Example implementations relate to determining and implementing a feasible resource optimization plan for public cloud consumption. Telemetry data over a period of time is obtained for a current deployment of virtual infrastructure resources within a current data center of a cloud provider that supports an existing service and an application deployed on the virtual infrastructure resources. Information regarding a set of constraints to be imposed on a resource optimization plan is obtained. Indicators of resource consumption relating to the currently deployed virtual infrastructure resources during the period of time are identified by applying a deep learning algorithm to the telemetry data. A resource optimization plan is determined that is feasible within the set of constraints based on a costing model associated with resources of an alternative data center of the cloud provider, the indicators of resource consumption and costs associated with the current deployment.

Presented are concepts for processing an event log. Once such concept obtains an event log comprising a log of event occurrences for an executed process. It also obtains an events embedding model representative of relationships between a plurality of events of one or more processes. Based on the events embedding model, repeating events in the event log are clustered into one or more groups, and each of the one or more groups are associated with a respective identifier. Repeating events in the event log are then replaced with the identifier associated with the group that the repeating event is a member of.

Systems, methods and/or computer program products dynamically managing log levels of microservices in a service mesh based on predicted error rates of calls made to the service mesh. A first AI module predicts health, status and/or failures of microservices individually or as part of microservice chains with a particular confidence level. Using health status mapped to the microservices and historical information inputted into a knowledge base (including error rates), the first AI module predicts error rates of the API call for each user profile or generally by the service mesh. A second AI module analyzes the predictions provided by the first AI module and determines whether the predictions meet threshold levels of confidence. To improve the confidence of predictions that are below threshold levels, the second AI module dynamically adjusts application logs of the microservices and/or proxies thereof to an appropriate level to capture more detailed information within the logs.

An overall performance metric of a computer system may be determined for each bin of the set of analysis bins. In case one or more bins of the set of analysis bins do not have at least a predefined minimum number of records, a new set of analysis bins may be redefined by joining analysis bins of the set of analysis bins. For each bin of the redefined set of bins a machine learning (ML) performance metric of the ML model may be computed. The ML performance metric may be estimated for the set of analysis bins using the ML performance metrics of the redefined bins. The computer system may be configured based on a correlation over the set of analysis bins between the computed overall performance metric and the ML performance metric.

A method and a system to perform the method are disclosed, the method includes receiving, by a virtualization server communicatively coupled with a client device, a request to provide a virtual machine (VM) to a client device, accessing a profile associated with the client device, instantiating a VM on the virtualization server, wherein the VM is a linked clone VM of a base VM, wherein the linked clone VM has (1) a read-only access to a shared range of a persistent memory associated with the base VM, wherein the shared range of the persistent memory is determined in view of the profile associated with the client device and stores at least one application installed on the virtualization server, (2) a write access to a private range of the persistent memory, wherein the private range is associated with the VM, and providing the VM to the client device.

Methods, systems, and computer-readable media (transitory or non-transitory) are described herein for automatic backup and replacement of a storage device. According to an example, a storage failure for given storage device may be predicted. A backup process of the give storage device to a remote system may be initiated based on predicting the storage failure for the given storage device. The backup process may create a one-to-one image backup or a user data backup based on a predicted amount of time until the storage failure of the given storage device. A restore process of a new storage device at the remote system may be initiated upon completion of the backup process. The restore process may depend on the backup created during the backup process and/or various types of new storage devices that are available. The new storage device may be based on the given storage device.

An electronic device includes at least one processor configured to obtain user data associated with a plurality of devices from multiple data sources. The at least one processor is also configured to determine a static weight for each of the plurality of devices based on at least one source of the multiple data sources. The at least one processor is further configured to identify a portion of the plurality of devices that represents the plurality of devices based on the static weight and a dynamic weight. In addition, the at least one processor is configured to determine the dynamic weight for each of the portion of the plurality of devices while the portion of the plurality of devices is identified, where the dynamic weight is based on one or more sources of the multiple data sources.

A computer-implemented method for detecting bottlenecks in microservice cloud systems is provided including identifying a plurality of nodes within one or more clusters associated with a plurality of containers, collecting thread profiles and network connectivity data by periodically dumping stacks of threads and identifying network connectivity status of one or more containers of the plurality of containers, classifying the stacks of threads based on a plurality of thread states, constructing a microservice dependency graph from the network connectivity data, aligning the plurality of nodes to bar graphs to depict an average number of working threads in a corresponding microservice, and generating, on a display, an illustration outlining the plurality of thread states, each of the plurality of thread states having a different representation.

A system and method for scaling resources of a secondary network for disaster recovery uses a disaster recovery notification from a primary resource manager of a primary network to a secondary resource manager of the secondary network to generate a scale-up recommendation for additional resources to the secondary network. The additional resources are based on latest resource demands of workloads on the primary network included in the disaster recovery notification. A scale-up operation for the additional resources is then executed based on the scale-up recommendation from the secondary resource manager to operate the secondary network with the additional resources to run the workloads on the secondary network.

Application lifecycle management based on real-time resource usage. A first plurality of resource values that quantify real-time computing resources used by a first instance of an application is determined at a first point in time. Based on the first plurality of resource values, one or more utilization values are stored in a profile that corresponds to the application. Subsequent to storing the one or more utilization values in the profile, it is determined that a second instance of the application is to be initiated. The profile is accessed, and the second instance of the application is caused to be initiated on a first computing device utilizing the one or more utilization values identified in the profile.