Netapp, Inc.

Other

Rukma Ameet Talwadker

Kaladhar Voruganti

Lakshmi Narayanan Bairavasundaram

Vipul Mathur

Neeraja Yadwadkar

David Slik

Gokul Soundararajan

Sai Rama Krishna Susarla

Sai Rama Krishna Susaria

Ajay Bakre

Kartheek Muthyala

Ross Ackerman

Chhavi Sharma

Dhruva Krishnamurthy

A system and computer-based method for performing a data transaction in a network storage system is provided. In an embodiment, the method includes receiving, from a client, a first data transaction request including a first data identifier associated with a first namespace protocol; determining a storage system associated with the first data identifier and associated with a second namespace protocol; determining a second data identifier associated with the first data identifier, wherein the second data identifier conforms to the second namespace protocol; and forwarding a second data transaction request including the second data identifier to the storage system based on determining an association between the second data identifier and the first data identifier, wherein the first namespace protocol and the second namespace protocol are different.

Federated namespace of heterogeneous storage system namespaces

A method and system for server fault analysis are described. A server configuration analyzer retrieves event logs collected from a server with a fault such as a misconfiguration. These event logs can then be analyzed using metrics to identify one or more messages in the logs that indicate a cause of the fault on the server. Once the messages are identified, the server configuration analyzer retrieves remedial commands that may be used to correct the fault from a database that stores remedial commands correlated with event log messages. The server configuration analyzer can then present the messages and remedial commands to a user to assist with identifying the cause of the fault and correcting it.

Server fault analysis system using event logs

A method and system for grouping and compressing system logs are described herein. A generation module residing and executing on a computer system may be configured to generate system logs containing system data important for support purposes describing the computer system, and system log metadata for describing the system logs. A grouping module residing and executing on a computer system may be configured to group system logs based on the system log metadata. Each group of system logs may comprise two or more system logs having the same or similar system log metadata. A data container is produced for each group of system logs and the data container is compressed. System logs having the same or similar system log metadata are more likely to have similar data, thus resulting in a higher degree of compression when grouped and compressed together.

Efficient compression of system logs through metadata-based grouping

Described herein is a system and method for dynamically managing service-level objectives (SLOs) for workloads of a cluster storage system. Proposed states/solutions of the cluster may be produced and evaluated to select one that achieves the SLOs for each workload. A planner engine may produce a state tree comprising nodes, each node representing a proposed state/solution. New nodes may be added to the state tree based on new solution types that are permitted, or nodes may be removed based on a received time constraint for executing a proposed solution or a client certification of a solution. The planner engine may call an evaluation engine to evaluate proposed states, the evaluation engine using an evaluation function that considers SLO, cost, and optimization goal characteristics to produce a single evaluation value for each proposed state. The planner engine may call a modeler engine that is trained using machine learning techniques.

Proposed storage system solution selection for service level objective management

In the area of storage management, service automation can be realized through the use of 'MAPE' loop(s). A Planner (P) interacts with the Monitoring (M), Analysis (A) and Execution (E) components in a closed loop. For each new option or potential planning action the Planner (P) invokes the Analysis (A) component. The correctness, as well as effectiveness, of the planning decision is dependent on the Analysis (A) component. Embodiments can utilize an adaptive Analysis (A) component (i.e., an analysis component that can be retrained) that also associates a value of confidence and a corresponding error in the evaluation along with a predicted impact. The Planner (P) component uses this additional information for quoting the final impact of a particular planning action as part of an adaptive MAPE loop to provide improved resource utilization and resource management.

Techniques for handling modeling errors during planning

Managing service level objectives for storage workloads

Evaluating proposed storage solutions

Graph transformations are used by a data management system to correct violations of service-level objectives (SLOs) in a data center. In one aspect, a process is provided to manage a data center by receiving an indication of a violation of a service-level objective associated with the data center from a server in the data center. A graph representation and a transformations data container are retrieved by the data management system from data storage accessible to the data management system. The transformations data container includes one or more transformations. The transformation is processed to create a mutated graph from a data center representation from the graph representation. An option for managing the data center is determined as a result of evaluating the mutated graphs.

Graph transformations to correct violations of service level objections in a data center

Method of predicting an impact on a storage system of implementing a planning action on the storage system based on modeling confidence and reliability of a model of a storage system to predict the impact of implementing the planning action on the storage system

Graph transformations to correct violations of service level objectives in a data center

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Karnataka, India

Rukma Ameet Talwadker