International Business Machines Corporation

Ca, Inc.

Computer Associates Think, Inc.

Jes Kiran Chittigala

Ravi Shankar

Perinkulam I Ganesh

Esdras E Cruz-Aguilar

Michael Burton

Taru Varshney

Santhosh Joshi

James Anthony Pafumi

Denise Marie Genty

Corradino Jones

Srirama R Kucherlapati

Vidya Ranganathan

Juliet C Candee

Prabhanjan Gururaj

Ravikiran Moningi

Venkateshwar R Yerravalli

Bradley J Smith

Suraj S Shirolikar

Michael S Fuller

Srikanth Thanneeru

Adhish Kapoor

Aruna S Chittigala

Described are techniques for data integrity monitoring of point-in-time backups. The techniques include obtaining a point-in-time backup of a virtual computing resource that executes in a computing environment which is monitored by a controller component of a disaster recovery system. The techniques further include starting a temporary virtual computing resource in a virtual private network from the point-in-time backup of the virtual computing resource. The techniques further include establishing communication between the controller component and the temporary virtual computing resource to enable the controller component to initiate an integrity check of data included in the point-in-time backup. The techniques further include, in response to an indication that the point-in-time backup passed the integrity check, preserving the point-in-time backup as a validated safe backup of the virtual computing resource.

Data integrity monitoring

A method, computer system, and computer program product area provided. A computer transmits a query command to a storage descriptor area of a first disk. The first disk belongs to a dual-site data replication system. The dual-site data replication system provides active-active access to a volume of data stored in an active disk and replicated in a backup disk. The computer receives a response to the query command. The response indicates the active disk and the backup disk for the dual-site data replication system. The computer controls an additional copy of the volume of data at a further remote site based on the active disk.

Integrating mirrored storage to remote replication site

Examples of techniques for failure detection for central electronics complex (CEC) group management are described herein. An aspect includes issuing a first virtual input/output server (VIOS) probe to a hardware management console (HMC) of a central electronics complex (CEC) group. Another aspect includes receiving a first response packet that includes health data corresponding to a plurality of VIOSes. Another aspect includes determining, based on the first response packet, that cluster down is indicated on a first VIOS. Another aspect includes, based on determining that cluster down is indicated on the first VIOS, getting a VIOS state for the first VIOS from the HMC. Another aspect includes determining based on the VIOS state that the first VIOS is in a down state and determining that the first VIOS is unhealthy. Another aspect includes updating a health data entry corresponding to the first VIOS to indicate that the first VIOS is unhealthy.

Failure detection for central electronics complex group management

In a system having at least two data storage and processing sites, each capable of alternatively serving as a primary site and a backup or target site, disaster recovery migration is optimized by cognitively analyzing at least one system parameter. Using machine learning, at least one pattern of that system related parameter is predicted, and planned or unplanned migration procedures are performed based on the predicted parameter patterns. The analyzed parameter may be data traffic at the sites, and the predicted data traffic pattern is used to assign primary and backup site status to those sites. The analyzed parameter may be the occurrence of events or transactions at the sites, and the predicted event or transaction patterns may be used to determine times of disaster recovery procedure processing so as to not interrupt a critical event or transaction.

Traffic and geography based cognitive disaster recovery

A system and method for automatic and dynamic virtual machine (VM) group membership adjustment to identify and maintain VM groupings for maintaining a data consistency and availability at a computer recovery site in response to a disaster event. Based on a hybrid approach of network and storage analytics using cognitive methods, the system automatically groups the VMs based on patterns of data exchanged between the VMs. The system can store the patterns found during the analysis, find the critical elements in those patterns (like VMs playing the key role of DB server), match the patterns and use them in case a detected failure to make the system functional again at the recovery site. The system stores the communications traffic patterns learned during the network analysis and uses analytics to form or change the VM grouping, and can perform the pattern matching to quickly readjust the group.

Automated and dynamic virtual machine grouping based on application requirement

A processor, based on detection of a configuration change to a central electronics complex (CEC) in a CEC group, automatically creates a monitoring network within the CEC. Automatically creating the monitoring network includes the processor issuing a request via a hardware management console (HMC) to the CEC to create a virtual switch implementing a virtual local area network (VLAN). The processor also issues a request via the HMC to the CEC to create, on a virtual input-output server (VIOS) hosted in the CEC, a virtual trunk adapter connected to the VLAN. In addition, the processor issues a request via the HMC to the CEC to create, on each of a plurality of logical partitions (LPARs) hosted in the CEC, a virtual network adapter connected to the VLAN. The processor can employ the network, for example, to monitor health of the LPARs and VIOS within the CEC via the HMC.

Automated deployment of a private monitoring network

Aspects include prefetching a plurality of high-level information, high-level metadata, low-level metadata, and low-level information including a plurality of components associated with a monitored entity for disaster recovery. A subsequent instance of the high-level information, the high-level metadata, and the low-level metadata is requested. The subsequent instance of the high-level information is fetched based on detecting a change in the high-level metadata. A subsequent instance of one or more of the components of the low-level information corresponding to one or more changes in the low-level metadata is fetched for updating a plurality of disaster recovery data of the monitored entity in a persistent database.

Disaster recovery data fetching control

Examples of techniques for failure detection for central electronics complex (CEC) group management are described herein. An aspect includes issuing a first logical partition (LPAR) probe to a hardware management console (HMC) of a central electronics complex (CEC) group, wherein the CEC group comprises a plurality of LPARs. Another aspect includes receiving a first response packet from the HMC corresponding to the first LPAR probe, wherein the first response packet comprises health data corresponding to a first LPAR of the plurality of LPARs. Another aspect includes storing the health data corresponding to the first LPAR in a first health data entry corresponding to the first LPAR. Another aspect includes, for a second LPAR of the plurality of LPARs that was not included in the first response packet, updating a second health data entry corresponding to the second LPAR to indicate that the second LPAR is healthy.

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.

Using dynamic host configuration protocol to protect data

Disclosed aspects relate to upgrade management for a shared pool of configurable computing resources having a set of logical partition (LPAR) nodes which includes a set of established members. Performance of a live kernel update (LKU) operation may be initiated with respect to a first original LPAR node. Generation of a first surrogate LPAR node to succeed the first original LPAR node may be initiated. The first surrogate LPAR node may be identified as a new original LPAR node. The first surrogate LPAR node may be joined with the set of LPAR nodes. In response to the first surrogate LPAR node joining the set of LPAR nodes, the first surrogate LPAR node may be identified as a new surrogate LPAR node. The first surrogate LPAR node may be established as a first established member, thereby removing the first original LPAR node from the set of established members.

Upgrade management for a shared pool of configurable computing resources

Operating system update management for a shared pool of configurable computing resources having a plurality of logical partitions (LPARs). An operating system update request may be received. A set of original shared portions and a set of original individualized portions may be classified with respect to a set of original files on a set of original nodes. A set of original overlay links may be created for the set of original files. A set of operating system network traffic may be routed using the set of original overlay links. A set of successor files may be established on a set of surrogate nodes. A set of successor overlay links may be created for the set of successor files. The set of operating system network traffic may be routed using the set of successor overlay links in place of the set of original overlay links.

Operating system update management

Responsive to a cluster manager for a particular node from among multiple nodes allocating at least one leased resource for a resource group for an application workload on the particular node, on fallover of the resource group from another node to the particular node, setting a timer thread, by the cluster manager for the particular node, to track an amount of time remaining for an initial lease period of the at least one leased resource. Responsive to the timer thread expiring while the resource group is holding the at least one leased resource, maintaining, by the cluster manager for the particular node, the resource group comprising the at least one leased resource for an additional lease period and automatically incurring an additional fee, only if the particular node has the capacity to handle the resource group at a lowest cost from among the nodes.

Managing use of lease resources allocated on fallover in a high availability computing environment

Provided are techniques for the fast and reliable distribution of security keys within a cluster of computing devices, or computers. One embodiment provides a method for secure distribution of encryption keys, comprising generating a symmetric key for the encryption of communication among a plurality of nodes of a cluster of nodes; encrypting the symmetric key with a plurality of public keys, each public key corresponding to a particular node of the plurality of modes, to generate a plurality of encrypted symmetric keys; storing the plurality of encrypted symmetric keys in a central repository; and distributing the encrypted symmetric keys to the nodes such that each particular node receives an encrypted symmetric key corresponding to a corresponding public key of the particular node.

Security key distribution in a cluster

The invention provides a system and method for tracking memory information associated with dynamically loaded kernel modules with the help of a tracking system. The tracking system defines its own kernel memory allocation functions. Whenever, a dynamic kernel module is loaded/unloaded into/from the kernel space, these newly defined functions are called in response to kernel memory allocation/de-allocation requests from the kernel module. The newly defined functions are responsible for allocating and de-allocating kernel memory, as well as, keeping track of information relating to the kernel memory allocations/de-allocations. The tracked information may be used to identify the source of kernel memory leaks.

Growing community of inventors

Kukatpally, India

Jes Kiran Chittigala