Abstract: A method for optimizing storage configuration for future demand and a system applying the method are disclosed. The system includes a monitoring module, a storage recording module, a traffic modeling module, a rule-based decision module, and a storage management module. With performance values and utilization values provided from the monitoring module, a traffic status of data access in a particular time in the future can be generated. Then, a storage configuration with the workload requirement according to some rules can be available. The storage configuration is implemented to fulfill the requirement of the traffic status of data access.

Abstract: A method and system for storage traffic modeling in a Software Defined Storage (SDS) is disclosed. The method includes the steps of: collecting observed values of at least one performance parameter in a period of time from a storage node; learning a trend structure of the at least one performance parameter varying with time from the observed values; and providing a predicted value of one performance parameter in a particular point in time in the future. The storage node is operated by SDS software. The trend structure is adjusted based on observed values collected after the period of time. The predicted value is an output of the trend structure which has been adjusted.

Abstract: Methods for optimizing utilization of workload-consumed resources for time-inflexible workloads are disclosed. By sorting workload-consumed resource profiles representing individual workloads in one system according to an order of standard deviation or descending volume, two workload-consumed resource profiles can be combined to check if combination criteria are fulfilled. If any combination satisfies the combination criteria, corresponding workloads can be combined to share the same resource from the system. Thus, optimizing utilization of the workload-consumed resource can be achieved.

Abstract: Methods for optimizing utilization of workload-consumed resources for time-inflexible workloads are disclosed. By sorting workload-consumed resource profiles representing individual workloads in one system according to an order of standard deviation or descending volume, two workload-consumed resource profiles can be combined to check if combination criteria are fulfilled. If any combination satisfies the combination criteria, corresponding workloads can be combined to share the same resource from the system. Thus, optimizing utilization of the workload-consumed resource can be achieved.

Abstract: An adaptive fuzzy rule controlling system for a software defined storage (SDS) system to control performance parameters in a storage node is disclosed. The system includes: a traffic monitoring module, for acquiring observed values of performance parameters in the storage node; an adaptive neural fuzzy inference module, for learning a dynamic relationship between configurations of a plurality of storage devices in the storage node and the performance parameters during a period of time, and outputting fuzzy rules which is built according to the dynamic relationship; a traffic forecasting module, for providing forecasted values of the performance parameters in a particular point in time in the future; and a fuzzy rule control module, for arranging the configuration of the storage devices in the storage node in the particular point in time in the future according to the fuzzy rules and the forecasted values.

Abstract: A method for optimizing storage configuration for future demand and a system applying the method are disclosed. The system includes a monitoring module, a storage recording module, a traffic modeling module, a rule-based decision module, and a storage management module. With performance values and utilization values provided from the monitoring module, a traffic status of data access in a particular time in the future can be generated. Then, a storage configuration with the workload requirement according to some rules can be available. The storage configuration is implemented to fulfill the requirement of the traffic status of data access.

Abstract: A method and system for storage traffic modeling in a Software Defined Storage (SDS) is disclosed. The method includes the steps of: collecting observed values of at least one performance parameter in a period of time from a storage node; learning a trend structure of the at least one performance parameter varying with time from the observed values; and providing a predicted value of one performance parameter in a particular point in time in the future. The storage node is operated by SDS software. The trend structure is adjusted based on observed values collected after the period of time. The predicted value is an output of the trend structure which has been adjusted.

Abstract: An adaptive fuzzy rule controlling system for a software defined storage (SDS) system to control performance parameters in a storage node is disclosed. The system includes: a traffic monitoring module, for acquiring observed values of performance parameters in the storage node; an adaptive neural fuzzy inference module, for learning a dynamic relationship between configurations of a plurality of storage devices in the storage node and the performance parameters during a period of time, and outputting fuzzy rules which is built according to the dynamic relationship; a traffic forecasting module, for providing forecasted values of the performance parameters in a particular point in time in the future; and a fuzzy rule control module, for arranging the configuration of the storage devices in the storage node in the particular point in time in the future according to the fuzzy rules and the forecasted values.

Abstract: An adaptive quick response controlling system for a software defined storage (SDS) system to improve a performance parameter is disclosed. The system includes: a traffic monitoring module, for acquiring an observed value of the performance parameter in a storage node; an adaptive dual neural module, for learning best configurations of a plurality of storage devices in the storage node under various difference values between the observed values and a specified value of the performance parameter from historical records of configurations of the storage devices and associated observed values, and providing the best configurations when a current difference value is smaller than a threshold value; and a quick response control module, for changing a current configuration of the storage devices in the storage node as the best configuration of the storage devices provided from the adaptive dual neural module if the current difference value is not smaller than the threshold value.