a cluster to which the new observation belongs, information that indicates a degree of similarity between the new observation and one or more other observations, and/or the like, such as when unsupervised learning is employed.

As an example, the trained machine learning model225may predict a value of labelled utilization features A for the target variable of the component for the new observation, as shown by reference number235. Based on this prediction, the machine learning system may provide a first recommendation, may provide output for determination of a first recommendation, may perform a first automated action, may cause a first automated action to be performed (e.g., by instructing another device to perform the automated action), and/or the like.

In some implementations, the trained machine learning model225may classify (e.g., cluster) the new observation in a cluster, as shown by reference number240. The observations within a cluster may have a threshold degree of similarity. As an example, if the machine learning system classifies the new observation in a first cluster (e.g., a workload data cluster), then the machine learning system may provide a first recommendation. Additionally, or alternatively, the machine learning system may perform a first automated action and/or may cause a first automated action to be performed (e.g., by instructing another device to perform the automated action) based on classifying the new observation in the first cluster.

As another example, if the machine learning system were to classify the new observation in a second cluster (e.g., a data lineage cluster), then the machine learning system may provide a second (e.g., different) recommendation and/or may perform or cause performance of a second (e.g., different) automated action.

In some implementations, the recommendation and/or the automated action associated with the new observation may be based on a target variable value having a particular label (e.g., classification, categorization, and/or the like), may be based on whether a target variable value satisfies one or more thresholds (e.g., whether the target variable value is greater than a threshold, is less than a threshold, is equal to a threshold, falls within a range of threshold values, and/or the like), may be based on a cluster in which the new observation is classified, and/or the like.

In this way, the machine learning system may apply a rigorous and automated process to label utilization features of the system and recommending a cloud architecture. The machine learning system enables recognition and/or identification of tens, hundreds, thousands, or millions of features and/or feature values for tens, hundreds, thousands, or millions of observations, thereby increasing accuracy and consistency and reducing delay associated with labeling utilization features of the system and recommending a cloud architecture relative to requiring computing resources to be allocated for tens, hundreds, or thousands of operators to manually label utilization features of the system and recommending a cloud architecture.

As indicated above,FIG.2is provided as an example. Other examples may differ from what is described in connection withFIG.2.

FIG.3is a diagram of an example environment300in which systems and/or methods described herein may be implemented. As shown inFIG.3, the environment300may include a migration system301, which may include one or more elements of and/or may execute within a cloud computing system302. The cloud computing system302may include one or more elements303-313, as described in more detail below. As further shown inFIG.3, the environment300may include a network320and/or a server device330. Devices and/or elements of the environment300may interconnect via wired connections and/or wireless connections.

The cloud computing system302includes computing hardware303, a resource management component304, a host operating system (OS)305, and/or one or more virtual computing systems306. The resource management component304may perform virtualization (e.g., abstraction) of the computing hardware303to create the one or more virtual computing systems306. Using virtualization, the resource management component304enables a single computing device (e.g., a computer, a server, and/or the like) to operate like multiple computing devices, such as by creating multiple isolated virtual computing systems306from the computing hardware303of the single computing device. In this way, the computing hardware303can operate more efficiently, with lower power consumption, higher reliability, higher availability, higher utilization, greater flexibility, and lower cost than using separate computing devices. The computing hardware303includes hardware and corresponding resources from one or more computing devices. For example, the computing hardware303may include hardware from a single computing device (e.g., a single server) or from multiple computing devices (e.g., multiple servers), such as multiple computing devices in one or more data centers. As shown, the computing hardware303may include one or more processors307, one or more memories308, one or more storage components309, and/or one or more networking components310. Examples of a processor, a memory, a storage component, and a networking component (e.g., a communication component) are described elsewhere herein.

The resource management component304includes a virtualization application (e.g., executing on hardware, such as the computing hardware303) capable of virtualizing the computing hardware303to start, stop, and/or manage the one or more virtual computing systems306. For example, the resource management component304may include a hypervisor (e.g., a bare-metal or Type 1 hypervisor, a hosted or Type 2 hypervisor, and/or the like) or a virtual machine monitor, such as when the virtual computing systems306are virtual machines311. Additionally, or alternatively, the resource management component304may include a container manager, such as when the virtual computing systems306are containers312. In some implementations, the resource management component304executes within and/or in coordination with a host operating system305.

A virtual computing system306includes a virtual environment that enables cloud-based execution of operations and/or processes described herein using computing hardware303. As shown, a virtual computing system306may include a virtual machine311, a container312, a hybrid environment313that includes a virtual machine and a container, and/or the like. A virtual computing system306may execute one or more applications using a file system that includes binary files, software libraries, and/or other resources required to execute applications on a guest operating system (e.g., within the virtual computing system306) or the host operating system305.

Although the migration system301may include one or more elements303-313of the cloud computing system302, may execute within the cloud computing system302, and/or may be hosted within the cloud computing system302, in some implementations, the migration system301may not be cloud-based (e.g., may be implemented outside of a cloud computing system) or may be partially cloud-based. For example, the migration system301may include one or more devices that are not part of the cloud computing system302, such as device400ofFIG.4, which may include a standalone server or another type of computing device. The migration system301may perform one or more operations and/or processes described in more detail elsewhere herein.

The network320includes one or more wired and/or wireless networks. For example, the network320may include a cellular network, a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a private network, the Internet, and/or the like, and/or a combination of these or other types of networks. The network320enables communication among the devices of the environment300.

The server device330may include one or more devices capable of receiving, generating, storing, processing, providing, and/or routing information, as described elsewhere herein. The server device330may include a communication device and/or a computing device. For example, the server device330may include a server, such as an application server, a client server, a web server, a database server, a host server, a proxy server, a virtual server (e.g., executing on computing hardware), or a server in a cloud computing system. In some implementations, the server device330may include computing hardware used in a cloud computing environment.

FIG.4is a diagram of example components of a device400, which may correspond to the migration system301and/or the server device330. In some implementations, the migration system301and/or the server device330may include one or more devices400and/or one or more components of the device400. As shown inFIG.4, the device400may include a bus410, a processor420, a memory430, an input component440, an output component450, and a communication component460.

The bus410includes a component that enables wired and/or wireless communication among the components of device400. The processor420includes a central processing unit, a graphics processing unit, a microprocessor, a controller, a microcontroller, a digital signal processor, a field-programmable gate array, an application-specific integrated circuit, and/or another type of processing component. The processor420is implemented in hardware, firmware, or a combination of hardware and software. In some implementations, the processor420includes one or more processors capable of being programmed to perform a function. The memory430includes a random-access memory, a read only memory, and/or another type of memory (e.g., a flash memory, a magnetic memory, and/or an optical memory).

The input component440enables the device400to receive input, such as user input and/or sensed inputs. For example, the input component440may include a touch screen, a keyboard, a keypad, a mouse, a button, a microphone, a switch, a sensor, a global positioning system component, an accelerometer, a gyroscope, an actuator, and/or the like. The output component450enables the device400to provide output, such as via a display, a speaker, and/or one or more light-emitting diodes. The communication component460enables the device400to communicate with other devices, such as via a wired connection and/or a wireless connection. For example, the communication component460may include a receiver, a transmitter, a transceiver, a modem, a network interface card, an antenna, and/or the like.

The device400may perform one or more processes described herein. For example, a non-transitory computer-readable medium (e.g., the memory430) may store a set of instructions (e.g., one or more instructions, code, software code, program code, and/or the like) for execution by the processor420. The processor420may execute the set of instructions to perform one or more processes described herein. In some implementations, execution of the set of instructions, by one or more processors420, causes the one or more processors420and/or the device400to perform one or more processes described herein. In some implementations, hardwired circuitry may be used instead of or in combination with the instructions to perform one or more processes described herein. Thus, implementations described herein are not limited to any specific combination of hardware circuitry and software.

The number and arrangement of components shown inFIG.4are provided as an example. The device400may include additional components, fewer components, different components, or differently arranged components than those shown inFIG.4. Additionally, or alternatively, a set of components (e.g., one or more components) of the device400may perform one or more functions described as being performed by another set of components of the device400.

FIG.5is a flowchart of an example process500for utilizing a machine learning model to migrate a system to a cloud computing environment. In some implementations, one or more process blocks ofFIG.5may be performed by a device (e.g., the migration system301). In some implementations, one or more process blocks ofFIG.5may be performed by another device or a group of devices separate from or including the device, such as a server device (e.g., the server device330). Additionally, or alternatively, one or more process blocks ofFIG.5may be performed by one or more components of the device400, such as the processor420, the memory430, the input component440, the output component450, and/or the communication component460.

As shown inFIG.5, process500may include receiving logs and files associated with a system to be migrated to a cloud computing environment (block510). For example, the device may receive logs and files associated with a system to be migrated to a cloud computing environment, as described above. In some implementations, the logs include structured data, semi-structured data, and unstructured data associated with the system and the files include execution log files associated with the system.

As further shown inFIG.5, process500may include determining, based on the logs and the files, workload data identifying workload patterns of the system (block520). For example, the device may determine, based on the logs and the files, workload data identifying workload patterns of the system, as described above. In some implementations, determining, based on the logs and the files, the workload data identifying workload patterns of the system includes parsing the logs and the files to identify procedures and functions performed by the system, and determining the workload data based on the procedures and the functions performed by the system.

As further shown inFIG.5, process500may include deriving a data lineage for source data and target data included the logs and the files (block530). For example, the device may derive a data lineage for source data and target data included the logs and the files, as described above. In some implementations, deriving the data lineage for the source data and the target data included the logs and the files includes parsing the logs and the files to identify the source data and the target data, and deriving the data lineage based on the source data and the target data.

As further shown inFIG.5, process500may include assessing a utilization pattern of the system based on the logs and the files to determine whether a distributed computing feature of the system is being utilized (block540). For example, the device may assess a utilization pattern of the system based on the logs and the files to determine whether a distributed computing feature of the system is being utilized, as described above. In some implementations, assessing the utilization pattern of the system based on the logs and the files to determine whether the distributed computing feature of the system is being utilized includes parsing the logs and the files to identify the utilization pattern of the system, and determining whether the distributed computing feature of the system is being utilized based on the utilization pattern.

As further shown inFIG.5, process500may include processing the workload data, the data lineage, and data identifying utilization of the distributed computing feature, with a machine learning model, to label utilization features of the system and to recommend a cloud architecture (block550). For example, the device may process the workload data, the data lineage, and data identifying utilization of the distributed computing feature, with a machine learning model, to label utilization features of the system and to recommend a cloud architecture, as described above. In some implementations, the machine learning model is a linear regression model or a reinforcement model.

As further shown inFIG.5, process500may include processing the workload data, the data lineage, and the data identifying utilization of the distributed computing feature, with a natural language processing model, to determine a cost of migrating the system to the cloud computing environment (block560). For example, the device may process the workload data, the data lineage, and the data identifying utilization of the distributed computing feature, with a natural language processing model, to determine a cost of migrating the system to the cloud computing environment, as described above. In some implementations, processing the workload data, the data lineage, and the data identifying utilization of the distributed computing feature, with the natural language processing model, to determine the cost of migrating the system to the cloud computing environment includes identifying products utilized by the system based on the workload data, the data lineage, and the data identifying utilization of the distributed computing feature; generating a mapping of the products utilized by the system and products available in the cloud computing environment; and determining the cost of migrating the system to the cloud computing environment based on the mapping.

As further shown inFIG.5, process500may include processing the labelled utilization features, the cloud architecture, and the cost, with a Q-matrix model, to determine migration actions for migrating the system to the cloud computing environment (block570). For example, the device may process the labelled utilization features, the cloud architecture, and the cost, with a Q-matrix model, to determine migration actions for migrating the system to the cloud computing environment, as described above. In some implementations, processing the labelled utilization features, the cloud architecture, and the cost, with the Q-matrix model, to determine the migration actions includes assigning complexity scores to the labelled utilization features based on the cloud architecture and the cost; creating a reward matrix based on the complexity scores assigned to the labelled utilization features; creating, based on the reward matrix, a Q-matrix that maps actions and states; and determining the migration actions based on the Q-matrix.

As further shown inFIG.5, process500may include performing one or more actions based on the migration actions (block580). For example, the device may perform one or more actions based on the migration actions, as described above. In some implementations, performing the one or more actions includes providing the migration actions for display, receiving a change to one of the migration actions and modifying the one of the migration actions based on the change, or retraining the machine learning model based on the migration actions. In some implementations, performing the one or more actions includes generating a sizing template based on the migration actions, and creating an architecture for the cloud computing environment based on the sizing template. In some implementations, performing the one or more actions includes determining complexities associated with migrating the system based on the migration actions and generating a migration roadmap based on the complexities, or determining data groups of the system based on the migration actions and migrating data of the system to the cloud computing environment based on the data groups.

In some implementations, process500includes storing, in a data structure associated with the device, the logs, the files, the workload data, the data lineage, and the data identifying utilization of the distributed computing feature.

In some implementations, process500includes training the machine learning model with a training dataset and prior to processing the workload data, the data lineage, and data identifying utilization of the distributed computing feature with the machine learning model, and testing the machine learning model with a test dataset and prior to processing the workload data, the data lineage, and data identifying utilization of the distributed computing feature with the machine learning model.

The foregoing disclosure provides illustration and description but is not intended to be exhaustive or to limit the implementations to the precise form disclosed. Modifications may be made in light of the above disclosure or may be acquired from practice of the implementations.

As used herein, the term “component” is intended to be broadly construed as hardware, firmware, or a combination of hardware and software. It will be apparent that systems and/or methods described herein may be implemented in different forms of hardware, firmware, and/or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems and/or methods is not limiting of the implementations. Thus, the operation and behavior of the systems and/or methods are described herein without reference to specific software code—it being understood that software and hardware can be used to implement the systems and/or methods based on the description herein. As used herein, satisfying a threshold may, depending on the context, refer to a value being greater than the threshold, greater than or equal to the threshold, less than the threshold, less than or equal to the threshold, equal to the threshold, and/or the like, depending on the context.