Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.