Red Hat, Inc.

International Business Machines Corporation

Gabriel Zvi BenHanokh

Orit Luba Wasserman

Yehoshua Salomon

Joshua Durgin

Uri Lublin

Brett D Niver

Mark David Nelson

Yuval Lifshitz

Joshua Nikolai Durgin

Or Friedmann

Brett Niver

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 data structure usage for storing data objects is described. In one example of the present disclosure, a system can receive a data object and properties associated with the data object. The system can determine, based on at least one of the properties and pre-defined rules for data objects and corresponding object types, an object type of the data object and a first data structure for storing the data object that is different from a second data structure currently storing data objects in the memory. The system can output a command for causing the first data structure to store the data object in the memory.

Using dynamic data structures for storing data objects

Computer-implemented methods for storing data with replicated metadata in a scale-out data storage system are provided. Aspects include receiving, by a first data storage node of the scale-out data storage system, a data item to be stored and storing, by the first data storage node, the data item on a storage area network in communication with the scale-out data storage system. Aspects also include generating, by the first data storage node, a metadata item corresponding to the data item and replicating the metadata item on a second data storage node of the scale-out data storage system.

Storing data with replicated metadata in a scale-out data storage system

An example method may include identifying one or more requested data items requested by a client system, identifying, in view of one or more requested data items, a plurality of predicted data items and, for each predicted data item, a respective probability that the predicted data item will be requested by a subsequent access request, identifying a plurality of cacheable data items, where the cacheable data items comprise one or more of the predicted data items, wherein each cacheable data item comprises a predicted data item that satisfies caching criteria, wherein the caching criteria are evaluated in view of the respective probability that the predicted data item will be requested by a subsequent access request, and storing each of the plurality of cacheable data items in a cache memory of a respective storage server at which the cacheable data item is located.

Prefetching cached data for predicted accesses

Computer-implemented methods for optimizing memory access for systems with memory expanders are provided. Aspects include analyzing a source code of a program to identify each data structure that will be accessed by the program during execution of the program, determining a predicted access pattern for each data structure, and determining a type of each data structure. Aspects also include calculating a data access score for each data structure based at least in part on the predicted access pattern and the type and creating an executable code of the program based on the source code. The executable code includes a memory allocation value for each data structure that is determined based on the data access score for each data structure.

Optimizing memory access for system with memory expander

A technique for improving workload distribution by utilizing unused resources in a distributed computing system is described. In one example of the present disclosure, a system can determine that a computing entity of a distributed computing system includes an unused portion of a CPU capacity. The computing entity can have a first defined limit of the CPU capacity. The system can use the unused portion of the CPU capacity to improve a usage of a resource of the computing entity. The computing entity can have a second defined limit of the resource.

Workload distribution by utilizing unused central processing unit capacity in 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

Storage data and metadata in separate storage layers of a distributed storage system can be replicated via separate channels. For example, a first gateway at a first storage layer in a first data center can identify a data object for replication to a second data center. The first gateway can access metadata to determine that the data object corresponds to a set of storage data on a first storage node in the first data center. The first gateway can then transmit the metadata to a second gateway in the second data center via a first network. The first gateway can also transmit a command to the first storage node for causing the first storage node to transmit the set of storage data to a second storage node in the second data center via a second network.

Replicating storage data and metadata in separate storage layers of a distributed storage system

Embodiments of the present disclosure relate to systems and methods for enabling a compute node to utilize all the resources of the cluster it is part of when accessing block storage resources. A deployment manager may receive configuration information for each of a set of compute nodes in a cluster, wherein the configuration information of each compute node in the cluster indicates a usage by the compute node of each of a set of block storage volumes (also referred to herein as storage volumes). The deployment manager determines that a first compute node cannot utilize a full amount of the storage volume bandwidth provided by one or more storage volumes assigned to it. A first storage volume of the one or more storage volumes may be reassigned to a second compute node in the cluster and deploys the cluster with the first storage volume mounted on the second compute node.

Sharing node storage resources with the entire cluster

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.

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Tel Aviv, Israel

Gabriel Zvi BenHanokh