Abstract: A device configured to identify a file in a network device, to generate a first set of block hash codes for data blocks for a first instance of the file, and to generate a second set of block hash codes for data blocks for a second instance of the file. The device is further configured to determine the first set of block hash codes matches the second set of block hash codes and to generate an entry in a file list for the instances of the file. The device is further configured to count the number of entries that are associated with the file and to determine the number of entries is greater than the redundancy threshold value. The device is further configured to delete one or more instances of the file in response to determining that the number of entries is greater than the redundancy threshold value.

Abstract: Aspects of the disclosure relate to resource allocation and rebating during in-flight data masking and on-demand encryption of big data on a network. Computer machine(s), cluster managers, nodes, and/or multilevel platforms can request, receive, and/or authenticate requests for a big data dataset, containing sensitive and non-sensitive data. Profiles can be auto provisioned, and access rights can be assigned. Server configuration and data connection properties can be defined. Secure connection(s) to the data store can be established. Sensitive information can be redacted into a sanitized dataset based on one or more data obfuscation types. RAM requirements and current RAM allocation can be diagnosed. Portion(s) of the current RAM allocation exceeding the RAM requirements can be rebated. The encrypted data can be transmitted, in response to the request, to a source, a target, and/or another computer machine and can be decrypted back into the sanitized dataset.

Abstract: A system accesses historical tasks. The system determines historical memory resources used to execute each historical task. the historical memory resources are associated with memory categories. The system determines total historical memory resources allocated for each memory category. The system determines memory resource utilization in executing each historical task. The system determines that the memory resource utilization is not optimal. In response, the system determines a memory resource configuration that yields memory resource utilization more than a threshold percentage. The system configures network nodes according to the determined memory resource configuration to execute tasks.

Abstract: A system access a blockchain network and conducts a blockchain transaction on a task log in the blockchain network. The system stores the blockchain transaction in a blockchain ledger. The system determines whether the blockchain transaction is associated with an anomaly. The anomaly indicates that the result of the blockchain transaction is unexpected. If it is determined that the blockchain transaction is associated with an anomaly, the blockchain transaction is rejected and removed from the blockchain ledger. Otherwise, the blockchain ledger is updated to indicate that the blockchain transaction is not associated with an anomaly.

Abstract: A system accesses a task log comprising text that is confidential information. The system selects a first portion of the task log. The system compares each word in the first portion with keywords that are known to be confidential information. The system determines that a word in the first portion is among the keywords. The system determines a hierarchical relationship between the word and neighboring words. The system determines that the word is associated with the neighboring words based on the hierarchical relationship. The system generates a template pattern comprising the word and one or more words associated with the word. The system obfuscates the template pattern.

Abstract: A device configured to identify a file in a network device, to generate a first set of block hash codes for data blocks for a first instance of the file, and to generate a second set of block hash codes for data blocks for a second instance of the file. The device is further configured to determine the first set of block hash codes matches the second set of block hash codes and to generate an entry in a file list for the instances of the file. The device is further configured to count the number of entries that are associated with the file and to determine the number of entries is greater than the redundancy threshold value. The device is further configured to delete one or more instances of the file in response to determining that the number of entries is greater than the redundancy threshold value.

Abstract: A device configured to identify a file in a network device, to generate a first set of block hash codes for data blocks for a first instance of the file, and to generate a second set of block hash codes for data blocks for a second instance of the file. The device is further configured to determine the first set of block hash codes matches the second set of block hash codes and to generate an entry in a file list for the instances of the file. The device is further configured to count the number of entries that are associated with the file and to determine the number of entries is greater than the redundancy threshold value. The device is further configured to delete one or more instances of the file in response to determining that the number of entries is greater than the redundancy threshold value.

Abstract: Aspects of the disclosure relate to resource allocation and rebating during in-flight data masking and on-demand encryption of big data on a network. Computer machine(s), cluster managers, nodes, and/or multilevel platforms can request, receive, and/or authenticate requests for a big data dataset, containing sensitive and non-sensitive data. Profiles can be auto provisioned, and access rights can be assigned. Server configuration and data connection properties can be defined. Secure connection(s) to the data store can be established. Sensitive information can be redacted into a sanitized dataset based on one or more data obfuscation types. State point information for previously reached safe points can be stored and progressively released such that only the incomplete portion(s) of task(s) need to be resubmitted. The encrypted data can be transmitted, in response to the request, to a source, a target, and/or another computer machine and can be decrypted back into the sanitized dataset.

Abstract: Aspects of the disclosure relate to resource allocation and rebating during in-flight data masking and on-demand encryption of big data on a network. Computer machine(s), cluster managers, nodes, and/or multilevel platforms can request, receive, and/or authenticate requests for a big data dataset, containing sensitive and non-sensitive data. Profiles can be auto provisioned, and access rights can be assigned. Server configuration and data connection properties can be defined. Secure connection(s) to the data store can be established. Sensitive information can be redacted into a sanitized dataset based on one or more data obfuscation types. RAM requirements and current RAM allocation can be diagnosed. Portion(s) of the current RAM allocation exceeding the RAM requirements can be rebated. The encrypted data can be transmitted, in response to the request, to a source, a target, and/or another computer machine and can be decrypted back into the sanitized dataset.

Abstract: Aspects of the disclosure relate to resource allocation and rebating during in-flight data masking and on-demand encryption of big data on a network. Computer machine(s), cluster managers, nodes, and/or multilevel platforms can request, receive, and/or authenticate requests for a big data dataset, containing sensitive and non-sensitive data. Profiles can be auto provisioned, and access rights can be assigned. Server configuration and data connection properties can be defined. Secure connection(s) to the data store can be established. Sensitive information can be redacted into a sanitized dataset based on one or more data obfuscation types. RAM requirements and current RAM allocation can be diagnosed. Portion(s) of the current RAM allocation exceeding the RAM requirements can be rebated. The encrypted data can be transmitted, in response to the request, to a source, a target, and/or another computer machine and can be decrypted back into the sanitized dataset.

Abstract: Aspects of the disclosure relate to resource allocation and rebating during in-flight data masking and on-demand encryption of big data on a network. Computer machine(s), cluster managers, nodes, and/or multilevel platforms can request, receive, and/or authenticate requests for a big data dataset, containing sensitive and non-sensitive data. Profiles can be auto provisioned, and access rights can be assigned. Server configuration and data connection properties can be defined. Secure connection(s) to the data store can be established. The big data dataset can be uncompressed based on a codec and uncompressed data blocks can be distributed for processing. Sensitive information can be redacted into a sanitized dataset based on one or more data obfuscation types. The encrypted data can be transmitted, in response to the request, to a source, a target, and/or another computer machine and can be decrypted back into the sanitized dataset.

Abstract: Aspects of the disclosure relate to resource allocation and rebating during in-flight data masking and on-demand encryption of big data on a network. Computer machine(s), cluster managers, nodes, and/or multilevel platforms can request, receive, and/or authenticate requests for a big data dataset, containing sensitive and non-sensitive data. Profiles can be auto provisioned, and access rights can be assigned. Server configuration and data connection properties can be defined. Secure connection(s) to the data store can be established. Sensitive information can be redacted into a sanitized dataset based on one or more data obfuscation types. Crashed executor(s) can be detected and caged to prevent further use. Uncompleted task(s) for crashed executor(s) can be reassigned. The encrypted data can be transmitted, in response to the request, to a source, a target, and/or another computer machine and can be decrypted back into the sanitized dataset.

Abstract: Aspects of the disclosure relate to in-flight data masking and on-demand encryption of big data on a network. Computer machine(s), cluster managers, nodes, and/or multilevel platforms can request, receive, and/or authenticate requests for a big data dataset, containing sensitive and non-sensitive data, in a data store based on credentials received from a source. Profiles can be auto provisioned, and access rights can be assigned. Server configuration and data connection properties can be defined. A secure connection to the data store can be established. The sensitive information in the big data dataset can be redacted into a sanitized dataset based on one or more data obfuscation types. The encrypted data can be transmitted, in response to the request, to a source, a target, and/or another computer machine and can be decrypted back into the sanitized dataset.

Abstract: Aspects of the disclosure relate to resource allocation and rebating during in-flight data masking and on-demand encryption of big data on a network. Computer machine(s), cluster managers, nodes, and/or multilevel platforms can request, receive, and/or authenticate requests for a big data dataset, containing sensitive and non-sensitive data. Profiles can be auto provisioned, and access rights can be assigned. Server configuration and data connection properties can be defined. Secure connection(s) to the data store can be established. Sensitive information can be redacted into a sanitized dataset based on one or more data obfuscation types. RAM requirements and current RAM allocation can be diagnosed. Portion(s) of the current RAM allocation exceeding the RAM requirements can be rebated. The encrypted data can be transmitted, in response to the request, to a source, a target, and/or another computer machine and can be decrypted back into the sanitized dataset.

Abstract: Aspects of the disclosure relate to resource allocation and rebating during in-flight data masking and on-demand encryption of big data on a network. Computer machine(s), cluster managers, nodes, and/or multilevel platforms can request, receive, and/or authenticate requests for a big data dataset, containing sensitive and non-sensitive data. Profiles can be auto provisioned, and access rights can be assigned. Server configuration and data connection properties can be defined. Secure connection(s) to the data store can be established. The big data dataset can be uncompressed based on a codec and uncompressed data blocks can be distributed for processing. Sensitive information can be redacted into a sanitized dataset based on one or more data obfuscation types. The encrypted data can be transmitted, in response to the request, to a source, a target, and/or another computer machine and can be decrypted back into the sanitized dataset.

Abstract: Aspects of the disclosure relate to resource allocation and rebating during in-flight data masking and on-demand encryption of big data on a network. Computer machine(s), cluster managers, nodes, and/or multilevel platforms can request, receive, and/or authenticate requests for a big data dataset, containing sensitive and non-sensitive data. Profiles can be auto provisioned, and access rights can be assigned. Server configuration and data connection properties can be defined. Secure connection(s) to the data store can be established. Sensitive information can be redacted into a sanitized dataset based on one or more data obfuscation types. Crashed executor(s) can be detected and caged to prevent further use. Uncompleted task(s) for crashed executor(s) can be reassigned. The encrypted data can be transmitted, in response to the request, to a source, a target, and/or another computer machine and can be decrypted back into the sanitized dataset.

Abstract: Aspects of the disclosure relate to resource allocation and rebating during in-flight data masking and on-demand encryption of big data on a network. Computer machine(s), cluster managers, nodes, and/or multilevel platforms can request, receive, and/or authenticate requests for a big data dataset, containing sensitive and non-sensitive data. Profiles can be auto provisioned, and access rights can be assigned. Server configuration and data connection properties can be defined. Secure connection(s) to the data store can be established. Sensitive information can be redacted into a sanitized dataset based on one or more data obfuscation types. State point information for previously reached safe points can be stored and progressively released such that only the incomplete portion(s) of task(s) need to be resubmitted. The encrypted data can be transmitted, in response to the request, to a source, a target, and/or another computer machine and can be decrypted back into the sanitized dataset.

Abstract: Aspects of the disclosure relate to in-flight data masking and on-demand encryption of big data on a network. Computer machine(s), cluster managers, nodes, and/or multilevel platforms can request, receive, and/or authenticate requests for a big data dataset, containing sensitive and non-sensitive data, in a data store based on credentials received from a source. Profiles can be auto provisioned, and access rights can be assigned. Server configuration and data connection properties can be defined. A secure connection to the data store can be established. The sensitive information in the big data dataset can be redacted into a sanitized dataset based on one or more data obfuscation types. The encrypted data can be transmitted, in response to the request, to a source, a target, and/or another computer machine and can be decrypted back into the sanitized dataset.

Abstract: Aspects of the disclosure relate to resource allocation and rebating during in-flight data masking and on-demand encryption of big data on a network. Computer machine(s), cluster managers, nodes, and/or multilevel platforms can request, receive, and/or authenticate requests for a big data dataset, containing sensitive and non-sensitive data. Profiles can be auto provisioned, and access rights can be assigned. Server configuration and data connection properties can be defined. Secure connection(s) to the data store can be established. Sensitive information can be redacted into a sanitized dataset based on one or more data obfuscation types. RAM requirements and current RAM allocation can be diagnosed. Portion(s) of the current RAM allocation exceeding the RAM requirements can be rebated. The encrypted data can be transmitted, in response to the request, to a source, a target, and/or another computer machine and can be decrypted back into the sanitized dataset.