Red Hat, Inc.

International Business Machines Corporation

Red Hat Israel, Ltd.

Orit Luba Wasserman

Gabriel Zvi BenHanokh

Yehoshua Salomon

Ben-Ami Yassour

Shmuel Ben-Yehuda

Marcel Apfelbaum

Anthony Nicholas Liguori

Michael Tsirkin

Leah Shalev

Dor Laor

Abel Alkon Gordon

Shmuel Ben Yehuda

An example method may include receiving, in a virtualized execution environment, a data access request from a storage system, identifying, in view of a virtualized execution image associated with the virtualized execution environment, an application running in the virtualized execution environment, generating a cache classification that specifies whether data accessed by the application is suitable for cache compression, including, in the data access request, a tag indicating whether cached data is to be accessed in a compressed-memory cache, wherein the tag is determined in view of the cache classification, and sending, to a server of the storage system, the data access request. The application can be identified in view of metadata included in the virtualized execution image, where the metadata comprises one or more of an application name or an application version.

Workload-based cache compression in a distributed storage system

A technique for dynamic scaling of a distributed computing system is described. In one example of the present disclosure, a system can include a base node configured to provide an access point to a distributed computing system and for servicing a first portion of requests and to generate at least one compute node based on a first load of the base node. The system can also include the at least one compute node of the distributed computing system for servicing a second portion of requests. The at least one compute node can be configured to generate an additional compute node for servicing a subset of the second portion of requests based on a second load of the at least one compute node.

Dynamic scaling of a distributed computing system

Resource allocation can be managed in a software-defined system. For example, a computing device can receive, for a container in a software-defined system, a container limit specifying a maximum value for the container. The computing device can receive, for the container, one or more benefit functions that assign a weight for the resource in the software-defined system. The computing device can determine a value for a resource is less than the container limit. In response to determining the value for the resource is less than the container limit, the computing device can allocate the resource to the container based on the weight from the one or more benefit functions.

Managing resource allocation in a software-defined system

Systems and methods are provided for managing prefetching operations for read requests for drives in a distributed storage system. For example, a system can determine that a first drive of a plurality of drives is powered on. Prior to receiving a read request for reading a first set of data from the first drive, the system can enable a prefetching operation for prefetching the first set of data from the first drive to be written to a cache. The system may power off the first drive. The system may receive a read request for reading the first set of data from the first drive of a plurality of drives. In response to receiving the read request, the system may read the first set of data from the cache.

Managing prefetching operations for drives in distributed storage systems

A container image can be used to determine a caching algorithm for a software application. For example, a storage system can receive a context tag indicating an input/output (IO) pattern associated with a software application of a container. The context tag can be determined based on a container image of the container. The storage system can determine a caching algorithm for the software application based on the context tag. The storage system can apply the caching algorithm to the software application.

Using a container image to determine a caching algorithm for a software application

Write removal for solid-state drives can be managed. For example, a computing device can designate a space of a solid-state drive (SSD) for a write of container. The computing device can store the write for the container in the space in response to loading the container for executing the container. The computing device can determine an end to an execution phase for the container. In response to determining the end to the execution phase, the computing device can remove the write from the space of the SSD.

Managing write removal for solid-state drives

Deduplication operations can be managed based on a likelihood of duplicability. For example, a computing device can generate, by a container of a storage system, an indication of duplicability corresponding to a likelihood of duplicability for the data unit in the storage system. The computing device can transmit the indication of duplicability to a storage node of the storage system for performing an operation based on the indication of duplicability.

Managing deduplication operations based on a likelihood of duplicability

Containers can be managed for cryptanalysis attack protection. For example, a computing system can receive, from a container, a description specifying a first hardware requirement for the container. The computing system can restrict access to hardware based on the first hardware requirement for the container. The computing system can perform, for a data object requested by the container, an encryption operation and a decryption operation using the hardware. A result of the encryption operation can be inaccessible to the container prior to the decryption operation.

Container management for cryptanalysis attack protection

Systems, methods, and apparatus are disclosed for management of data storage with stream based compressibility. In an example, an orchestrator including a storage controller managing one or more storage nodes executes on one or more processors. The orchestrator receives compression ratio measurements associated with attempts to compress data from a plurality of applications running on a plurality of service guests. The orchestrator receives a request for storing a first data stream from a first application running in a first service guest. Based on a compression ratio of previously stored data associated with the first application being less than a threshold, the orchestrator assigns a first compressibility setting to the first data stream. The first compressibility setting causes a storage node to store the first data stream without attempting to compress the first data stream.

Stream based compressibility with auto-feedback

Container data can be prefetched in data storage systems. For example, a computing device can determine a first container to be activated subsequent to an execution of a second container that is in an execution phase. The computing device can access a description specifying a data object for activating the first container. The computing device can receive the data object for the first container from a storage device using the description. The computing device can store the data object in a memory device prior to an end of the execution of the second container for subsequent use in an activation of the first container.

Prefetching container data in a data storage system

A container image is received, the container image including a container manifest referencing a first base operating system layer and an application layer. An execution compatibility is determined between the application layer and a second base operating system layer, different from the first operating system layer. An updated container image, an updated container manifest, or the updated container image and the updated container manifest is generated in which the first base operating system layer is replaced with the second base operating system layer.

Automated container size reduction

Fragmented data on a storage device may be additionally stored in a cache. A cache weight for determining storage of a data unit can be determined. For example, a computing device can receive storage device characteristics from a storage device. A data unit comprising multiple fragments may be stored on the storage device. The computing device can receive data unit characteristics from the storage device. The computing device can determine a cache weight for the data unit. The computing device may output the cache weight for determining storage of the data unit in a cache.

Determining weights for cache storage of fragmented data

A system includes a memory, a processor in communication with the memory, and an operating system ('OS') executing on the processor. The processor belongs to a processor socket. The OS is configured to pin a workload of a plurality of workloads to the processor belonging to the processor socket. Each respective processor belonging to the processor socket shares a common last-level cache ('LLC'). The OS is also configured to measure an LLC occupancy for the workload, reserve the LLC occupancy for the workload thereby isolating the workload from other respective workloads of the plurality of workloads sharing the processor socket, and maintain isolation by monitoring the LLC occupancy for the workload.

Dynamic last level cache allocation for cloud real-time workloads

Systems and methods are provided for managing write requests for drives in a cloud storage system. For example, a system can receive a plurality of write requests for writing a first set of data to a first drive of a plurality of drives. The first drive may be powered off. The system can write the first set of data to a cache in response to receiving the plurality of write requests. The system can determine that a number of the plurality of write requests exceeds a predetermined write request threshold. The system can power on the first drive in response to determining that the number of the plurality of write requests exceeds the predetermined write request threshold. The system can write the first set of data stored in the cache to the first drive.

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Haifa, Israel

Orit Luba Wasserman