Abstract: A method includes receiving data uploaded to a storage system from a client device and in response to receiving the data, instantiating a serverless function for anonymizing the data uploaded to the storage system. The method further includes retrieving, by the serverless function, an anonymization model to anonymize the data uploaded to the storage system and applying the anonymization model to the data uploaded to the storage system to generate anonymized data.

Abstract: An example method of providing a configuration for a multitier microservice architecture includes receiving a configuration request from a user for a configuration that satisfies a set of conditions in a cloud environment. The method also includes searching a configuration data store for the configuration that matches the set of conditions. The configuration specifies a first container and a second container, the first container sends a first communication to the second container, and the second container sends a second communication responsive to the first communication to the first container. The method further includes in response to finding the configuration that matches the set of conditions: sending an allocation request to a cloud provider for allocation of the configuration in the cloud environment and providing a first identifier (ID) that identifies the first container and a second ID that identifies the second container to the user.

Abstract: Data can be automatically anonymized in a distributed storage system. For example, a system can receive a notification indicating that an object is stored in a non-persistent bucket of a distributed storage system. The system can read the object from the non-persistent bucket in response to receiving the notification. The system can generate an anonymized version of the object by performing one or more anonymization operations with respect to the object. The system can store the anonymized version of the object in a persistent bucket of the distributed storage system. The system can then transmit a command for causing the object to be removed from the non-persistent bucket.

Abstract: Image subunit based guest scheduling is disclosed. For example, a memory stores an image registry, which stores a plurality of reference entries each associated with subunits hosted on each node of a plurality of nodes. A scheduler executing on a processor manages deployment of guests to the plurality of nodes including a first node and a second node, where a first guest is associated with an image file that includes a first subunit and a second subunit. The image registry is queried for at least one node of the plurality of nodes hosting the first subunit and/or the second subunit and the first node is determined to host the first subunit. The first guest is scheduled to the first node based on the first node hosting the first subunit.

Abstract: A first set of storage accesses of a storage system are identified. The first set of storage accesses are associated with a first user. A first plurality of storage access notifications is sent to a first message queue associated with the first user based on the first set of storage accesses. A first message queue timeout value of the first message queue is set based on at least one characteristic associated with the first set of storage accesses.

Abstract: Methods and systems are provided for assigning nodes to execute functions in a serverless computing environment. In one embodiment, a method is provided that includes receiving a function for execution in a serverless computing environment and identifying a storage pool needed during execution of the function. The serverless computing environment may include nodes for executing functions and a first set of nodes may be identified that implement the storage pool. Colocation measures may be determined between the first set of nodes and a second set of nodes. Available computing resources may be determined for the second set of nodes, such as available processing cores and available memory. The second set of nodes may be ranked according to the colocation measures and the available computing resources and a first node may be selected based on the ranking. The first node may be assigned to execute the function.

Abstract: Network interface provisioning of containerized instances based on tenant policies. A network interface assignment process (NIAP) receives a first request to assign a network interface to a first containerized instance comprising at least one container. The NIAP determines that a first tenant of a plurality of different tenants is associated with the first containerized instance. The NIAP accesses a first network assignment tenant policy (NATP) that corresponds to the first tenant. Based on the first NATP, the NIAP assigns, to the first containerized instance, a first network interface via which the first containerized instance can communicate with other containerized instances associated with the first tenant.

Abstract: Systems and methods for providing a container network interface (CNI) specification that has been modified with a set of annotations, a set of operations, and a set of policies to provide network configuration reuse, network configuration replication, and database management (garbage collection) functionality are described. In response to receiving a request to initiate a function, whether the function is to be reused or replicated may be determined. In response to determining that the function is to be reused, calling a container network interface (CNI) plugin with a first operation that decouples database functions from network functions, so that the database can cache and reuse a network configuration generated for the function. In response to determining that the function is to be replicated, calling the CNI plugin with a second operation to create the network configuration and generate and store a template of the network configuration in the database.

Abstract: A system for container migration includes containers running instances of an application running on a cluster, an orchestrator with a controller, a memory, and a processor in communication with the memory. The processor executes to monitor a vitality metric of the application. The vitality metric indicates that the application is in either a live state or a dead state. Additionally, horizontal scaling for the application is disabled and the application is scaled-down until the vitality metric indicates that the application is in the dead state. Responsive to the vitality metric indicating that the application is in the dead state, the application is scaled-up until the vitality metric indicates that the application is in the live state. Also, responsive to the vitality metric indication transitioning from the dead state to the live state, the application is migrated to a different cluster while the horizontal scaling of the application is disabled.

Abstract: Compressibility instrumented dynamic volume provisioning is disclosed. For example, a plurality of storage pools includes first and second storage pools, and is managed by a storage controller that receives a request to provision a first persistent storage volume associated with a first container, where the first storage pool has a first storage configuration including a deduplication setting, a compression setting, and/or an encryption setting. The first persistent storage volume is created in the first storage pool based on a first storage mode stored in metadata associated with the first container, where the storage mode includes a deduplication mode, a compression mode, and/or an encryption mode. A second persistent storage volume is in the second storage pool with a second storage configuration different from the first storage configuration based on a second storage mode associated with a second container.

Abstract: A power consumption optimization system includes a virtual machine (VM) provisioned on a host, a memory, a server, and a processor in communication with the memory. The processor causes the server to store a power consumption profile of the VM. The VM runs at a processor frequency state. Additionally, the processor causes the server to receive a request to lower a processor frequency for the VM from an original processor frequency state to a reduced processor frequency state. The request has request criteria indicating a time duration associated with the request. The server validates the request criteria and a requirement of another tenant on the host. Responsive to validating the request criteria and the requirement the other tenant on the host, the server confirms the request to lower the processor frequency. Additionally, the server lowers the processor frequency to the reduced processor frequency state during the time duration.

Abstract: Flexible deprovisioning of distributed storage is disclosed. For example, an orchestrator executes on a processor to measure a current storage demand factor based on a current count of service guests and a storage capacity of a plurality of storage nodes. A projected storage demand factor is calculated by (i) adjusting the current count of service guests with a timing factor resulting in a projected count, and (ii) combining the projected count with a storage class associated with the service guests. The orchestrator determines that the projected storage demand factor is lower than the current storage demand factor, and in response requests termination of a first storage node of the plurality of storage nodes based on the first storage node lacking an active communication session with the service guests. Cancel termination of the first storage node based on an association between the first storage node and a second storage node.

Abstract: Software performance can be automatically managed in a distributed computing environment. In one example, a system that can receive metrics information describing resource usage by a first instance of a service in a distributed computing environment. The system can also determine a quality-of-service (QoS) constraint for the service. The system can then modify a definition file based on the metrics information and the QoS constraint, the definition file being configured for deploying instances of the service in the distributed computing environment. The system can deploy a second instance of the service in the distributed computing environment using the modified definition file. As a result, the second instance can more closely satisfy the QoS constraint than the first instance.

Abstract: Data can be automatically anonymized in a distributed storage system. For example, a system can receive a notification indicating that an object is stored in a non-persistent bucket of a distributed storage system. The system can read the object from the non-persistent bucket in response to receiving the notification. The system can generate an anonymized version of the object by performing one or more anonymization operations with respect to the object. The system can store the anonymized version of the object in a persistent bucket of the distributed storage system. The system can then transmit a command for causing the object to be removed from the non-persistent bucket.

Abstract: Scalable storage cluster mirroring is disclosed. A compute instance comprising a processor device determines that storage segments have been modified on a first storage node of a plurality of storage nodes in a first cluster of storage nodes at a first data modification rate. In response to determining that the storage segments have been modified on the first storage node at the first data modification rate, a first mirror process that is configured to copy storage segments from an identified storage node to a mirrored cluster of storage nodes is initiated, and a storage node identifier that identifies the first storage node is communicated to the first mirror process.

Abstract: Data can be automatically anonymized in a distributed storage system. For example, a system can receive a notification indicating that an object is stored in a non-persistent bucket of a distributed storage system. The system can read the object from the non-persistent bucket in response to receiving the notification. The system can generate an anonymized version of the object by performing one or more anonymization operations with respect to the object. The system can store the anonymized version of the object in a persistent bucket of the distributed storage system. The system can then transmit a command for causing the object to be removed from the non-persistent bucket.

Abstract: Aspects of the disclosure provide for reducing service disruptions in a computer system. A method of the disclosure may include identifying a plurality of services running on a node of a computer system, determining a plurality of priorities corresponding to the plurality of services, determining a plurality of service capacity factors for the plurality of services in view of the plurality of priorities, and determining a lost impact factor in view of the plurality of service capacity factors.

Abstract: Cascading job scheduling in guests is disclosed. For example, first, second, third, and fourth nodes, each execute respective first, second, third, and fourth pluralities of guests each of which executes respective first, second, third, and fourth pluralities of jobs. A scheduler executes on a processor to receive a current capacity update of the first node. A respective quantity of jobs executing on each of the first, second, third, and fourth nodes is tracked. A first, second, third, and fourth estimated capacity of the respective first, second, third, and fourth nodes is calculated. The first, second, third, and fourth nodes are ranked in a list based on the respective estimated capacities. A request to execute a job is received. The first, second, and third nodes are selected as a schedulable set based on the list. A schedulable set notice and the job are sent to the first node to be executed.

Abstract: A method performed by a computing system, includes executing an application, using a data call to an Application Programming Interface, the data call requesting access to a file stored on a storage system associated with the computing system, with a context extraction module, determining contextual information associated with the data call, through use of a library, causing a kernel to access the file according to the data call, storing the contextual information on the storage system, and performing an analysis on the contextual information, the analysis including determining an average size of a call stack when the data was accessed.

Abstract: Systems and methods for providing scalable object storage query capabilities in a distributed storage system are disclosed. In one implementation, a processing device may receive, by an object-based distributed storage system, a request from a client to execute a query with respect to data stored at the distributed storage system. The processing device may execute the query to produce a result object and may store the result object at the distributed storage system. The processing device may further transmit the result object to the client. The processing device may re-execute the query at a subsequent point in time to update the result object and transmit the updated result object to the client.