Abstract: A method for determining credibility of content in a number of documents includes: obtaining topics from each document; for each document, generating topic combinations, each topic combination being a subset of the topics of the document; for each topic combination, obtaining a summary from the corresponding document; performing a semantic similarity test on each pair of two summaries that are respectively from two documents, so as to obtain a similarity percentage between the two summaries; for a group of the topic combinations that are identical combinations of topic(s), calculating a credibility score for the group based on the similarity percentage(s) calculated for the summaries that correspond to the topic combinations in the group.

Abstract: A SSD caching system for hybrid storages is disclosed. The caching system for hybrid storages includes: a Solid State Drive (SSD) for storing cached data, separated into a Repeated Pattern Cache (RPC) area and a Dynamical Replaceable Cache (DRC) area; and a caching managing module, including: an Input/output (I/O) profiling unit, for detecting I/O requests for accesses of blocks in a Hard Disk Drive (HDD) during a number of continuously detecting time intervals, and storing first data corresponding to first blocks being repeatedly accessed at least twice in individual continuously detecting time intervals to the RPC area sequentially; and a hot data searching unit, for detecting I/O requests for accesses of a HDD during a independently detecting time interval, and storing second data corresponding to second blocks being accessed at least twice in the independently detecting time interval to the DRC area sequentially.

Abstract: A system for extending life expectancy of disks in a cloud-based service system and the system using the method are disclosed. The present invention uses LSTM modeling and the k-means clustering algorithms to find out performance limit and target lifespan for a cluster of disks assigned for a specific workload running over the cloud-based service system. The disks can be predicted to have a minimum lifetime and the requirement of the workload can be satisfied. Meanwhile, the minimum lifetime is the longest life the disk can last.

Abstract: A method for data protection in a cloud-based service system is disclosed. The method includes the steps of: A. collecting historical operating data of storage devices in the cloud-based service system; B. building up a life expectancy model and a next-7-days failure probability model by the collected operating data; C. inputting operating data in the past 24-hours into the life expectancy model and the next-7-days failure probability model for every storage device to obtain ranges of expected lifespans in respective groups and corresponding failure probabilities; and D. backing up data in the storage devices according to the results of step C.

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 for deploying storage system resources with learning of workloads applied to a storage system is disclosed. The method utilizes state-action fuzzy rules and action-reward fuzzy rules for the storage system to learn a best dynamic deployment of resources from the parameters of workloads. No more human interference is required. It reduces work loading of the storage system administrator and enhances performance of the storage system.

Abstract: A system for extending life expectancy of disks in a cloud-based service system and the system using the method are disclosed. The present invention uses LSTM modeling and the k-means clustering algorithms to find out performance limit and target lifespan for a cluster of disks assigned for a specific workload running over the cloud-based service system. The disks can be predicted to have a minimum lifetime and the requirement of the workload can be satisfied. Meanwhile, the minimum lifetime is the longest life the disk can last.

Abstract: A method for determining data in cache memory of a cloud storage architecture and a cloud storage system using the method are disclosed. The method includes the steps of: A. recording transactions from cache memory of a cloud storage during a period of time in the past, wherein each transaction comprises a time of recording, or a time of recording and cached data been accessed during the period of time in the past; B. assigning a specific time in the future; C. calculating a time-associated confidence for every cached data from the transactions based on a reference time; D. ranking the time-associated confidences; and E. providing the cached data with higher time-associated confidence in the catch memory, and removing the cached data in the cache memory with lower time-associated confidence when the cache memory is full before the specific time in the future.

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: A method for data protection in a cloud-based service system is disclosed. The method includes the steps of: A. collecting historical operating data of storage devices in the cloud-based service system; B. building up a life expectancy model and a next-7-days failure probability model by the collected operating data; C. inputting operating data in the past 24-hours into the life expectancy model and the next-7-days failure probability model for every storage device to obtain ranges of expected lifespans in respective groups and corresponding failure probabilities; and D. backing up data in the storage devices according to the results of step C.

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: A storage system having nodes with light weight containers is disclosed by the present invention. The storage system includes: at least one cluster, having a number of nodes linked to one another, each node having: a node host, operated by a storage operating system, and installed with a number of service containers, which are used for providing specific services to clients and/or operations of the node out of the service containers; and at least one node storage device, operated by the storage operating system, for providing storing and accessing of data for the service containers; and a messaging module, linked to each node host, for bridging the node hosts and/or node hosts in different clusters. Coarse-grained REST APIs are used for communication between any two service containers to reduce chatty communication in the storage system.

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: A method for deploying storage system resources with learning of workloads applied to a storage system is disclosed. The method utilizes state-action fuzzy rules and action-reward fuzzy rules for the storage system to learn a best dynamic deployment of resources from the parameters of workloads. No more human interference is required. It reduces work loading of the storage system administrator and enhances performance of the storage system.

Abstract: A workload-aware I/O scheduler in a Software-Defined Hybrid Storage (SDHS) system is disclosed. The scheduler includes a queue managing module, a workload property database, a traffic monitoring module, and a traffic modeling module. The queue managing module further has a request receiving sub-module, a request controlling sub-module, and a request dispatching sub-module. With the operations of the queue managing module, requests for accesses of HDDs and SSDs can be separately queued. Thus, performance of the SDHS can be improved. Meanwhile, the scheduler can be used for requests from a number of workloads at the same time while meet performance requirement of each workload.

Abstract: A storage system having nodes with light weight containers is disclosed by the present invention. The storage system includes: at least one cluster, having a number of nodes linked to one another, each node having: a node host, operated by a storage operating system, and installed with a number of service containers, which are used for providing specific services to clients and/or operations of the node out of the service containers; and at least one node storage device, operated by the storage operating system, for providing storing and accessing of data for the service containers; and a messaging module, linked to each node host, for bridging the node hosts and/or node hosts in different clusters. Coarse-grained REST APIs are used for communication between any two service containers to reduce chatty communication in the storage system.

Abstract: A workload-aware I/O scheduler in a Software-Defined Hybrid Storage (SDHS) system is disclosed. The scheduler includes a queue managing module, a workload property database, a traffic monitoring module, and a traffic modeling module. The queue managing module further has a request receiving sub-module, a request controlling sub-module, and a request dispatching sub-module. With the operations of the queue managing module, requests for accesses of HDDs and SSDs can be separately queued. Thus, performance of the SDHS can be improved. Meanwhile, the scheduler can be used for requests from a number of workloads at the same time while meet performance requirement of each workload.

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 SSD caching system for hybrid storages is disclosed. The caching system for hybrid storages includes: a Solid State Drive (SSD) for storing cached data, separated into a Repeated Pattern Cache (RPC) area and a Dynamical Replaceable Cache (DRC) area; and a caching managing module, including: an Input/output (I/O) profiling unit, for detecting I/O requests for accesses of blocks in a Hard Disk Drive (HDD) during a number of continuously detecting time intervals, and storing first data corresponding to first blocks being repeatedly accessed at least twice in individual continuously detecting time intervals to the RPC area sequentially; and a hot data searching unit, for detecting I/O requests for accesses of a HDD during a independently detecting time interval, and storing second data corresponding to second blocks being accessed at least twice in the independently detecting time interval to the DRC area sequentially.

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.