An example embodiment involves persistent storage defining a first configuration item representing an application deployed within a network, a second configuration item representing a software program that is deployable within the network, and a relationship between the first configuration item and the second configuration item. One or more processors may be configured to: (i) receive an indication that a change has been applied to the application or has been arranged to be applied to the application; (ii) identify the relationship between the first configuration item and the second configuration item; (iii) based on the relationship between the first configuration item and the second configuration item, determine that the change can affect operation of the software program; and (iv) in response to determining that the change can affect operation of the software program, provide a notification of the change to an agent associated with the software program.

A computer-implemented method, according to one approach, includes: receiving a stream of incoming I/O requests, all of which are satisfied using one or more buffers in a primary cache. However, in response to determining that the available capacity of the one or more buffers in the primary cache is outside a predetermined range: one or more buffers in the secondary cache are allocated. These one or more buffers in the secondary cache are used to satisfy at least some of the incoming I/O requests, while the one or more buffers in the primary cache are used to satisfy a remainder of the incoming I/O requests. Moreover, in response to determining that the available capacity of the one or more buffers in the primary cache is not outside the predetermined range: the one or more buffers in the primary cache are again used to satisfy all of the incoming I/O requests.

A system, method, and computer program product for implementing dynamic virtual network access is provided. The method includes monitoring hardware devices associated with network locations comprising data and software code. In response, updates applied to the data and software code are detected and a context of the updates is determined. Based on the context, it is determined that that the updates applied to the data and software code should be cloned and associated user access is verified. A target component location associated with the updates applied to the data and software code to be cloned is determined and the updates applied to the data and software code with respect to the target component location are cloned. A notification indicating the cloning is transmitted to users.

Technologies for composing a managed node with multiple processors on multiple compute sleds to cooperatively execute a workload include a memory, one or more processors connected to the memory, and an accelerator. The accelerator further includes a coherence logic unit that is configured to receive a node configuration request to execute a workload. The node configuration request identifies the compute sled and a second compute sled to be included in a managed node. The coherence logic unit is further configured to modify a portion of local working data associated with the workload on the compute sled in the memory with the one or more processors of the compute sled, determine coherence data indicative of the modification made by the one or more processors of the compute sled to the local working data in the memory, and send the coherence data to the second compute sled of the managed node.

Server hardware failure is predicted, with a probability estimate, of a possible future server failure along with an estimated cause of the future server failure. Based on the prediction, the particular server can be evaluated and if the risk is confirmed, load balancing can be performed to move a load (e.g., virtual machines (VMs)) off of the at-risk server onto low-risk servers. High availability of deployed load (e.g., VMs) is then achieved. A flow of big data may be on the order of 1,000,000 parameters per minute. A scalable tree-based AI inference engine processes the flow. One or more leading indicators are identified (including server parameters and statistic types) which reliably predict hardware failure. This allows a telco operator to monitor cloud-based VMs and perform a hot-swap on virtual machines if needed by shifting virtual machines VMs from the at-risk server to low-risk servers. Servers having a health score indicating high risk are indicated on a visual display called a heat map. The heat map quickly provides a visual indication to the telco person of identities of at-risk servers. The heat map can also indicate commonalities between at-risk servers, such as if the at-risk servers are correlated in terms of protocols in use, if the at-risk servers are correlated in terms of geographic location, server manufacturer, server OS load, or the particular hardware failure mechanism predicted for the at-risk servers.

An apparatus includes a media management superblock component. The media management superblock component determines that a quantity of blocks of a superblock of a non-volatile memory array are bad blocks. The media management superblock component compares the quantity of bad blocks to a bad block criteria. The media management superblock component writes host data to the superblock with the quantity of bad blocks in response to the quantity of bad blocks meeting the bad block criteria.

Techniques are disclosed for efficient utilization worker threads in a workflow-as-a-service (WFaaS) environment. A client device may request a workflow for execution by the client device. The client device may receive the requested workflow and initialize a set of worker threads to execute the workflow and a set of heartbeater threads to monitor the set of worker threads. Upon receiving an indication of a processing delay, the client device may capture the state of the workflow, suspend execution of the workflow, and store the workflow in a temporary queue. While the processing delay persists, the client device may use the set of worker threads to execute other tasks. When the processing delay terminates, the client device may resume execution of the workflow.

An apparatus comprises a processing device configured to collect system state information from host devices, to split the collected system state information into logical chunks, and to determine, based at least in part on a plurality of factors, a compression level to be applied to each of the logical chunks. The plurality of factors comprise a first factor characterizing a time at which the collected system state information is needed at a destination device and at least a second factor characterizing resources available for at least one of performing compression of the collected system state information and transmitting the collected system state information over at least one network to the destination device. The processing device is further configured to apply the determined compression level to each of the logical chunks to generate compressed logical chunks, and to transmit the compressed logical chunks to the destination device.

Examples provided herein describe a method for reducing recovery time for an application. For example, a first physical processor of a computing device may monitor, based on a first application instance of the application running in a first mode, for failure detection of the first application instance running on a first computing device. The first physical processor may determine that the first application instance is to be changed from the first mode to a second mode. Based on the determination, the first physical processor may validate that a second application instance can run in the first mode by performing a data integrity compliance check. Responsive to validating that the second application instance can run in the first mode, the first physical processor may facilitate running of the second application instance in the first mode.

Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls. Such tests may be used to train the machine learning model.