This document describes a framework for measuring and improving the performance of applications, such as distributed applications and web applications. In one aspect, a method includes performing a test on an application. The test includes executing the application on one or more computers and, while executing the application, simulating a set of workload scenarios for which performance of the application is measured during the test. While performing the test, a set of performance metrics that indicate performance of individual components involved in executing the application during the test is obtained. A knowledge graph is queried using the set of performance metrics. The knowledge graph links the individual components to corresponding performance metrics and defines a set of hotspot conditions that are each based on one or more of the corresponding performance metrics for the individual components. A given hotspot condition is detected based on the set of performance metrics.

It is presented a method for enabling allocation of resources for a plurality of hosts. The method is performed by a server (1) and comprises identifying (S100) a service running on one or more of the plurality of hosts, determining (S140) a stretch factor for a recurring load pattern for the service running on the one or more of the plurality of hosts, and storing (S150) the identified service together with the determined stretch factor. It is also presented a server, a computer program and a computer program product.

A method for managing specialized hardware resources includes obtaining, by a disaster recovery (DR) virtual resource agent, a request for a DR environment for a set of virtual resources in a primary site, in response to the request: monitoring the primary site to obtain virtual workload information corresponding to the set of virtual resources, performing a workload analysis on the set of virtual resources in the primary site using the virtual workload information to obtain a virtual resource mapping of each virtual resource in the primary site to a tiered component in the DR environment, and initiating a DR environment allocation of DR virtual resources based on the virtual resource mapping.

A resource processing method includes: determining a current application scenario and usage data of the mobile terminal; inputting the usage data into a machine learning algorithm model corresponding to the current application scenario to obtain predicted recommendation parameters; and configuring resources of the mobile terminal based on the recommendation parameters.

Examples described relate to determining a future operation failure in a cloud system. In an example, a historical utilization of resources for performing an operation in a cloud system may be determined. A current utilization of resources in the cloud system may be determined. Based on the historical utilization of resources for performing the operation in the cloud system and the current utilization of resources in the cloud system, a determination may be made whether a future performance of the operation in the cloud system is likely to be a failure. In response to a determination that the future performance of the operation in the cloud system is likely to be a failure, an alert may be generated.

Various aspects are disclosed for distributed application management using an embedded persistent queue framework. In some aspects, task execution data is monitored from a plurality of task execution engines. A task request is identified. The task request can include a task and a Boolean predicate for task assignment. The task is assigned to a task execution engine embedded in a distributed application process if the Boolean predicate is true, and a capacity of the task execution engine is sufficient to execute the task. The task is enqueued in a persistent queue. The task is retrieved from the persistent queue and executed.

Workload profiling can be used in a distributed computing environment for automatic performance tuning. For example, a computing device can receive a performance profile for a workload in a distributed computing environment. The performance profile can indicate resource usage by the workload in the distributed computing environment. The computing device can determine a performance bottleneck associated with the workload based on the resource usage specified in the performance profile. A tuning profile can be selected to reduce the performance bottleneck associate with the workload. The computing device can output a command to adjust one or more properties of the workload in accordance with the tuning profile to reduce the performance bottleneck associated with the workload.

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.

An example system for geographic deployment of applications to edge computing nodes includes: a memory storing an application; a receive engine to receive, from edge computing nodes, indications of requests for the application as received at the edge computing nodes from edge clients, the indications being indicative of geographic demand for the application; a demand engine to determine a geographic area where demand for the application exceeds a threshold demand; and an application deployment engine to deploy the application to the edge computing nodes within the geographic area where the demand for the application exceeds the threshold demand.

Application link scaling method, apparatus and system are provided. The method includes obtaining an application link, the application link being a path formed by at least two associated applications for a service scenario; determining information of target resources required by capacity scaling for all applications in the application link; allocating respective resources to the applications according to the information of the target resources; and generating instances for the applications to according the respective resources. From the perspective of services, the method performs capacity assessment for related applications on a link as a whole, and capacity scaling of the entire link, thus fully utilizing resources, and preventing the applications from being called by other applications which results in insufficient resources. This ensures the applications not to become the vulnerability of a system, ensures the stability of the system, avoids allocating excessive resources to the applications, and reduces a waste of resources.