Abstract: An illustrative method for performing a risk scenario assessment and remediation may include identifying, based on posture data associated with a compute environment, one or more compute resources deployed in the compute environment that are configured to be connected to a network, accessing runtime workload data associated with the one or more compute resources representative of network activity for the one or more compute resources, and performing, based on the posture data and the runtime workload data, a remediation operation associated with the one or more compute resources.

Abstract: Example systems and methods monitor a cloud compute environment. An example method includes an agent obtaining a data packet from an interface in the cloud compute environment, the data packet including a source address and a non-endpoint destination address; determining, based on the non-endpoint destination address and routing information for the data packet, an endpoint destination address associated with the non-endpoint destination address of the data packet; modifying the data packet by replacing the non-endpoint destination address with the endpoint destination address; and providing, based on the modified data packet, monitoring data to a data platform.

Abstract: In some examples, a system computes respective values for corresponding data value indicators added to and removed from a deduplication data store in which duplicated data values have been eliminated, where each respective data value indicator of the data value indicators represents presence of a unique data value in the deduplication data store. The system updates an estimator based on the respective values, to reflect an addition of a first data value indicator to the deduplication data store and a removal of a second data value indicator from the deduplication data store. The system computes, using the updated estimator, a parameter relating to data deduplication at the deduplication data store.

Abstract: An illustrative method for monitoring a cloud environment may include identifying, by at least one computing device and based on a scan of a cloud environment, a vulnerable software component in the cloud environment, determining, by the at least one computing device, an operational status for the vulnerable software component in the cloud environment, and generating, by the at least one computing device and based on the operational status for the vulnerable software component, an alert for the vulnerable software component.

Abstract: Examples may forward an input/output (IO) request with use of kernel-level instructions. Examples may receive the IO request via a port of a standby controller, generate an alternate version of the IO request using at least kernel-level instructions of the standby controller, and provide the alternate version of the IO request to physical memory of the active controller by providing the alternate version of the IO request to a designated region of physical memory of the standby controller that is mapped to a designated region of the physical memory of the active controller.

Abstract: In some examples, a system computes respective values for corresponding data value indicators added to and removed from a deduplication data store in which duplicated data values have been eliminated, where each respective data value indicator of the data value indicators represents presence of a unique data value in the deduplication data store. The system updates an estimator based on the respective values, to reflect an addition of a first data value indicator to the deduplication data store and a removal of a second data value indicator from the deduplication data store. The system computes, using the updated estimator, a parameter relating to data deduplication at the deduplication data store.

Abstract: A system may include a persistent storage device, a low latency cache device, a volatile memory; and a processor. The processor is to store a data structure in the volatile memory that is usable to directly translate a block logical address for targeted data to a candidate physical location on the cache device, store a multilevel translation index in the volatile memory for translating the block logical address for the targeted data to an expected physical location of the targeted data on the cache device and attempt accessing the targeted data at the candidate physical location retrieved from the direct cache address translation data structure. In response to the targeted data not being at the candidate physical address, access the targeted data at the expected physical location retrieved from the multilevel translation index.

Abstract: Examples may forward an input/output (IO) request with use of kernel-level instructions. Examples may receive the IO request via a port of a standby controller, generate an alternate version of the IO request using at least kernel-level instructions of the standby controller, and provide the alternate version of the IO request to physical memory of the active controller by providing the alternate version of the IO request to a designated region of physical memory of the standby controller that is mapped to a designated region of the physical memory of the active controller.

Abstract: Examples may forward an input/output (IO) request with use of kernel-level instructions. Examples may receive the IO request via a port of a standby controller, generate an alternate version of the IO request using at least kernel-level instructions of the standby controller, and provide the alternate version of the IO request to physical memory of the active controller by providing the alternate version of the IO request to a designated region of physical memory of the standby controller that is mapped to a designated region of the physical memory of the active controller.

Abstract: A system may include a persistent storage device, a low latency cache device, a volatile memory; and a processor. The processor is to store a data structure in the volatile memory that is usable to directly translate a block logical address for targeted data to a candidate physical location on the cache device, store a multilevel translation index in the volatile memory for translating the block logical address for the targeted data to an expected physical location of the targeted data on the cache device and attempt accessing the targeted data at the candidate physical location retrieved from the direct cache address translation data structure. In response to the targeted data not being at the candidate physical address, access the targeted data at the expected physical location retrieved from the multilevel translation index.

Abstract: Examples may forward an input/output (IO) request with use of kernel-level instructions. Examples may receive the IO request via a port of a standby controller, generate an alternate version of the IO request using at least kernel-level instructions of the standby controller, and provide the alternate version of the IO request to physical memory of the active controller by providing the alternate version of the IO request to a designated region of physical memory of the standby controller that is mapped to a designated region of the physical memory of the active controller.

Abstract: A system may include a persistent storage device, a low latency cache device, a volatile memory; and a processor. The processor is to store a data structure in the volatile memory that is usable to directly translate a block logical address for targeted data to a candidate physical location on the cache device, store a multilevel translation index in the volatile memory for translating the block logical address for the targeted data to an expected physical location of the targeted data on the cache device and attempt accessing the targeted data at the candidate physical location retrieved from the direct cache address translation data structure. In response to the targeted data not being at the candidate physical address, access the targeted data at the expected physical location retrieved from the multilevel translation index.

Abstract: A hyperconverged data storage system including a storage array. A first node includes hardware and a virtualization layer supporting guest virtual machines running first applications. An active first virtual storage controller executing in the first virtualization layer is configured for handling IOs accessing the storage array. A second node includes hardware and a second virtualization layer supporting guest virtual machines running second applications. A second virtual storage controller executing in the second virtualization layer operates in a standby mode to the first virtual storage controller. An internal communication network facilitates communications between the first node and the second node. The first virtual storage controller when operating in active mode is configured for handling IOs originating from the first applications and the second applications.

Abstract: A hyperconverged data storage system including a storage array. A first node includes hardware and a virtualization layer supporting guest virtual machines running first applications. An active first virtual storage controller executing in the first virtualization layer is configured for handling IOs accessing the storage array. A second node includes hardware and a second virtualization layer supporting guest virtual machines running second applications. A second virtual storage controller executing in the second virtualization layer operates in a standby mode to the first virtual storage controller. An internal communication network facilitates communications between the first node and the second node. The first virtual storage controller when operating in active mode is configured for handling IOs originating from the first applications and the second applications.

Abstract: Storage arrays, systems and methods for operating storage arrays for maintaining consistency in configuration data between processes running on an active controller and a standby controller of the storage array are provided. One example method includes executing a primary process in user space of the active controller. The primary process is configured to process request commands from one or more initiators, and the primary process has access to a volume manager for serving data input/output (I/O) requests and non-I/O requests. The primary process has primary access to the configuration data and includes a first logical unit (LU) cache for storing the configuration data. The method also includes executing a secondary process in user space of the standby controller. The secondary process is configured to process request commands from one or more of the initiators, wherein the secondary process does not have access to the volume manger.

Abstract: Detection and reduction of unaligned input/output (“I/O”) requests is implemented by a storage server determining an alignment value for data stored by the server within a storage system on behalf of a first client, writing the alignment value to a portion of the volume that stores the data for the first client, but not to a portion of the volume that stores data for a second client, and changing a location of data within the portion of the volume that stores the data for the first client, but not a location of data in the portion of the volume that stores data for the second client, to an alignment corresponding to the alignment value. The alignment value is applied to I/O requests directed to the portion of the volume that stores the data blocks for the first client after the location of the data blocks has been changed.

Abstract: Detection and reduction of unaligned input/output (“I/O”) requests is implemented by a storage server determining an alignment value for data stored by the server within a storage system on behalf of a first client, writing the alignment value to a portion of the volume that stores the data for the first client, but not to a portion of the volume that stores data for a second client, and changing a location of data within the portion of the volume that stores the data for the first client, but not a location of data in the portion of the volume that stores data for the second client, to an alignment corresponding to the alignment value. The alignment value is applied to I/O requests directed to the portion of the volume that stores the data blocks for the first client after the location of the data blocks has been changed.

Abstract: A storage system tracks statistical behavior of client read requests directed to a storage device to form prediction about data that the client will require next. The storage system collects the size of read sequences for various streams into a data structure, which summarizes past behavior of read requests. This data structure reports the number of streams in each equivalence class of stream sizes that is tracked. The data structure is then used to determine expected size of a selected read stream. The data structure is also used to improve predictions about an expected size computed by a known technique.

Abstract: Detection and reduction of unaligned input/output (“I/O”) requests is implemented by a storage server determining an alignment value for data stored by the server within a storage system on behalf of a first client, writing the alignment value to a portion of the volume that stores the data for the first client, but not to a portion of the volume that stores data for a second client, and changing a location of data within the portion of the volume that stores the data for the first client, but not a location of data in the portion of the volume that stores data for the second client, to an alignment corresponding to the alignment value. The alignment value is applied to I/O requests directed to the portion of the volume that stores the data blocks for the first client after the location of the data blocks has been changed.

Abstract: Example embodiments provide various techniques for initiating read-ahead requests. A rate at which applications is requesting data from a data storage device is identified. Additionally, a length of time in retrieving or servicing the data from the data storage device is also identified. The identified rate and length of time in retrieving the data are used to determine when read-ahead requests should be sent to pre-fetch data.