Techniques are provided for enforcing policies at a sub-logical unit number (LUN) granularity, such as at a virtual disk or virtual machine granularity. A block range of a virtual disk of a virtual machine stored within a LUN is identified. A quality of service policy object is assigned to the block range to create a quality of service workload object. A target block range targeted by an operation is identified. A quality of service policy of the quality of service policy object is enforced upon the operation using the quality of service workload object based upon the target block range being within the block range of the virtual disk.

Provided is an anomaly pattern detection system including an anomaly detection device connected to one or more servers. The anomaly detection device may include an anomaly detector configured to model input data by considering all of the input data as normal patterns, and detect an anomaly pattern from the input data based on the modeling result.

The disclosure herein describes managing multiple clusters within a container environment using a control cluster. The control cluster includes a single deployment model that manages deployment of cluster components to a plurality of clusters at the cluster level. Changes or updates made to one cluster are automatically propagated to other clusters in the same environment, reducing system update time across clusters. The control cluster aggregates and/or stores monitoring data for the plurality of clusters creating a centralized data store for metrics data, log data and other systems data. The monitoring data and/or alerts are displayed on a unified dashboard via a user interface. The unified dashboard creates a single representation of clusters and monitor data in a single location providing system health data and unified alerts notifying a user as to issues detected across multiple clusters.

Technologies for providing shared memory for accelerator sleds includes an accelerator sled to receive, with a memory controller, a memory access request from an accelerator device to access a region of memory. The request is to identify the region of memory with a logical address. Additionally, the accelerator sled is to determine from a map of logical addresses and associated physical address, the physical address associated with the region of memory. In addition, the accelerator sled is to route the memory access request to a memory device associated with the determined physical address.

Techniques of automated interoperation tracking in computing systems are disclosed herein. One example technique includes tokenizing a first event log from a first software component and a second event log from the second software component by calculating frequencies of appearance corresponding to strings in the first and second event logs and selecting, as tokens, a first subset of the strings in the first event log and a second subset of the strings in the second event log individually having calculated frequencies of appearance above a preset frequency threshold. The example technique can also include generating an overall event log for a task executed by both the first and second software components by matching one of the strings in the first subset to another of the strings in the second subset.

A computer-implemented method, according to one embodiment, includes: receiving, at a computer, a request to facilitate a testing environment, where the request specifies a number and type of resources to be included in the testing environment. A database which lists available resources in systems and/or devices that are in communication with the computer is inspected and the available resources are compared to the number and type of resources specified in the request to be included in the testing environment. In response to determining that a valid combination of the available resources meets the number and type of resources specified in the request to be included in the testing environment, the database is updated to indicate that each of the resources in the valid combination are in use. Moreover, the request is satisfied by returning information about the resources in the valid combination.

A system may include a historical time series data store that contains electronic records associated with Software-as-a-Service (“SaaS”) applications in a multi-tenant cloud computing environment (including time series data representing execution of the SaaS applications). A monitoring platform may retrieve time series data for the monitored SaaS application from the historical time series data store and create tenant vector representations associated with the retrieved time series data. The monitoring platform may then provide the retrieved time series data and tenant vector representations together as final input vectors to an autoencoder to produce an output including at least one of a tenant-specific loss reconstruction and tenant-specific thresholds for the monitored SaaS application. The monitoring platform may utilize the output of the autoencoder to automatically detect an anomaly associated with the monitored SaaS application.

A method and a device for analyzing a consumption of resources in a computing infrastructure to predict a resource consumption anomaly on a computing device. The method includes determining a plurality of resource consumption modeling functions; determining a correlation between the resource consumption modeling functions; measuring a resource consumption by a measurement of a consumption value of a first resource; and predicting the resource consumption of the computing infrastructure. The predicting includes a calculation of a value of future consumption of a resource to be predicted from the consumption value of the first resource and from a previously calculated correlation between modeling functions.

A method, system and computer program product, the method comprising: obtaining computer code of an employed system comprising a plurality of components; obtaining data related to operating the plurality of components; based on the computer code and the data, identifying: a first component from the plurality of components, to be maintained; and a second component from the plurality of components, to be at least partly replaced by a machine learning component; and providing to a user an identification of the first component and the second component.

A method of operating a telemetry system includes automatically populating a base field of a schema in an event definition using a logging library of the telemetry system for an event of an instrumented application, and automatically populating a conditional field of the schema in the event definition using the logging library in response to a selected condition for the event.