Abstract: One example method includes receiving, by a TEA (trustworthy energy awareness) module, a request from a storage controller to identify a most energy-efficient storage array, as among a plurality of storage arrays, on which to store a set of data, determining, by the TEA module based on energy efficiency information and user constraints, the most energy efficient storage array, and identifying, by the TEA module to the storage controller, the most energy efficient storage array.

Abstract: One example method includes receiving, at a target, a migrated container, the container being migrated by a source, retrieving, by the target, a first container hash from an immutable distributed ledger, the first container hash being created at the source by hashing the container, and the first container hash being written by the source to the immutable distributed ledger, hashing, at the target, the container to create a second container hash, writing, by the target, the second container hash to the immutable distributed ledger, and determining, by the target, whether the first container hash and the second container hash match, and a failure of the first container hash to match the second container hash indicates that in-flight tampering of the container has occurred while the container was migrating from the source to the target.

Abstract: Automatic image splicing with metadata retention is disclosed. An image is retrieved from an image repository and checked for missing or invalid pixels. Additional images are retrieved from the image repository based on the missing or invalid pixels. Valid pixels from the additional images are used to replace the missing or invalid pixels in the original image. The resulting spliced image is complete. Metadata from all images represented in the spliced image are included in a metadata lineage associated with the spliced image.

Abstract: Energy aware data compression is disclosed. A storage array may receive data from a client to be compressed. The storage array may request information from a compression awareness engine that is configured to estimate compression times in the context of energy source and energy cost. The storage array makes a decision to compress the data based on the estimates or response received from the compression awareness engine. The data is then compressed and stored in the storage array.

Abstract: One example method includes identifying a dataset, which may be a backup dataset, that has been subjected to a mix&slice process, identifying a cryptographic key that was used to encrypt the dataset, randomly selecting, from the backup dataset, a portion of the dataset, randomly applying writes to the portion of the dataset, and deleting the cryptographic key. The dataset cannot be recovered without both the cryptographic key, and the portion of the dataset to which the writes were randomly applied.

Abstract: A fragment and shuffle erasure coding operation is disclosed. In a transformative operation, encrypted fragments are generated from an input file. The transformative operation includes one or more iterations of chunking the file and mixing the chunks to generate a mixed sequence. The mixed sequence is sliced into encrypted fragments. The encrypted fragments are shuffled in a storage system such that physical locations of the encrypted fragments change over time.

Abstract: One example method includes receiving a request to perform a data process, determining, by a trustworthy energy awareness (TEA) module, based on energy information and user constraints, a best time to perform the data process, and conveying, by the TEA module to a recipient, information indicating the best time to perform the data process. The data process may be a data backup process, or a garbage collection process, for example.

Abstract: Techniques are disclosed for evaluating immutable ledger requests using a semantic taxonomy. An example system includes a memory comprising instructions, and a processor configured to execute the instructions. Example instructions include providing a semantic taxonomy of semantically linked terms and phrases; using the semantic taxonomy to validate input fields of requests for immutable ledger transactions according to one or more predefined templates; and submitting the validated requests to an immutable ledger.

Abstract: One example method includes receiving, by a server, an initial blockchain request from a client, and the initial blockchain request includes a description, based on the description, performing a taxonomy phrase search of a taxonomy to identify one or more phrases that correspond to the description, receiving, from the client, a phrase selection indicating one of the phrase(s) selected by a user, performing a taxonomy word search of the taxonomy to identify one or more words that correspond to respective fields of the selected phrase(s), receiving, from the client, a completed blockchain transaction request that comprises the initial blockchain request and the selected phrase(s) and selected words, and adding, to a blockchain, the selected phrase(s) and selected words of the completed blockchain request.

Abstract: Techniques described herein relate to a method for managing transactions in a distributed ledger. The method includes obtaining a transaction request from a client; in response to obtaining the transaction request: verifying client information associated with the client and the transaction request; anonymizing the transaction request to generate an anonymous transaction request; updating client payload mappings based on the anonymous transaction request; shuffling the anonymous transaction request with a batch of requests to generate a shuffle index; assigning the batch of requests to virtual users to generate virtual user assignments; and processing the transaction request using the batch of requests.

Abstract: One example method includes identifying a dataset, which may be a backup dataset, that has been subjected to a mix&slice process, identifying a cryptographic key that was used to encrypt the dataset, randomly selecting, from the backup dataset, a portion of the dataset, randomly applying writes to the portion of the dataset, and deleting the cryptographic key. The dataset cannot be recovered without both the cryptographic key, and the portion of the dataset to which the writes were randomly applied.

Abstract: Techniques are disclosed for evaluating immutable ledger requests using a semantic taxonomy. An example system includes a memory comprising instructions, and a processor configured to execute the instructions. Example instructions include providing a semantic taxonomy of semantically linked terms and phrases; using the semantic taxonomy to validate input fields of requests for immutable ledger transactions according to one or more predefined templates; and submitting the validated requests to an immutable ledger.

Abstract: One example method includes receiving a request for performance of a cryptographic task, such as the encryption of data, associating a cryptographic standard and a user policy with the cryptographic task, associating a discretionary factor with the cryptographic task, and selecting, based on the cryptographic standard, the user policy, and the discretionary factor, a cryptographic mechanism to perform the cryptographic task. The discretionary factor may specify a time, or window of time, when the cryptographic task should be performed.

Abstract: Energy aware data prefetching is disclosed. Data stored in a source storage may be retrieved or prefetched to a target storage based on a prefetching time predicted or estimated by an awareness engine. The awareness engine may include models configured to predict the prefetching time so that the prefetching time accounts for a cost and/or source of energy. The prefetching operation is performed at the prefetching time. The models of the awareness engine may also be configured to recommend a source for the prefetching operation from among multiple potential sources for the prefetching operation in an energy aware manner.

Abstract: Energy aware data migration and data replication is disclosed. Data stored in a source storage may be migrated or replicated to a target storage based on a migration time or replication time predicted or estimated by an awareness engine. The awareness engine may include models configured to predict the migration time or the replication time so that the migration time or replication time accounts for a cost and/or source of energy. The migration operation or replication operation is performed at the estimated migration or replication time.

Abstract: One example method includes obtaining historical green energy production data that comprises information indicating when and where green energy was generated, obtaining green energy cost data that comprises information indicating a cost of green energy at various locations in various seasons, using the historical green energy production data and the green energy cost data to identify a potential target location for migration of a dataset from a current location of the dataset, and when a cost to perform the migration is lower, by a specified threshold amount, than a cost savings expected to be realized as a result of storing the dataset at the potential target location rather than at the current location, migrating the dataset from a current location of the dataset to the potential target location.

Abstract: One example method includes receiving a request to perform a data process, determining, by a trustworthy energy awareness (TEA) module, based on energy information and user constraints, a best time to perform the data process, and conveying, by the TEA module to a recipient, information indicating the best time to perform the data process. The data process may be a data backup process, or a garbage collection process, for example.

Abstract: One example method includes receiving, by a TEA (trustworthy energy awareness) module, a request from a storage controller to identify a most energy-efficient storage array, as among a plurality of storage arrays, on which to store a set of data, determining, by the TEA module based on energy efficiency information and user constraints, the most energy efficient storage array, and identifying, by the TEA module to the storage controller, the most energy efficient storage array.

Abstract: One example method includes monitoring, by a data confidence fabric (DCF), an energy generator/provider (EGP) to determine whether the EGP is meeting its obligation to provide energy, according to terms of a service level agreement (SLA), to a customer that has executed a smart contract with the EGP, when the DCF determines that the EGP is not meeting the obligation, and depending upon a reason for the EGP failing to do so, lowering, by the DCF, a confidence score of the EGP to generate a reduced confidence score, and recording the reduced confidence score in an immutable ledger.

Abstract: Energy aware data compression is disclosed. A storage array may receive data from a client to be compressed. The storage array may request information from a compression awareness engine that is configured to estimate compression times in the context of energy source and energy cost. The storage array makes a decision to compress the data based on the estimates or response received from the compression awareness engine. The data is then compressed and stored in the storage array.