International Business Machines Corporation

Arunachalam Jayaraman

Suryanarayana K Rao

Ramesh Chandra Pathak

Viren D Parikh

Agnello Mervyn Pinto

Approaches presented herein enable automated cognitive problem management in a systems environment. A first problem component is identified from a problem ticket. In response to the problem ticket, a logical relationship between the problem component and other related components is generated. While applying an automated fix to the problem component, its related components are measured for any deviation. If a deviation exists, a second problem component is identified and a causality relationship among the first and second components is established. An automated fix is applied to the second problem component and then to the first problem component based on the causality relationship. The causality relationship and order of applying the component fixes is stored for resolving future similar problem tickets.

Automated cognitive multi-component problem management

An approach is provided for predicting a reoccurrence of a failure of a critical high availability (HA) component. A real-time failover is determined to be happening based on a failure of a HA system. An actual amount of time taken by an event occurring during the failover is determined. A reference amount of time that the event is expected to take is received. Based on the actual amount of time determined to be not equal to the reference amount of time, the failure is predicted to reoccur unless a fault is repaired. Critical HA components of the HA system that are participating in the event are identified. One of the critical HA components is determined to have failed based on the fault. Self-healing is invoked to repair the fault in the critical HA component by performing a branch based decision making process on the critical HA components.

Predicting failure reoccurrence in a high availability system

An approach is provided for managing a failure of a critical high availability (HA) component. Weights are received and assigned to categories of critical HA components in a HA system. A current value indicating a performance of a component included in the identified components is obtained by periodically monitoring the components. A reference value for the performance of the component is received. A deviation between the current value and the reference value is determined. Based on the deviation, the component is determined to have failed. Based in part on the failed component, the categories, and the weights, a health index is determined in real-time. The health index indicates in part how much the component having failed affects a measure of health of the HA system.

Managing faults in a high availability system

A method, computer program product, and system for dynamic relational integrated intelligent monitoring and problem resolution of systems is provided. An IT environment is monitored for a first symptom. The first symptom is a malfunction of at least one component of a plurality of components. In response to determining a first measurement deviates from a reference value, a first component is determined to be the cause component. In response to determining a monitoring tier of the cause component is activated, a plurality of measurements is determined for the plurality of components. A component with the greatest number of activated monitoring tiers is identified and compared to the cause component. Probe data for the plurality of components is collected. A probe ripple is determined, based on one or more components affected by the malfunction. A root cause of the first symptom is reported.

Dynamic monitoring and problem resolution

An approach is provided for managing a failure of a critical high availability (HA) component in a HA system. Critical HA components are identified. Categories are assigned to the identified components and weights are assigned to the categories. A current value indicating a performance of a component included in the identified components is obtained by periodically monitoring the components. A reference value for the performance of the component is received. A deviation between the current value and the reference value is determined. Based on the deviation, the component is determined to have failed. Based in part on the failed component, the categories, and the weights, a health index is determined in real-time. The health index indicates in part how much the component having failed affects a measure of health of the HA system.

A computer-implemented method, system, and/or computer program product optimizes storage space usage. One or more processor, which implement a Smart Volume Manager, receive an input/output (I/O) client process that requests an I/O action from a file system storage device. In response to determining that the file system storage device is operating at 100% capacity, the Smart Volume Manager generates a dynamic virtual buffer disk group from multiple donor disk groups, and then redirects the I/O client process to the dynamic virtual buffer disk group, which is appended to the file system storage device to create an extended filesystem volume.

Smart volume manager for storage space usage optimization

A method, computer program product and system of preventing the unauthorized rebooting of a server having a change record, reboot password and valid reboot key. The method includes authenticating that rebooting is authorized by the change record; responsive to entering a reboot password, authenticating that a valid reboot password has been entered; and responsive to entering a reboot key, authenticating by a computer processor that a valid reboot key has been entered.

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Chennai, India

Arunachalam Jayaraman