Hewlett Packard Enterprise Development LP

International Business Machines Corporation

Prabhanjan Gururaj

Thomas Golway

Jes Kiran Chittigala

Suraj S Shirolikar

Balaji Venkatraman

Bhakthavatsala K Naidu

Guhendran Devendran

Sharath Gopinath

Grandhi Gururaja

Hari Sahaya Tiwari

Gururaja Grandhi

Ravi Mishra

Shreyas Prabhakar Gaikwad

Parag Gaur

Yashashvi Tarana

Examples of analyzing a log file are described. In an example, a log file comprising a plurality of event records is obtained from a computing device. The event records in the plurality of log files may be processed. Thereafter, an occurrence of anomaly in the log file may be ascertained.

Event records in a log file

A distributed ledger system is described. The system includes a provider to provide a plurality of infrastructure resources, a client to access a first set of the plurality of resources; and an operator platform to facilitate access to the first set of resources from the providers, including a processor to generate one or more blocks of transaction data associated with each resource in the first set of resources to update chain code and measure of usage the first set of resources measure, wherein the chain code is stored in a distributed ledger database shared between the operator platform and the client.

Distributed ledger technology platform

A system is described. The system includes a distributed ledger peer-to-peer blockchain fabric comprising a plurality of peer nodes, including a first peer node to receive a workload package, examine the workload package to determine a role of the first peer node within a cluster configuration of a first set of the plurality of peer nodes and execute the workload package at resources included in the first peer node.

Peer-to-peer blockchain fabric management mechanism

An example system including a processing resource and a memory resource. The memory resource may store instructions executable by the processing resource to receive, at a software-defined node of a computational infrastructure, a policy specifying a node metric criterion associated with executing a workload at the node; collect a present node metric corresponding to the specified node metric criterion at the node; receive a prediction, based on historical data, of a future modification to the present node metric corresponding to the specified node metric criterion at the node; determine, at the software-defined node, an eligibility value of the node to execute the workload based on the specified node metric criterion, the present node metric criterion, and the predicted future node metric criterion; and determine whether to deploy the workload to the node for execution based on the determined eligibility value.

Node eligibility determinations

Some examples described herein relate to a cluster computer system. In an example, a first node in a cluster computer system may determine that a second node has become unavailable. The first node may form a first sub-cluster with a plurality of nodes that are communicatively reachable in the cluster computer system. The first node may retain ownership over a first application running on the first node, to the exclusion of remaining nodes in the first sub-cluster, wherein retaining ownership over the first application comprises controlling access to a storage volume related to the first application. The first node may attempt to obtain ownership over a second application previously running on the second node before the second node became unavailable. In response to a successful attempt the first node may obtain ownership over the second application, to the exclusion of remaining nodes in the first sub-cluster.

Cluster computer system with failover handling

Examples disclosed herein relate performing a disaster recovery rehearsal of a workload in a three-datacenter topology. A workload may be selected on a computing system at a first datacenter location of a three-datacenter topology, for performing a disaster recovery rehearsal. The three-datacenter topology may comprise a first datacenter location, a second datacenter location and a third datacenter location. At least one of second datacenter location or third datacenter location may be selected for performing the disaster recovery rehearsal. A configuration of the workload may be cloned to generate a cloned workload. A resource may be identified in a selected datacenter location for performing the disaster recovery rehearsal. The cloned workload may be applied to the resource in the selected datacenter location, and a result of running the cloned workload on the resource may be generated. The computing system may receive the result from the selected datacenter location.

Disaster recovery rehearsal of a workload

Two or more nodes in a cluster are determined. The cluster manages one or more resources found on a device. An indication from a first node of the two or more nodes is received. The indication includes a request for an IP lease for the first node. A parity bit setting for the first node is determined. Responsive to determining the parity bit setting of the first node being set to one, an IP lease to the first node is indicated. Responsive to determining the parity bit setting of the first node being set to zero, a denial of an IP lease to the first node is indicated.

Growing community of inventors

Bengaluru, India

Prabhanjan Gururaj