Data representation generation without access to content

Techniques for generating a data representation without access to content are described. A method for generating a data representation without access to content comprises receiving a request to analyze one or more data items in a protected area of the provider network, sending the request to the protected area of the provider network, wherein the cluster model is used to identify a cluster identifier associated with each of the one or more data items, receiving the cluster identifier associated with each of the one or more data items, and regenerating each of the one or more data items based on the cluster identifier.

BACKGROUND

The advent of virtualization technologies for commodity hardware has provided benefits with respect to managing large-scale computing resources for many customers with diverse needs, allowing various computing resources to be efficiently and securely shared by multiple customers. For example, virtualization technologies may allow a single physical computing machine to be shared among multiple users by providing each user with one or more virtual machines hosted by the single physical computing machine. Each such virtual machine is a software simulation acting as a distinct logical computing system that provides users with the illusion that they are the sole operators and administrators of a given hardware computing resource, while also providing application isolation and security among the various virtual machines. Furthermore, some virtualization technologies are capable of providing virtual resources that span two or more physical resources, such as a single virtual machine with multiple virtual processors that spans multiple distinct physical computing systems. As another example, virtualization technologies may allow data storage hardware to be shared among multiple users by providing each user with a virtualized data store which may be distributed across multiple data storage devices, with each such virtualized data store acting as a distinct logical data store that provides users with the illusion that they are the sole operators and administrators of the data storage resource.

Some clients may shift their computing resources from being on-premises and controlled by the user into data centers administered and managed by a data center operator or other service provider. However, different users may have different security requirements, which may limit the number of employees of the provider who are available to view data needed to administer, troubleshoot, and manage computing resources for high security clients.

DETAILED DESCRIPTION

The present disclosure relates to methods, apparatus, systems, and non-transitory computer-readable storage media for generating a data representation without access to content. According to some embodiments, different areas of a provider network may be associated with different security levels. For example, a public area of a provider network may host computing resources (e.g., including data and services) associated with a variety of customers who utilize the services of the provider network, while a protected area of the provider network may also host data and equivalent services but these resources may be air-gapped (e.g., resources that cannot communicate readily with resources in other areas of the provider network). Because access to resources in the protected area is limited, troubleshooting these resources may be difficult. For example, protected areas may only be accessed by certain administrators, engineers, or other employees of the provider network who have sufficient clearance levels to access the protected area. Uncleared users are instead required send requests to cleared users to obtain information about the resources in the protected area (e.g., to troubleshoot the resources, deploy new resources, etc.).

Even when such requests are handled by cleared users, the data the cleared users are viewing may not be removed from the protected area and provided to uncleared users for troubleshooting. For example, log files that were emitted when an error was detected may not be removed, as the log files may include sensitive data in addition to data that is useful for troubleshooting. In some embodiments, the protected area may be restricted from sending any data that can be used to identify content in the protected area. Embodiments enable data that is useful from an engineering perspective to be identified and used without exposing content from the protected area.

In some embodiments, data (such as log files) emitted by services hosted in the public area of the provider network can be analyzed by a data clustering service. The data clustering service can implement a K-means (or other) cluster model to generate K clusters of similar data. The resulting model can then be provided to the protected area of the provider network. When data generated by services in the protected area needs to be analyzed, the data can be analyzed using the model and each individual data item can be assigned a cluster identifier and confidence score. This cluster ID and confidence score can then be returned back to the public area of the provider network and used to regenerate prototypical data that represents the data that was analyzed in the protected area. The confidence score can be used to inform how likely this regenerated data matches the data from the protected area. The regenerated data may then be used to troubleshoot any events that have been detected in the protected area, while ensuring no protected content has been removed from the protected area.

FIG. 1is a diagram illustrating an environment for generating a data representation without access to content according to some embodiments. A provider network100(or, “cloud” provider network) provides users with the ability to utilize one or more of a variety of types of computing-related resources such as compute resources (e.g., executing virtual machine (VM) instances and/or containers, executing batch jobs, executing code without provisioning servers), data/storage resources (e.g., object storage, block-level storage, data archival storage, databases and database tables, etc.), network-related resources (e.g., configuring virtual networks including groups of compute resources, content delivery networks (CDNs), Domain Name Service (DNS)), application resources (e.g., databases, application build/deployment services), access policies or roles, identity policies or roles, machine images, routers and other data processing resources, etc. These and other computing resources may be provided as services, such as a hardware virtualization service that can execute compute instances, a storage service that can store data objects, etc. The users (or “customers”) of provider networks100may utilize one or more user accounts that are associated with a customer account, though these terms may be used somewhat interchangeably depending upon the context of use. Users may interact with a provider network100across one or more intermediate networks106(e.g., the internet) via one or more interface(s), such as through use of application programming interface (API) calls, via a console implemented as a website or application, etc. The interface(s) may be part of, or serve as a front-end to, a control plane of the provider network100that includes “backend” services supporting and enabling the services that may be more directly offered to customers.

For example, a cloud provider network (or just “cloud”) typically refers to a large pool of accessible virtualized computing resources (such as compute, storage, and networking resources, applications, and services). A cloud can provide convenient, on-demand network access to a shared pool of configurable computing resources that can be programmatically provisioned and released in response to customer commands. These resources can be dynamically provisioned and reconfigured to adjust to variable load. Cloud computing can thus be considered as both the applications delivered as services over a publicly accessible network (e.g., the Internet, a cellular communication network) and the hardware and software in cloud provider data centers that provide those services.

A cloud provider network can be formed as a number of regions, where a region may be a geographical area in which the cloud provider clusters data centers. Each region can include multiple (e.g., two or more) availability zones (AZs) connected to one another via a private high-speed network, for example a fiber communication connection. An AZ may provide an isolated failure domain including one or more data center facilities with separate power, separate networking, and separate cooling from those in another AZ. Preferably, AZs within a region are positioned far enough away from one other that a same natural disaster (or other failure-inducing event) should not affect or take more than one AZ offline at the same time. Customers can connect to AZ of the cloud provider network via a publicly accessible network (e.g., the Internet, a cellular communication network).

To provide these and other computing resource services, provider networks100often rely upon virtualization techniques. For example, virtualization technologies may be used to provide users the ability to control or utilize compute instances (e.g., a VM using a guest operating system (O/S) that operates using a hypervisor that may or may not further operate on top of an underlying host O/S, a container that may or may not operate in a VM, an instance that can execute on “bare metal” hardware without an underlying hypervisor), where one or multiple compute instances can be implemented using a single electronic device. Thus, a user may directly utilize a compute instance (e.g., provided by a hardware virtualization service) hosted by the provider network to perform a variety of computing tasks. Additionally, or alternatively, a user may indirectly utilize a compute instance by submitting code to be executed by the provider network (e.g., via an on-demand code execution service), which in turn utilizes a compute instance to execute the code—typically without the user having any control of or knowledge of the underlying compute instance(s) involved.

For example, in various embodiments, a “serverless” function may include code provided by a user or other entity—such as the provider network itself—that can be executed on demand Serverless functions may be maintained within provider network100by an on-demand code execution service and may be associated with a particular user or account or be generally accessible to multiple users/accounts. A serverless function may be associated with a Uniform Resource Locator (URL), Uniform Resource Identifier (URI), or other reference, which may be used to invoke the serverless function. A serverless function may be executed by a compute instance, such as a virtual machine, container, etc., when triggered or invoked. In some embodiments, a serverless function can be invoked through an application programming interface (API) call or a specially formatted HyperText Transport Protocol (HTTP) request message. Accordingly, users can define serverless functions that can be executed on demand, without requiring the user to maintain dedicated infrastructure to execute the serverless function. Instead, the serverless functions can be executed on demand using resources maintained by the provider network100. In some embodiments, these resources may be maintained in a “ready” state (e.g., having a pre-initialized runtime environment configured to execute the serverless functions), allowing the serverless functions to be executed in near real-time.

In some embodiments, the provider network100can be optionally subdivided into different areas. For example, as illustrated inFIG. 1, the provider network100may be subdivided into a public area114and at least one protected area116. Each area may be logically isolated from the other (and from any other areas not shown within provider network100. For example, each area may be a distinct logical data center, supported by one or more physical data centers, and each area may have its own power supply and networking infrastructure to limit the likelihood of a failure in one area from affecting another area. In various embodiments provider network100may include a plurality of regions, each having its own plurality of areas. In some embodiments, an area of the provider network100may represent a region of the provider network. Each region of provider network100may include resources located in the same geographic area (e.g., state, country, etc.). By dividing provider network100into various regions and areas, the data and customer instances stored therein can be protected against failure events and access to different areas may be separately controlled.

In some embodiments, the public area114may be an area within the provider network that provides open access to and interconnectivity among a plurality of entities (users) of the provider network. The protected area116may be an area within a private area of the provider network. Access to protected areas of the provider network, and interconnectivity between a protected area and other areas of the provider network (e.g., ability to receive data into and send data from the protected area) may be restricted to certain users of the provider network. In some embodiments, the protected area may be air-gapped, such that data cannot be readily transferred into or out of the protected area except through specialized channels, such as secure transfer service124. In some embodiments, the protected area116may comprise one or more resource(s). The resources may comprise, for instance, computation and storage resources utilized by applications and/or services implemented on various devices/hosts in the protected area116. Deployment, maintenance, and use of these resources may be associated with different users, which may include internal users (e.g., users of the provider network that develop applications or services that the provider network100offers to external users) and external users (e.g., customers of the provider network that host their own solutions within the provider network100). The resources may be associated with a particular user or account or may be generally accessible to multiple users and/or multiple accounts.

Because it is difficult to get data out of the protected area116, log files and other protected data128generated by various services within the protected area may be inaccessible to engineers and other users who may use such data to perform maintenance, troubleshooting, and other tasks. However, the services operating in the protected area typically are the same or very similar to services running in the public area114of the provider network. As such, the log files and other data emitted by these services may also be similar. In accordance with various embodiments, a cluster mapping service110A and110B can be implemented in bother the public area and the protected area of the provider network. In the public area of the provider network, cluster mapping service110A can include a cluster training manager118that receives training data112at numeral1. This training data may include log files or other data emitted by various services within public area114. In some embodiments, the training data112may be selected from data emitted by only those services that are also implemented in protected area116. Cluster training manager118can train a model, such as a K-means cluster model or other cluster model using the training data. At numeral2, the cluster training manager118can output one or more cluster centroids/medoids122to data regeneration service102and, at numeral3, can output the trained cluster model120to cluster mapping service110B in the protected area. Cluster centroids may correspond to the theoretical center of each cluster. In some embodiments, if no data item from the training data corresponds to the cluster centroid, then one may be synthetically generated. Alternatively, cluster medoids may correspond to the closest member of the dataset to the cluster centroid. If no data item from the training data corresponds to the cluster centroid, then the cluster medoid may be used. In some embodiments, the cluster model provided by cluster training manager118can include a complete cluster model and its associated parameters. In some embodiments, the cluster model120can include the cluster centroids or medoids identified by the cluster training manager. The cluster model can be sent to the protected area using secure transfer service, as discussed below with respect to at leastFIGS. 7 and 8.

In various embodiments, data item analysis may be event driven. For example, upon detection of a fault with a service in the protected area, data items associated with that service may be analyzed and the results exported to the unprotected area for use in troubleshooting, resource management, etc. In some embodiments, such an event may be a request received from a user, service, or other entity. For example, at numerals4A-4C, a request for protected data can be received. In some embodiments, this request may originate from a user external to the provider network100, such as using electronic device104and client106(e.g., an app, an application programming interface, a console, etc.), as shown at numeral4A. In some embodiments, the request may originate from a user or service internal to the provider network, such as from service130and client132within the public area of the provider network, as shown at numeral4B, or from service134and client136in the protected area of the provider network, as shown at numeral4C. In some embodiments, the request may identify one or more data items to be analyzed. The data item(s) may be identified by the service that created them and/or by a time period in which they were created. In some embodiments, the data item(s) may be identified based on an identifier associated with the data item(s), such as an event identifier that was created when an event was detected, and the data item(s) were created. In some embodiments, the request may additionally, or alternatively, identify one or more storage locations in which data item(s) to be analyzed are stored. For example, a service may be onboarded to the cluster mapping service by providing a storage location (in the public area for services in the public area or in the protected area for services in the protected area) to which data items to be analyzed by the cluster mapping service are to be stored.

In some embodiments, the request at4A and4B can be sent to data regeneration service102. Data regeneration service102is responsible for obtaining information output by the cluster mapping service about the requested data items and providing and then regenerating a data item that represents the protected data item. At numeral5, data regeneration service102can send the request to cluster mapping service110B for information about the data item(s) indicated in the request. A cluster mapping manager126can coordinate obtaining the protected data items128identified based on the request, at numeral6, and processing the protected data items through the cluster model120received from cluster mapping service110A. As discussed, at numeral6, the cluster mapping manager can obtain the protected data items from one or more storage locations in protected area116. The storage locations may be identified based on the request. For example, the request may include a specific storage location(s) from which to obtain the protected data items. Alternatively, depending on the service(s) with which the data items are associated, the cluster mapping manager126may identify one or more associated storage locations that were defined during an onboarding process. Additionally, or alternatively, the protected data items stored in the storage locations may be queried (e.g., using SQL statements or other query processing techniques) to identify at least a subset of the protected data items128for processing. For example, the request may include a query that defined a date range, identifier range, or other operation or predicate to effectively filter at least a portion of the protected data items for processing by the cluster mapping service.

At numeral7, the protected data items obtained by the cluster mapping manager126based on the request can be provided to the cluster model120obtained from the cluster mapping service110A in the public area of the provider network. Each data item can be analyzed by the cluster model120and assigned a cluster identifier (ID) and a confidence score. The cluster ID and confidence score for each data item can be returned to the cluster mapping manager. At numeral8, the cluster ID and confidence score associated with each protected data item can be returned to the data regeneration manager125of data regeneration service102. By reducing each protected data item to a cluster ID and a confidence score, any possible protected content is removed. As a result, this information can be passed through the secure transfer service124from the protected area to the public area. At numeral9, for each data item, data regeneration manager125can identify the cluster centroid or medoid122associated with the cluster ID received from the cluster mapping service110B and create a corresponding regenerated data item134using the cluster centroid or medoid, at numeral10. In some embodiments, depending on the confidence score, the data item may not be regenerated, or the regenerated data item may be associated with a visual representation that indicates the confidence score. For example, a confidence score below a first threshold may cause data regeneration manager124to indicate that the data item could not be regenerated, a confidence score between the first threshold and second threshold may cause the data regeneration manager to add a visual indicator (such as a change in color of the data item) to indicate a lower confidence regeneration, and a confidence score greater than the second threshold may cause the data regeneration manager to make no changes to the visual appearance of the regenerated data item. In some embodiments, the regenerated data item may include the confidence score as a field in the regenerated data item.

In some embodiments, when data items are added to the storage location, a serverless function may be triggered which takes the data items and sends them to an endpoint associated with a machine learning service that hosts the cluster model. In some embodiments, this machine learning service may be part of cluster mapping service110B or may be a separate service that hosts the cluster model. Likewise, when a new cluster model is sent through the secure transfer service124, a serverless function may be triggered which adds the new cluster model to the endpoint of the machine learning service.

The regenerated data134can be returned to the requestor for further use, such as troubleshooting, resource management etc. This may include returning the regenerated data to the client106or132that requested the data. In some embodiments, client136may indicate in its request a different client in the public area of provider network100or external to provider network100which is to receive the regenerated data. Additionally, or alternatively, the client136may indicate a storage location in the public area of the provider network in which regenerated data134is to be stored.

In some embodiments, the confidence scores may be used to determine that the cluster model's performance has degraded (e.g., due to a change in data item formatting as a result of a service upgrade, or other changes that impact the ability of the cluster model to accurately identify clusters). If the confidence scores for one or more clusters collected over time are trending negatively (e.g., confidence scores are going down over time), then the model can be retrained or optimized using new data from the public area of the provider network. The resulting new model can then be sent to the protected area for use going forward.

In some embodiments, if confidence scores are below a threshold value, but overall confidence scores have not been trending negatively, then this may indicate that an unknown type of data item has been identified. In such cases, a request can be sent to a cleared engineer who can access the data items in the protected area for additional information about the unknown data item. In some embodiments, per-cluster confidence scores can be tracked over time. If confidence scores for only a particular cluster trend negatively then additional training may be performed using corresponding data items from the public area of the provider network.

In some embodiments, rather than exporting the cluster model to the protected area, a data item and cluster ID for each identified cluster in the public area can be exported. This mapping can be used as the seed for the clustering analysis performed in the protected area. This approach can provide additional insight into the model being used to engineers in the protected area. Rather than having the cluster model identify the cluster ID associated with each data item being analyzed, a distance from each data item to the received centroids or medoids can be computed. Based on the computed distances, a minimum distance to one of the centroids or medoids can be identified and a confidence score calculated based on the distances. The cluster ID corresponding to the closest centroid or medoid and the confidence score can then be returned.

FIG. 2is a diagram illustrating an environment for model optimization according to some embodiments. As discussed, clustering techniques, such as K-means clustering or other techniques, may be used to identify clusters of different types of data items, such as logs, generated in a public area of the provider network. In some embodiments, the cluster mapping service110A may include a cluster training manager118. In some embodiments, cluster training manager can be a continually operating component that updates and optimizes the cluster model120based on unprotected data200that is available in the public area (e.g., logs generated by services hosted in the public area, or other data of interest available in the public area). The cluster training manager118may include a data pre-processor202A and a model optimizer204. In some embodiments, data preprocessor202may implement various preprocessing rules that are defined for a particular type of data item. For example, data items may include some fields that will vary from data item to data item but which may not be relevant for a clustering analysis. These may include fields that are fixed in a given region but which may vary across areas of the provider network, such as area identifiers. These may also include procedurally generated fields such as timestamps, or repeating values that may be specific to a particular host machine, network, or other resource, such as partition identifiers, etc. By replacing such fixed, repeating, or procedurally generated data with placeholder values, the data items may be clustered on portions of the data items that are most useful to the engineers who use the data items for troubleshooting etc. These same preprocessing rules can be passed to the cluster mapping service110B to be implemented by data pre-processor202B. When protected data128is to be analyzed using the cluster model120, it can first be preprocessed using the same preprocessing rules. This results in more accurate cluster analysis being performed.

In some embodiments, placeholder values may also be used to identify configuration errors between areas of the provider network. As discussed, when a data item is regenerated, the placeholder identifiers may be replaced with placeholder values obtained from a placeholder dictionary. Different areas of the provider network may be associated with different configuration data. If configuration data for area A has been applied to a resource in area B, this may lead to errors. When log files, or other data items, are generated due to one of those errors, the area identifiers can be inspected to determine whether the correct area identifier is included in the log file. Such inspection may be performed manually or automatically as a check performed during regeneration by data regeneration manager125. If the wrong area identifier is detected, a notification can be generated by the data regeneration manager indicating that the configuration file needs to be updated.

FIG. 3is a diagram illustrating a model optimizer according to some embodiments. In some embodiments, preprocessed unprotected data300can be provided to model optimizer204to optimize the cluster model in use. This may include training data, new data items that have been generated in the public area of the provider network, or other data on which to further optimize the cluster model. A cluster analyzer can implement a clustering technique (e.g., K-means clustering or other clustering technique) and output an updated cluster model306using the new data for training. The updated cluster model can be tested using a test dataset308. The updated cluster model outputs regenerated data310corresponding to the test dataset. A model evaluator312can compare the regenerated data310to the test dataset308and determine whether the model has been improved. If the model has been improved, it can be exported to the protected area to be used in data analysis. If the model has not been improved, then model parameters304can be modified and model training can continue. In some embodiments, the model optimizer can continuously analyze preprocessed unprotected data, and develop a cluster model for mapping this data into one of N (e.g., a plurality) Clusters. In some embodiments, model evaluation may be performed manually by a user, using another machine learning service, or through a combination or manual and automated processes.

FIG. 4is a diagram illustrating data preprocessing in protected and public areas of a provider network according to some embodiments. As shown inFIG. 4, portions of the data items that are known to be varying or different between the protected data items and the unprotected data items can be replaced by placeholder values, rendering the cluster model more accurate. As discussed, preprocessing can be performed using placeholder rules. The placeholder rules can be defined by a user who manually reviews the unprotected data items400to identify fields, columns, or other portions of the data items which include information that is likely to vary, such as request IDs, region IDs, timestamps, etc. Because this information is known to vary, by replacing them with placeholder values, the cluster model can identify clusters based on information in the data items that is more useful for the task being performed, such as troubleshooting, resource management, etc. In some embodiments, automated techniques may be used to identify these portions. For example, machine learning techniques, principal component analysis, or other techniques may be used to identify timestamps, universally unique IDs (UUIDs), etc. and replace them with placeholders. Placeholder rules406can include the manual rules generated by a user and/or a machine learning model trained to identify and replace portions of the data items. Once the unprocessed unprotected data400has been preprocessed using the placeholder rules406, processed unprotected data404is generated. This processed data can be used to train the cluster model discussed above.

Additionally, the placeholder rules can be passed through secure transfer service124to protected area116. The placeholder rules406can then be used to preprocess unprocessed protected data408into processed protected data410. This processed data can then be analyzed using the cluster model as discussed above. In some embodiments, the placeholder rules can include both what portions of the data to replace as well as a list of placeholder IDs. The placeholder IDs may be a dictionary that maps an identifier to a placeholder value, for example placeholder 1 may map to [Request ID] and placeholder 2 may map to [Datetime], etc.

In some embodiments, when the cluster IDs and confidence scores are returned for protected data items, the dictionary of placeholder IDs may also be returned. During data regeneration, the cluster ID can be replaced with the cluster centroid or medoid and any placeholder IDs can be replaced with their corresponding placeholder using the placeholder dictionary. In some embodiments, a copy of the placeholder dictionary can be maintained in the protected area and the public area, eliminating the need to include the dictionary with the cluster results. In some embodiment, when the dictionary is updated in the public area or the protected area, a new copy of the dictionary can be sent to the other area and used going forward for placeholder ID mapping.

In addition to preventing protected content from leaving a protected area, embodiments additionally provide significant data compression of the data items. As discussed, each data item can be reduced to an identifier and a confidence score to be regenerated later for further analysis. Although embodiments have been discussed with respect to preventing protected data from leaving a protected area, such as an air-gapped area of a provider network, embodiments may also be used in various environments in which access to sensitive data is to be restricted and where troubleshooting and other management tasks do not require the content of the sensitive data to be exposed.

FIG. 5is a flow diagram illustrating operations500of a method for generating a data representation without access to content according to some embodiments. Some or all of the operations500(or other processes described herein, or variations, and/or combinations thereof) are performed under the control of one or more computer systems configured with executable instructions and are implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) executing collectively on one or more processors, by hardware or combinations thereof. The code is stored on a computer-readable storage medium, for example, in the form of a computer program comprising instructions executable by one or more processors. The computer-readable storage medium is non-transitory. In some embodiments, one or more (or all) of the operations500are performed by the data regeneration service102and/or the cluster mapping service110A,110B of the other figures.

The operations500include, at block502, receiving a request to analyze one or more data items in a protected area of a provider network. In some embodiments, the operations may further comprise training a cluster model using a plurality of log files in an unsecured area of a provider network. In some embodiments, the operations may further comprise sending the cluster model to the protected area of the provider network using a secure transfer service. In some embodiments, the operations may further comprise sending a plurality of cluster centroids or medoids identified by the cluster model to the protected area of the provider network using a secure transfer service. In some embodiments, the secure transfer service comprises a first schema that restricts data that can be sent from a public area of the provider network to the protected area of the provider network, and a second schema that restricts data that can be sent from the protected area of the provider network to the public area of the provider network.

The operations500further include, at block504, sending the request to the protected area of the provider network, wherein a cluster model is used to identify a cluster identifier associated with each of the one or more data items. In some embodiments, the operations may further include preprocessing the one or more data items in the protected area of the provider network before they are analyzed, wherein preprocessing includes replacing one or more of portions of the one or more data items with placeholder identifiers. In some embodiments, the placeholder identifiers indicate one or more of a region identifier, a request identifier, or a timestamp.

The operations500further include, at block506, receiving the cluster identifier associated with each of the one or more data items. The operations500further include, at block508, regenerating each of the one or more data items based on the cluster identifier. In some embodiments, regenerating each of the one or more data items based on the cluster identifier, may further comprise replacing the cluster identifier with a cluster centroid or medoid associated with the cluster identifier, and updating any placeholder identifiers with placeholder values using a placeholder dictionary. In some embodiments, the protected area of the provider network is an air-gapped area of the provider network.

In some embodiments, the operations500may comprise training a cluster model using a plurality of log files in a public area of a provider network, sending the cluster model to a protected area of the provider network using a secure transfer service, receiving a request to analyze a plurality of log files in the protected area of the provider network, sending the request to the protected area of the provider network, wherein a data analysis service uses the cluster model to identify a cluster identifier associated with each log file from the plurality of log files in the protected area, receiving the cluster identifier associated with each log file from the plurality of log files in the protected area using the secure transfer service, identifying a cluster centroid or medoid associated with each cluster identifier, and regenerating each log file from the plurality of log files in the protected area based on the cluster centroid or medoid.

FIG. 6is a diagram illustrating a framework for searching for resources in an isolated area of a provider network, according to some embodiments. In some embodiments, the provider network100can be optionally subdivided into different areas. For example, as illustrated inFIG. 6, the provider network100may be subdivided into a public area114and one or more protected areas116A-116N. Each area may be logically isolated from the other (and from any other areas not shown within provider network100. For example, each area may be a distinct logical data center, supported by one or more physical data centers, and each area may have its own power supply and networking infrastructure to limit the likelihood of a failure in one area from affecting another area. In various embodiments provider network100may include a plurality of regions, each having its own plurality of areas. Each region of provider network100may include resources located in the same geographic area (e.g., state, country, etc.). By dividing provider network100into various regions and areas, the data and customer instances stored therein can be protected against failure events and access to different areas may be separately controlled.

In some embodiments, the public area114may be an area within the provider network that provides open access to, and interconnectivity among, a plurality of entities (users) of the provider network. The protected areas116A-116N may be areas within a private area of the provider network and may be air-gapped from other areas of the provider network. Access to protected areas of the provider network, and interconnectivity between a protected area and other areas of the provider network (e.g., ability to receive data into and send data from the protected area) may be restricted to certain users of the provider network. In some embodiments, the protected areas116A-116N may comprise one or more resource(s)618. The resources618may comprise, for instance, computation and storage resources utilized by applications and/or services implemented on various devices/hosts in the protected areas116A-116N. Because the protected areas of the provider network may include sensitive or otherwise protected information (e.g., information designated by a governmental or non-governmental entity as classified data or classified metadata), access to the protected areas may be restricted to users who have been cleared to view such protected information (e.g., cleared users). Deployment, maintenance, and use of these resources may be associated with different cleared users and uncleared users, which may include internal users (e.g., users of the provider network that develop applications or services that the provider network100offers to external users) and external users (e.g., customers of the provider network that host their own solutions within the provider network100). The resources618may be associated with a particular user or account or may be generally accessible to multiple users and/or multiple accounts.

In some embodiments, the secure query service620provides uncleared users of the provider network100who do not have access to the one or more of the protected areas116A-116N with the ability to request and/or query for information about the resources618. As discussed, previously such uncleared users would have to request such information from cleared users. Because there are typically many fewer cleared users than uncleared users, this can be a time consuming process for the cleared users to respond to such requests. Additionally, it introduces the possibility of human error leading to incorrect or inaccurate information about the resources being relayed to the uncleared users, leading to further delays in deploying infrastructure, troubleshooting, or otherwise maintaining the protected areas of the provider network. The secure query service620may include various components, modules, or functionalities such as a search orchestration agent622, a secure transfer service124and a resource identification service626. The components may be implemented in hardware, software, or a combination of both and collectively used by the secure query service620for executing search queries against resources hosted in one or more protected areas116A-116N of the provider network.

In some embodiments, the secure query service620and its components may be distributed across different areas (e.g., the public area114and the protected area(s)116A-116N) in the provider network. For instance, as shown inFIG. 6, the search orchestration agent622may be implemented in the public area114and used by the secure query service to obtain search requests (from users) for resources residing in the protected areas116A-116N of the provider network. The resource identification service626may be implemented in the protected areas116A-116N and used by the secure query service620to execute the search requests and provide search responses to the users. The secure transfer service124may include one or more components, modules, or functionalities that may be implemented in both the public area114and protected areas116A-116N. In some embodiments, the secure query service620may utilize the secure transfer service124to process search requests for resources residing in the protected areas116A-116N, cause the execution of the search requests against the resources and send search responses to the users.

In some embodiments, at numeral1A, a search query request may be submitted to the secure query service620via a client application106of an electronic device104. For example, a user may interact with a user interface (UI) in the client application106to submit the search query request. In some embodiments, the search query request may specify a request for information about a resource in a protected area (e.g.,116A) of the provider network for which the user is responsible for maintaining, troubleshooting, deploying, etc. (e.g., the user has an account associated with the resource). In some embodiments, the search query request may specify one or more search parameters. For instance, the search parameters may include an identifier (e g, name) of the resource and an identifier of a protected area (e.g.,116A) where the resource is located. For example, a search query request for information about a database instance (e.g., a database table in the database instance) in a protected area116A may specify an identifier associated with the database table and an identifier of the protected area116A where the database table is located. The database table may be associated with the user's account and may include permissions indicating the ways in which the user may access and/or query information about the database table. In some embodiments, the user may first be authenticated using an authentication service in the provider network which authenticates the user making the search request prior to submitting the search request to the secure query service620. For instance, the authentication service128may authenticate the user based on data provided by the user (e.g., credentials, encrypted material, etc.).

Alternatively, in some embodiments, at numeral1B, a search query request may be submitted by the user via an Application Programing Interface (API) call to an API612in a control plane610in the provider network100. For instance, the user may submit the API call via the client application106of the electronic device104which may potentially occur responsive to a user interacting with the client application106. For example, the user may interact with a UI in the client application106to submit an API request. Alternatively, in some embodiments, the client application may directly issue the search query request (e.g., as an API request) as part of a script or program without user interaction. The search query request may be received via one or more APIs612in the control plane610which may then transmit the search query request to the secure query service620. The control plane610may handle many of the tasks involved in accepting and processing requests from users, including traffic management, authorization and access control, monitoring, and API management. For example, in some embodiments the control plane610creates, publishes, maintains, and monitors various APIs for users to access and interact with services of the provider network100. In some embodiments, as shown at numeral1C, the search request may also originate from another client application632implemented within another service630in the provider network such as an on-demand code execution service, a hardware virtualization service, or another service implemented by the provider network.

Secure query service620can receive the search query request (e.g., directly via client application106as shown at numeral1A, or via API612at numeral2). The search query request may be received by the search orchestration agent622in the secure query service620. As discussed further below, the search orchestration agent622can store a record of the search and pass the search, at numeral3, to secure transfer service124. As shown inFIG. 6, secure transfer service124may be implemented across the public area and the protected area of the provider network. For example, the secure transfer service124may include a first storage location hosted in the public area in which the search query request may be stored. The secure transfer service can verify the search query request (e.g., apply a first schema provided by the protected area to the search query request) before passing the search query request to a second storage location hosted in the destination protected area116A. Once the search query request has been added to the second storage location, at numeral4, an event can be generated and sent to resource identification service626. Resource identification service626may then perform the query on resources618, as shown at numeral5. The resource identification service may then generate a response based on the query results. At numeral6, the resource identification service626can add the response to the second storage location of secure transfer service124. The response can be verified using a second schema provided by the protected area116A to ensure the response includes only data that is allowed to be sent from the protected area to a public area. Once the response is verified, it can be added to the first storage location of the secure transfer service124. This may trigger an event, at numeral7, to search orchestration agent622indicating that a response has been added to the first storage location. Search orchestration agent622can obtain the response from the first storage location and provide the response to the client application106(e.g., via a notification, email, or other communication) at numeral8.

FIG. 7is a diagram illustrating exemplary operations performed by one or more components of the secure transfer service124for processing a search query request accessible in a protected area (e.g.,116A) of the provider network and obtaining a search query response as a result of executing the search query request according to some embodiments. Referring toFIG. 7, in some embodiments, the secure transfer service124comprises a data filtering agent700, a low-high storage location702and a high-low storage location704. In some embodiments, the low-high storage location702and the high-low storage location704may be implemented as part of a storage service provided by the provider network. In some embodiments, and as will be described in detail below, the storage service may provide object-based storage that may be used by the secure transfer service124to process a search query request for a resource and obtain a search query response as a result of execution of the search request.

In some embodiments, the low-high storage location702(e.g., a first storage location) may be implemented in the public area114of the provider network and the high-low storage location (e.g., a second storage location) may be implemented in the protected area116A of the provider network. In some embodiments, as discussed above with respect toFIG. 6, at numeral7, the secure transfer service124receives the search query request from the search orchestration agent622and stores the search query request in the low-high storage location702. In some embodiments, a representation of the search query request may be stored as, e.g., an object, record, or file in the low-high storage location702and referenced by a URL (Uniform Resource Locator).

At numeral7A, the data filtering agent700may access the low-high storage location702(e.g., programmatically or via an API call) and retrieve the search request object706from the low-high storage location702. For example, the data filtering agent700may be configured to use the URL referencing the search request object706to retrieve the object from the low-high storage location702. In some embodiments, the data filtering agent700may determine that there is a search request object706stored in the low-high storage location702by periodically polling the low-high storage location702. In other examples, the data filtering agent700may subscribe to a monitoring service in the provider network that may inform the low-high storage location702of the existence of a search request object706. As shown inFIG. 7, the data filtering agent700can be hosted in protected area116A and managed by the protected area. For example, the schemas implemented by the data filtering agent700may be controlled by the protected area, to ensure that the security standards required by the protected area are implemented in the schemas.

In some embodiments, the data filtering agent700may perform checks on the search request object706before the search request object is sent to the high-low storage location704in protected area116A. For instance, in some embodiments, the data filtering agent700may identify a low-high schema708to be applied to the search request object to verify the search request object (e.g., to determine whether the search request object includes any prohibited data, as defined in the low-high schema). For example, the low-high schema708may analyze the search request object706to filter out executable file types, such as binaries, from the search request object and allow free form strings, integers, or text fields in metadata associated with the search request object706. If any prohibited data is identified in the search request object, the search may fail and a response indicating such can be returned. Alternatively, in some embodiments, the prohibited data may be removed from the search request object, and the resulting filtered search request object can be used to perform the search.

Upon verification of the search request object, at numeral7B, the data filtering agent700may generate a filtered search request object710and transmit the filtered search request object710to the high-low storage location704associated with the secure transfer service124. In some embodiments, the high-low storage location704may be located in the protected area116A of the provider network100. In some embodiments, the storage of the filtered search request object710may trigger an event which may in turn cause the high-low storage location704to publish a message to the search gatherer service626of the existence of the filtered search request object710in the high-low storage location704.

As discussed above, secure transfer service124may also manage the return of a search query response to the requestor. In some embodiments, at numeral6, a representation of the search query response is stored as a search response object712in the high-low storage location704. For example, as discussed above with respect toFIG. 6, the resource identification service626can perform the search request query, generate a search request response, and return a representation of the search request response (e.g., an object, record, file, etc.). At numeral6A, the data filtering agent is notified that a search result is in the second storage location and accesses the second storage location (e.g., programmatically or via an API call). The data filtering agent700then identifies a second schema to be applied to one or more attributes, and/or types of attributes of the search response object. In some embodiments, the second schema identifies a type of each of one or more attributes of the search response object712and comprises one or more validation rules indicating at least one expected characteristic of values of the attributes of the search response object. For example, the second schema may define particular types of metadata (e.g., specific attributes about one or more of the types of resources which may be included in the protected area) that are allowed to be included in the search response object. Additionally, or alternatively, the second schema may define prohibited types of metadata that are not allowed to be included in the search response object. Further, the second schema may define validation rules which may specify one or more threshold values that the metadata values must not be larger than, smaller than, etc. The rule may specify that values of the attribute must be within a set of defined values. The rule may specify that values of the attribute must be larger than, equal to, and/or smaller than some other value that can be derived (e.g., dynamically, or periodically) based on other data. At numeral6B, the data filtering agent publishes an event to the first storage location to store the search result which includes metadata about the resource. If any prohibited information (e.g., based on the type of information or the value of the information included in the response object) is identified in the search response object, the search may fail and a response indicating such can be returned. Alternatively, in some embodiments, the prohibited information may be removed from the search response object, and the resulting filtered search response object can be returned. At numeral7, the search response is returned to the search orchestration agent622, and the search query response is returned to the requestor as discussed above.

FIG. 8illustrates an example provider network (or “service provider system”) environment according to some embodiments. A provider network800may provide resource virtualization to customers via one or more virtualization services810that allow customers to purchase, rent, or otherwise obtain instances812of virtualized resources, including but not limited to computation and storage resources, implemented on devices within the provider network or networks in one or more data centers. Local Internet Protocol (IP) addresses816may be associated with the resource instances812; the local IP addresses are the internal network addresses of the resource instances812on the provider network800. In some embodiments, the provider network800may also provide public IP addresses814and/or public IP address ranges (e.g., Internet Protocol version 4 (IPv4) or Internet Protocol version 6 (IPv6) addresses) that customers may obtain from the provider800.

Conventionally, the provider network800, via the virtualization services810, may allow a customer of the service provider (e.g., a customer that operates one or more client networks850A-850C including one or more customer device(s)852) to dynamically associate at least some public IP addresses814assigned or allocated to the customer with particular resource instances812assigned to the customer. The provider network800may also allow the customer to remap a public IP address814, previously mapped to one virtualized computing resource instance812allocated to the customer, to another virtualized computing resource instance812that is also allocated to the customer. Using the virtualized computing resource instances812and public IP addresses814provided by the service provider, a customer of the service provider such as the operator of customer network(s)850A-850C may, for example, implement customer-specific applications and present the customer's applications on an intermediate network840, such as the Internet. Other network entities820on the intermediate network840may then generate traffic to a destination public IP address814published by the customer network(s)850A-850C; the traffic is routed to the service provider data center, and at the data center is routed, via a network substrate, to the local IP address816of the virtualized computing resource instance812currently mapped to the destination public IP address814. Similarly, response traffic from the virtualized computing resource instance812may be routed via the network substrate back onto the intermediate network840to the source entity820.

FIG. 9is a block diagram of an example provider network that provides a storage service and a hardware virtualization service to customers, according to some embodiments. Hardware virtualization service920provides multiple computation resources924(e.g., VMs) to customers. The computation resources924may, for example, be rented or leased to customers of the provider network900(e.g., to a customer that implements customer network950). Each computation resource924may be provided with one or more local IP addresses. Provider network900may be configured to route packets from the local IP addresses of the computation resources924to public Internet destinations, and from public Internet sources to the local IP addresses of computation resources924.

Provider network900may provide a customer network950, for example coupled to intermediate network940via local network956, the ability to implement virtual computing systems992via hardware virtualization service920coupled to intermediate network940and to provider network900. In some embodiments, hardware virtualization service920may provide one or more APIs902, for example a web services interface, via which a customer network950may access functionality provided by the hardware virtualization service920, for example via a console994(e.g., a web-based application, standalone application, mobile application, etc.). In some embodiments, at the provider network900, each virtual computing system992at customer network950may correspond to a computation resource924that is leased, rented, or otherwise provided to customer network950.

From an instance of a virtual computing system992and/or another customer device990(e.g., via console994), the customer may access the functionality of storage service910, for example via one or more APIs902, to access data from and store data to storage resources918A-918N of a virtual data store916(e.g., a folder or “bucket”, a virtualized volume, a database, etc.) provided by the provider network900. In some embodiments, a virtualized data store gateway (not shown) may be provided at the customer network950that may locally cache at least some data, for example frequently-accessed or critical data, and that may communicate with storage service910via one or more communications channels to upload new or modified data from a local cache so that the primary store of data (virtualized data store916) is maintained. In some embodiments, a user, via a virtual computing system992and/or on another customer device990, may mount and access virtual data store916volumes via storage service910acting as a storage virtualization service, and these volumes may appear to the user as local (virtualized) storage998.

While not shown inFIG. 9, the virtualization service(s) may also be accessed from resource instances within the provider network900via API(s)902. For example, a customer, appliance service provider, or other entity may access a virtualization service from within a respective virtual network on the provider network900via an API902to request allocation of one or more resource instances within the virtual network or within another virtual network.

Illustrative Systems

In some embodiments, a system that implements a portion or all of the techniques described herein may include a general-purpose computer system that includes or is configured to access one or more computer-accessible media, such as computer system1000illustrated inFIG. 10. In the illustrated embodiment, computer system1000includes one or more processors1010coupled to a system memory1020via an input/output (I/O) interface1030. Computer system1000further includes a network interface1040coupled to I/O interface1030. WhileFIG. 10shows computer system1000as a single computing device, in various embodiments a computer system1000may include one computing device or any number of computing devices configured to work together as a single computer system1000.

System memory1020may store instructions and data accessible by processor(s)1010. In various embodiments, system memory1020may be implemented using any suitable memory technology, such as random-access memory (RAM), static RAM (SRAM), synchronous dynamic RAM (SDRAM), nonvolatile/Flash-type memory, or any other type of memory. In the illustrated embodiment, program instructions and data implementing one or more desired functions, such as those methods, techniques, and data described above are shown stored within system memory1020as cluster mapping service code1025, data regeneration service code1027, and data1026.

Network interface1040may be configured to allow data to be exchanged between computer system1000and other devices1060attached to a network or networks1050, such as other computer systems or devices as illustrated inFIG. 1, for example. In various embodiments, network interface1040may support communication via any suitable wired or wireless general data networks, such as types of Ethernet network, for example. Additionally, network interface1040may support communication via telecommunications/telephony networks such as analog voice networks or digital fiber communications networks, via storage area networks (SANs) such as Fibre Channel SANs, or via I/O any other suitable type of network and/or protocol.

In some embodiments, a computer system1000includes one or more offload cards1070(including one or more processors1075, and possibly including the one or more network interfaces1040) that are connected using an I/O interface1030(e.g., a bus implementing a version of the Peripheral Component Interconnect-Express (PCI-E) standard, or another interconnect such as a QuickPath interconnect (QPI) or UltraPath interconnect (UPI)). For example, in some embodiments the computer system1000may act as a host electronic device (e.g., operating as part of a hardware virtualization service) that hosts compute instances, and the one or more offload cards1070execute a virtualization manager that can manage compute instances that execute on the host electronic device. As an example, in some embodiments the offload card(s)1070can perform compute instance management operations such as pausing and/or un-pausing compute instances, launching and/or terminating compute instances, performing memory transfer/copying operations, etc. These management operations may, in some embodiments, be performed by the offload card(s)1070in coordination with a hypervisor (e.g., upon a request from a hypervisor) that is executed by the other processors1010A-1010N of the computer system1000. However, in some embodiments the virtualization manager implemented by the offload card(s)1070can accommodate requests from other entities (e.g., from compute instances themselves), and may not coordinate with (or service) any separate hypervisor.

In some embodiments, system memory1020may be one embodiment of a computer-accessible medium configured to store program instructions and data as described above. However, in other embodiments, program instructions and/or data may be received, sent or stored upon different types of computer-accessible media. Generally speaking, a computer-accessible medium may include non-transitory storage media or memory media such as magnetic or optical media, e.g., disk or DVD/CD coupled to computer system1000via I/O interface1030. A non-transitory computer-accessible storage medium may also include any volatile or non-volatile media such as RAM (e.g., SDRAM, double data rate (DDR) SDRAM, SRAM, etc.), read only memory (ROM), etc., that may be included in some embodiments of computer system1000as system memory1020or another type of memory. Further, a computer-accessible medium may include transmission media or signals such as electrical, electromagnetic, or digital signals, conveyed via a communication medium such as a network and/or a wireless link, such as may be implemented via network interface1040.