Abstract: A protocol chip transmits the request from the host apparatus to a first processor through a first address translation unit. A first processor transmits a response to the request from the host apparatus, to the protocol chip through the first address translation unit. When the first processor stops processing, an instruction to transmit the request from the host apparatus to a second processor is transmitted to the protocol chip. When receiving the instruction to transmit the request from the host apparatus to the second processor, the protocol chip transmits the request from the host apparatus to the second processor through a second address translation unit. The second processor transmits the response to the request from the host apparatus to the protocol chip through the second address translation unit.

Abstract: A non-aqueous electrolyte secondary battery includes a positive electrode, a negative electrode, and a separator disposed between the positive electrode and the negative electrode. The separator has a multilayer structure in which a first filler layer containing phosphate particles, a porous resin substrate, and a second filler layer containing inorganic particles having higher heat resistance than the phosphate particles are stacked in this order from the negative electrode side. The first filler layer is disposed on the porous resin substrate in such a manner that the surface of the first filler layer faces the surface of the negative electrode. The phosphate particles have a BET specific surface area in the range of 5 m2/g or more and 100 m2/g or less.

Abstract: A data management system including a processor connected to a storage device that stores data usable for learning and inference in a machine learning model, the data management system including a compressor/decompressor for human that compresses and decompresses data to be usable for verification performed by human, in which the processor specifies data that is no longer used for learning and inference in the machine learning model among the data stored in the storage device, and compresses the specified data using the compressor/decompressor for human.

Abstract: A data compression processing system includes: a selection unit configured to select a compression processing setting to be applied to processing target data from among a plurality of compression processing settings; and a compression processing unit configured to compress the processing target data according to the compression processing setting selected by the selection unit. The selection unit is configured to: receive a condition required for compression processing of the processing target data; apply a plurality of compression processing settings applicable to the processing target data to evaluation data to perform compression processing; select, from the plurality of compression processing settings, a setting that satisfies the condition when applied to the processing target data based on a result of the compression processing for the evaluation data; and manage the selected compression processing setting in association with the condition.

Abstract: Example implementations described herein are directed to searching ML models to facilitate IO load prediction. Even if there is no existing ML model applicable to the underlying container, example implementations described herein obtain the ML model for the nearest container. Through such example implementations, the underlying systems can reduce the total ML models maintained for the prediction of IO load and can enhance ML models with enough learning data for each ML model.

Abstract: Example implementations described herein are directed to systems and methods to accelerating the time to identify the latest snapshot that is not infected by malware and swap the snapshot for an underlying volume for a host computer without needing to communicate with the storage administrator of the storage system. In example implementations described herein, an iterative process is utilized to traverse snapshots through a storage plug-in application until such a snapshot is identified. Through the example implementations described herein, such snapshots can be identified quickly in comparison to manual selection.

Abstract: Embodiments described herein are directed to a cloud-bursting system utilizing optimal resources on a remote site within the range of requirements of business operation ordered by a user. Embodiments can involve receiving user requirements for the copy operation of a storage volume, and for a determination that the copy operation of the storage volume meets the user requirements, executing the copy operation on the storage volume from the first storage system to the second storage system; and continuously adjusting the end-point to facilitate a copy speed of the storage volume that meets the user requirements.

Abstract: Example implementations described herein are directed to systems and methods to create authorization rules and configurations automatically for each microservice, each of which rules are built from the front-end use case. The example implementations involve a public facing environment on a public cloud system, and a test environment deployed on a private cloud system that is isolated from the public cloud system that mirrors the public cloud system functionality for testing deployment of applications.

Abstract: Example implementations described herein are directed to systems and methods to accelerating the time to identify the latest snapshot that is not infected by malware and swap the snapshot for an underlying volume for a host computer without needing to communicate with the storage administrator of the storage system. In example implementations described herein, an iterative process is utilized to traverse snapshots through a storage plug-in application until such a snapshot is identified. Through the example implementations described herein, such snapshots can be identified quickly in comparison to manual selection.

Abstract: Example implementations described herein are directed to management of a first storage system for an on-premise system and a second storage system for a public cloud system. Such example implementations can involve, responsive to an update to one or more of a plurality of snapshot volumes in the first storage system, updating first shared page information stored in the first storage system according to the update to the one or more snapshot volumes, the first shared page information indicative of pages shared between the plurality of snapshot volumes in the first storage system; transmitting instructions to the second storage system to copy the updates to the one or more snapshot volumes from the first shared page information to a second shared page information stored in the second storage system; and executing read or write operations made to the second storage system based on the second shared page information.

Abstract: Embodiments described herein are directed to volume creation such that the volume is created in the storage system that is closest to the group of servers that the container is likely to be launched. Through such embodiments, the volume can be created or migrated in anticipation to a launch or a relaunch of a container.

Abstract: Embodiments described herein are directed to a cloud-bursting system utilizing optimal resources on a remote site within the range of requirements of business operation ordered by a user. Embodiments can involve receiving user requirements for the copy operation of a storage volume, and for a determination that the copy operation of the storage volume meets the user requirements, executing the copy operation on the storage volume from the first storage system to the second storage system; and continuously adjusting the end-point to facilitate a copy speed of the storage volume that meets the user requirements.

Abstract: Example implementations described herein are directed to searching ML models to facilitate IO load prediction. Even if there is no existing ML model applicable to the underlying container, example implementations described herein obtain the ML model for the nearest container. Through such example implementations, the underlying systems can reduce the total ML models maintained for the prediction of IO load and can enhance ML models with enough learning data for each ML model.

Abstract: Example implementations described herein are directed to management of a first storage system for an on-premise system and a second storage system for a public cloud system. Such example implementations can involve, responsive to an update to one or more of a plurality of snapshot volumes in the first storage system, updating first shared page information stored in the first storage system according to the update to the one or more snapshot volumes, the first shared page information indicative of pages shared between the plurality of snapshot volumes in the first storage system; transmitting instructions to the second storage system to copy the updates to the one or more snapshot volumes from the first shared page information to a second shared page information stored in the second storage system; and executing read or write operations made to the second storage system based on the second shared page information.

Abstract: A computing system that can maintain reliability required of a storage program while causing the storage program to operate on an operating system (OS) is provided. A processor of the computing system executes an OS controlling a hardware device and a storage program operating on the OS and using the hardware device via the OS. The OS identifies an error status of the hardware device when receiving a notification of an error that has occurred to the hardware device, and notifies the storage program that operates on the OS of the error status when the error status satisfies a predetermined condition. The storage program determines error handling on the hardware device on the basis of the error status, and requests the OS to perform the determined error handling. The OS performs the determined error handling on the hardware device.

Abstract: Computer components, such as processors and storage devices, provide a performance and consumes an electric power within a range of an upper limit performance and an upper limit power consumption of a power state set for the component among a plurality of power states corresponding to a type of the component. A processor unit determines whether a budget power as a power consumption permitted for a target computer is equal to or more than a power consumption of the target computer or not. When the determination result is false, for at least one component of the target computer, the processor unit selects a power state based on at least one of a priority of an operation using the component and a data characteristic corresponding to the component among a plurality of types of power states corresponding to a type of the component as power state of the component.

Abstract: Example implementations described herein are directed to a storage service system providing elastic, exclusive storage service to multiple users. In the example implementations, there is a storage system service involving a plurality of storage clusters configured to be isolated from each other, each of the plurality of storage clusters configured to be directed to a public cloud, and managed by a management server. Such a storage service system can also involve storage nodes that are in a colocation data center.

Abstract: Example implementations described herein are directed to conducting data transform optimization for utilizing special hardware such as field programmable gate arrays (FPGAs) or graphics processor units (GPUs) to facilitate the processing of SQL queries, based on the characteristics of the workload of the system and the penalty of conducing the transform itself. Example implementations then process SQL queries through using special hardware or general hardware based on the determination, and also can transform SQL queries for processing on special hardware.

Abstract: Example implementations described herein are directed to systems and methods to create authorization rules and configurations automatically for each microservice, each of which rules are built from the front-end use case. The example implementations involve a public facing environment on a public cloud system, and a test environment deployed on a private cloud system that is isolated from the public cloud system that mirrors the public cloud system functionality for testing deployment of applications.

Abstract: High reliability and high performance of a storage device formed of a Dual port NVMe SSD are achieved while preventing the risk of destruction of data. The storage device includes a main memory that belongs to each of two or more clusters and that stores data related to an IO request; and a processor belonging to each of the clusters controlling accesses to the main memory. The main memory includes a first region where writing from the memory drive is permitted and a second region where the writing is prohibited. The processor selects the first region as a transfer destination related to the IO request from the memory drive when the IO request is a first request, and selects the second region as the transfer destination related to the IO request from the memory drive while permitting writing to the second region when the IO request is a second request.