Abstract: An approach for managing workload deployment in a distributed network, including edge computing is provided. The approach includes deploying several modules, such as, EMM (energy management module), LDM (localized deployment manager) and EDM (edge deployment manager). These modules will be constantly monitoring and managing the energy consumption at the edge nodes under their purview and communicate with other modules to develop a holistic energy management system (e.g., energy policies, energy algorithms, energy plans, etc.) to ensure the most effective energy management of workload is implemented.

Abstract: An example operation may include one or more of storing a full-step hash of a data file and a reduced-step hash of the data file within a data block of a hash-linked chain of blocks of a blockchain, receiving a request from a client application to verify the data file, determining whether to provide the full-step hash of the data file or the reduced-step hash of the data file based on the request, and in response to determining to provide the reduced-hash, transmitting the reduced-step hash of the data file to the client application.

Abstract: Using interaction profiling data and deployment state data of a software application, an observability module configuration plan is generated, the configuration plan comprising an event to be observed and an observability parameter. Using infrastructure state estimation data of the infrastructure on which the software application is executing, the observability module configuration plan is adjusted, the adjusting resulting in an adjusted reconfiguration plan, the adjusted reconfiguration plan comprising a second event to be observed, the second event associated with a node failure within the infrastructure. A set of observability modules is reconfigured according to the adjusted reconfiguration plan, the reconfiguring comprising causing an observability module in the set of observability modules to be activated to monitor the event.

Abstract: An approach for managing workload deployment in a distributed network, including edge computing is provided. The approach includes deploying several modules, such as, EMM (energy management module), LDM (localized deployment manager) and EDM (edge deployment manager). These modules will be constantly monitoring and managing the energy consumption at the edge nodes under their purview and communicate with other modules to develop a holistic energy management system (e.g., energy policies, energy algorithms, energy plans, etc.) to ensure the most effective energy management of workload is implemented.

Abstract: Systems, methods and/or computer program products for dynamically generating interactive content at an edge node of an edge network. For each set of video frames received, altered frames cached by the edge network can be fetched (when available) and reused to deliver interactive content to users. Where cached content is not present or distinctly different content from previously delivered content is intended for delivery to user(s), edge node identifies hotspots within video frames to insert interactive content, defining a timing and location for insertion, selects content consistent with user insights and/or preferences as well as an interaction types and placements known to be preferred by the user requesting the video frames. The edge node uses video creation services to alter the incoming video frames into altered video frames, inserts selected content at corresponding hotspots within the video and applies the interaction types for presenting the content to the user.

Abstract: An example operation may include one or more of receiving a data block for storage on a blockchain from an orderer node, the data block comprising a full-step hash of a storage request and a reduced-step hash of the storage request, performing an approximate hash verification on the data block based on the reduced-step hash of the storage request included in the data block, and in response to a success of the approximate hash verification, committing the data block among a hash-linked chain of data blocks stored within a distributed ledger of a blockchain.

Abstract: Provided is a method for selecting a service provider for an application. The method includes detecting a plurality of available service providers for an application running on a computing device. The method further includes comparing service information for each of the plurality of service providers to a set of service provider selection factors for the application. The method further includes selecting a first service provider of the plurality of service providers to provide service for the application.

Abstract: Methods, systems, and computer program products for distributed scheduling in container orchestration engines are provided herein. A computer-implemented method includes: configuring a plurality of entities of a container-based computing environment to perform a distributed scoring process, wherein, for a given one of the entities, the distributed scoring process comprises: (i) obtaining information corresponding to a plurality of workloads from a database that is accessible to the other entities, (ii) generating, based on the information, respective scores for at least a portion of the plurality of workloads, and (iii) publishing the generated scores to the database; and selecting, by a centralized scheduler of the container-based computing environment, at least one of the entities to host at least a given one of workloads based at least in part on the generated scores in the database.

Abstract: An example operation may include one or more of receiving a data block for storage on a blockchain from an orderer node, the data block comprising a full-step hash of a storage request and a reduced-step hash of the storage request, performing an approximate hash verification on the data block based on the reduced-step hash of the storage request included in the data block, and in response to a success of the approximate hash verification, committing the data block among a hash-linked chain of data blocks stored within a distributed ledger of a blockchain.

Abstract: Methods, systems, and computer program products for multi-controller declarative fault management and coordination for microservices are provided herein. A computer-implemented method includes processing information pertaining to at least one fault impacting multiple resources within a given system, wherein respective portions of the multiple resources are managed by multiple independent controllers; determining, by each of at least a portion of the multiple independent controllers and based at least in part on the processing of the information, one or more desired resource states and one or more remediation actions; generating, based at least in part on one or more of the determined desired resource states and the determined remediation actions, a sequential ordering of the determined remediation actions to be carried out by the at least a portion of the multiple controllers; and automatically initiating execution of the determined remediation actions in accordance with the generated sequential ordering.

Abstract: An example operation may include one or more of receiving a location of an output stored on a data structure of a blockchain, where the location comprises a path of hashes generated by a reduced-step hash instead of a full-step hash of the blockchain, performing an approximate hash verification on the path of hashes based on the reduced-step hash values to verify whether the output is unused, and in response to a determination that the output is unused as a result of the approximate hash verification, approving a use of the output by a client associated with the output.

Abstract: An ensemble of autoencoder models can be trained using different seeds. The trained ensemble of autoencoder models can be run on new time series data to generate a prediction associated with the new time series data. The new time series data can include multiple dimensions per time step. Reconstruction errors can be determined for the prediction. Dimensions having highest reconstruction errors can be selected among the multiple dimensions based on a threshold. The prediction can be segmented based on bursts of the reconstruction errors over time, where temporal segments can be obtained. At least one common pattern including a set of dimensions among the selected dimensions across the temporal segments can be obtained to represent a failure fingerprint.

Abstract: The present disclosure relates to the enforcement of a smart contract execution hierarchy on a blockchain. A method includes receiving a submission of a first smart contract to be appended to a blockchain, comparing a priority value of the first smart contract to a priority value of a second smart contract previously appended to the blockchain, determining based on the comparison that the first smart contract has a lower priority than the second smart contract, comparing the terms of the first smart contract to the terms of the second smart contract, determining at least one of an overlap and a conflict between the terms of the first smart contract and the terms of the second smart contract, resolving any terms of the first smart contract that are determined to overlap or conflict with the terms of the second smart contract, and appending the resolved first smart contract to the blockchain.

Abstract: An ensemble of autoencoder models can be trained using different seeds. The trained ensemble of autoencoder models can be run on new time series data to generate a prediction associated with the new time series data. The new time series data can include multiple dimensions per time step. Reconstruction errors can be determined for the prediction. Dimensions having highest reconstruction errors can be selected among the multiple dimensions based on a threshold. The prediction can be segmented based on bursts of the reconstruction errors over time, where temporal segments can be obtained. At least one common pattern including a set of dimensions among the selected dimensions across the temporal segments can be obtained to represent a failure fingerprint.

Abstract: An example operation may include one or more of receiving, from a blockchain peer node, a sequence of blocks stored in a hash-linked chain of blocks on a distributed ledger, where each block in the sequence of blocks includes a reduced-step hash of block content from a previous block in the sequence, performing an approximate hash verification on the reduced-step hashes stored among the sequence of blocks, and determining whether the sequence of blocks has been tampered with based on the approximate hash verification on the reduced-step hashes.

Abstract: An example operation may include one or more of storing a full-step hash of a data file and a reduced-step hash of the data file within a data block of a hash-linked chain of blocks of a blockchain, receiving a request from a client application to verify the data file, determining whether to provide the full-step hash of the data file or the reduced-step hash of the data file based on the request, and in response to determining to provide the reduced-hash, transmitting the reduced-step hash of the data file to the client application.

Abstract: An example operation may include one or more of storing a full-step hash of a data file and a reduced-step hash of the data file within a data block of a hash-linked chain of blocks of a blockchain, receiving a request from a client application to verify the data file, determining whether to provide the full-step hash of the data file or the reduced-step hash of the data file based on the request, and in response to determining to provide the reduced-hash, transmitting the reduced-step hash of the data file to the client application.

Abstract: Methods, systems, and computer program products for multi-controller declarative fault management and coordination for microservices are provided herein. A computer-implemented method includes processing information pertaining to at least one fault impacting multiple resources within a given system, wherein respective portions of the multiple resources are managed by multiple independent controllers; determining, by each of at least a portion of the multiple independent controllers and based at least in part on the processing of the information, one or more desired resource states and one or more remediation actions; generating, based at least in part on one or more of the determined desired resource states and the determined remediation actions, a sequential ordering of the determined remediation actions to be carried out by the at least a portion of the multiple controllers; and automatically initiating execution of the determined remediation actions in accordance with the generated sequential ordering.

Abstract: An example operation may include one or more of receiving a location of an output stored on a data structure of a blockchain, where the location comprises a path of hashes generated by a reduced-step hash instead of a full-step hash of the blockchain, performing an approximate hash verification on the path of hashes based on the reduced-step hash values to verify whether the output is unused, and in response to a determination that the output is unused as a result of the approximate hash verification, approving a use of the output by a client associated with the output.

Abstract: A blockchain configuration may be used to store a distributed ledger for information security and accessibility. One example method of operation may include one or more of logging an asset in a blockchain, identifying a sub-asset linked to the asset, creating a use constraint for the sub-asset, logging the use constraint associated with the sub-asset, and during an access attempt of the sub-asset, prohibiting access to the sub-asset based on the use constraint.