International Business Machines Corporation

Krishnasuri Narayanam

Ramasuri Narayanam

Vinayaka D Pandit

Mukundan Sundararajan

Sameep Mehta

Pankaj Satyanarayan Dayama

Akshar Kaul

Sreyash Kenkre

Yedendra Shrinivasan

Balaji Viswanathan

Senthilnathan Natarajan

Ken Kumar

Vijay Ekambaram

Amith Singhee

Venkatraman Ramakrishna

Seep Goel

Dhinakaran Vinayagamurthy

Abhishek Singh

Vishnu Choudhary

Sarvesh S Patel

Subhojit Roy

Manish Anand Bhide

Padmanabha Venkatagiri Seshadri

Gopal Kumar Bhageria

Ashok Pon Kumar Sree Prakash

Manish Kesarwani

Ashish Kumar Mathur

Vikas Chandan

Sumanta Mukherjee

Sandeep Nishad

Phani Kumar Ayyagari V U

Jasti Purnachandra Rao

Kushal S Patel

Sasikanth Eda

Sachin Chandrakant Punadikar

Munish Goyal

Angelo De Caro

Phani Kumar V U Ayyagari

Harshit Kumar

Ermyas Abebe

Gagandeep Singh

Deepak Vijaykeerthy

Raphael Ezry

Shashank Mujumdar

Aswin Kannan

Parameswaran Selvam

Francisco Barahona

Surya Shravan Kumar Sajja

Soujanya Soni

Ravi Mandalika

Joao P M Goncalves

Rahul Rahul

Yining Hu

A system and method for a multi-party computation (MPC) is provided. In implementations, a method includes identifying a blockchain network and a computing device to perform an MPC based on an index; generating an MPC request including a function to be performed by the blockchain network and the computing device, data required for the function, and a verification policy defining a verification protocol to be performed by the blockchain network and the computing device; sending the MPC request to the blockchain network and the computing device; and receiving responses from a representative computing node of the blockchain network and the computing device, wherein each of the responses includes: an output of an MPC protocol performed by the blockchain network and the computing device to jointly compute the function while keeping the inputs private from one another and private from the computing system; and a proof based on the verification policy.

Collaborative computation across blockchain networks

A computer program product comprises a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by one or more processors to cause the one or more processors to construct an interaction graph based in part on a distributed application of a cloud platform comprised of a plurality of services; extract one or more sub-graphs from the interaction graph by mining the interaction graph using one or more feasibility validation tests; estimate a resource pressure metric for the one or more sub-graphs, wherein the resource pressure metric is at least one of a prospective resource pressure metric and a retrospective resource pressure metric, the resource pressure metric being associated with each service on one or more respective dependent services; construct a degradation plan for a least-cost sub-graph in accordance with the estimated resource pressure metric; and apply the degradation plan to the distributed application and the cloud platform.

Degradation planning and mitigation in hybrid cloud environments

A computer-implemented method for automatically and digitally securing an asset is disclosed. The computer-implemented method includes hashing a secret preimage to produce a secret hash. The computer-implemented method further includes locking a digital collateral asset with the secret hash for a first predetermined period of time. The computer-implemented method further includes locking a digital loan asset with the secret hash for a second predetermined period of time, wherein the second predetermined period of time is less than the first predetermined period of time. The computer-implemented method further pledging a digital loan repayment up to the amount of time of the first predetermined period of time plus the second predetermined period of time. The computer-implemented method further includes responsive to receiving a pledge for the digital loan repayment, automatically unlocking the digital collateral asset for a borrower of the digital loan asset.

Method for digitally securing an asset

A method of replicating the volume of stateful-sets by receiving input request types for each container of a number of containers, extracting control flow paths and mapping the input request types to the extracted control flow paths. The method learns control flow path-to-volume mappings by computing, for each control flow path of the number of control flow paths, (i) one or more associations between the control flow path and one or more volumes and (ii) a nature of access of the one or more volumes. For each pod replica a dominant control flow path is computed by computing a most common control flow path and a most common volume corresponding to the most common control flow path. A request for volume management of an identified most common volume is received and the identified most common volume is replicated or a its view is created.

Volume replication of stateful-sets

A group of transactions to be rolled back in a blockchain ledger can be identified. Whether the transactions in the group are eligible to be rolled back together can be determined. Other transactions can be recommended, and the other transactions may be added to the group to be rolled back together. A rollback transaction can be added to the blockchain ledger that rolls back effect of the transactions, where the rollback transaction has zero or more inputs and zero or more output, the rollback transaction having at least one selected from at least one input and at least one output. An unspent output of a transaction in the group is provided as an input of the rollback transaction. Input of a transaction in the group, that is not an output of another transaction in the group, is provided as an output of the rollback transaction.

Blockchain ledger rollback

An example operation may include one or more of connecting, by a lead peer, to a blockchain network of an organization configured to store assets, receiving, by the lead peer, from an ordering server, a block that contains a plurality of chaincode transactions belonging to plurality of peers of the blockchain network, applying, by the lead peer, a filter to the block, determining, by the lead peer, a recipient peer of the block in response to the application of the filter, receiving, by the lead peer, a validation result of the block from the recipient peer, and committing, by the lead peer, the block to the blockchain network.

Sparse peer with transient participation

An example operation may include one or more of connecting, by a sparse peer, to a blockchain network of a plurality of peers controlled by orderers, selecting, by the sparse peer, a subset of data to be replicated, specifying, by the sparse peer, data selection filters based on the selected subset of the data, providing, by the sparse peer, the selection filters to the plurality of the peers, receiving, by the sparse peer, blocks matching the selection filters over a gossip protocol from the plurality of the peers, constructing, by the sparse peer, a local ledger of the sparse peer from the received blocks, and bootstrapping the sparse peer based on the local ledger.

An example operation may include one or more of detecting, by a blockchain (BC) node, a contract update on a blockchain, determining, by the BC node, at least one charge affected by the contract update, executing, by the BC node, a smart contract to re-generate an invoice based on the at least one charge, and recording the invoice on a ledger of the blockchain.

Dynamic updates of decentralized invoices

An example operation may include one or more of connecting, by a lead peer, to a blockchain network of an organization configured to store assets, receiving, by the lead peer, a block from an ordering server, that contains a plurality of chaincode transactions belonging to a plurality of peers of the blockchain, applying, by the lead peer, a filter to the block, determining, by the lead peer, a recipient peer of the block in response to the application of the filter, and receiving, by the lead peer, a notification of validation and commitment of the block to the blockchain by the recipient peer.

An example operation may include one or more of querying, via an application programming interface (API), a blockchain ledger for attributes of a shipment by a carrier from an origin location to a destination location, predicting, via an artificial intelligence (AI) model, one or more future events that will occur during the shipment based on the attributes of the shipment retrieved from querying the blockchain ledger, generating, via a smart contract, an accelerated e-invoice based on the one or more future events predicted by the AI model, and storing the accelerated e-invoice on the blockchain ledger.

Accelerated invoicing using predictive freight events

An example operation may include one or more of monitoring, by a blockchain node, a delivery of a service to a first node from a second node based on a service contract and an order retrieved from a blockchain, determining, by the blockchain node, an incremental charge for a partial delivery of the service based on the monitoring, and executing, by the blockchain node, a smart contract to issue the incremental charge for the partial delivery of the service, and responsive to a resolution of a dispute raised for the incremental charge, add the incremental charge to an incremental invoice.

Optimization of trusted invoicing

Methods, systems, and computer program products for generating multi-dimensional host specific storage tiering are provided herein. A computer-implemented method includes maintaining information of a plurality of storage resources connected to at least one host device; configuring, based at least in part on the information, a plurality of partitions of the storage resources, each partition comprising multiple storage tiers, wherein the plurality of partitions is configured to provide physical migration paths between the multiple storage tiers within each of the partitions; detecting a change to the plurality of storage resources; and reconfiguring the plurality of partitions based at least in part on the detected change.

Generating multi-dimensional host-specific storage tiering

Disclosed are techniques for programmatically mapping workloads for storage in different classes of a networked computer data storage environment, using a set of rules or data storage hints and properties of the workloads themselves. With the set of data storage hints, metadata sets are assigned to files of the incoming workloads to the networked computer data storage environment based on the properties of the workloads. Then, a target storage class is determined for each file of the workload from the plurality of storage classes present in the networked computer data storage environment. The workload files are then mapped to a logical bucket that is associated with the plurality of storage classes through a shared namespace, with the assigned metadata set for a file cueing the filesystem modules as to which storage class within the logical bucket to direct the various files of the workload to.

Programmatically mapping workloads to networked object storage

A method for deduplicating a distributed ledger, a blockchain, and a computer program product. One embodiment may comprise splitting a new block on a distributed ledger into a plurality of partitions, comparing the plurality of partitions to a deduplication (dedup) map, replacing a first partition of the plurality of partitions with a first reference to the dedup map to produce a transformed block, and communicating the transformed block via a network interface to one or more peers.

Data deduplication in blockchain platforms

Methods, systems, and computer program products for memory protection in hypervisor environments are provided herein. A method includes maintaining, by a memory management layer of a hypervisor environment, a blockchain-based hash chain associated with a page table of the memory management layer, the page table corresponding to a plurality of memory pages; and verifying, by the first memory management layer, content obtained in connection with a read operation for a given one of the plurality of memory pages based at least in part on hashes maintained for the given memory page in the blockchain-based hash chain.

Memory protection in hypervisor environments

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Bengaluru, India

Krishnasuri Narayanam