A data processing system implements obtaining a set of input parameters associated with an update to be deployed to a plurality of server farms of a cloud-based service, wherein each server farm includes a primary replica configured to handle user traffic and a disaster recovery replica configured to handle user traffic responsive to a failure of the primary replica; determining temperature information for each of the server farms, ranking the server farms based on the temperature information to determine an order in which an update is to be deployed to the server farms; iteratively deploying the updates to the primary replicas of the server farms according to the ranking until an deployment threshold has been satisfied; and iteratively deploying the updates to the primary replicas of server farms for which the primary replicas have not yet been updated and to the disaster recovery replicas of the server farms.

Methods and systems for managing the performance of workloads in a distributed system are disclosed. The distributed system may include any number of clients and deployments where workloads may be performed. The deployments may include different hardware resources, may have different levels of performance, and/or may have other different characteristics that may impact performance of a given workload using any of the deployments. To service the workloads, container instances may be deployed to various deployments. When deciding where to deploy the container instances, the hardware resources of the deployments and/or resource expectations associated with the container instances may be taken into account. By doing so, container instances may be more likely to be deployed to deployments that meet their resource expectations.

Methods and systems for managing the performance of workloads in a distributed system are disclosed. The distributed system may include any number of clients, deployments, and data sources operably to one another. To service the workloads, container instances may be deployed to various deployments. When deciding where to deploy the container instances, the hardware resources of the deployments and/or resource expectations associated with the container instances may be taken into account. By doing so, container instances may be more likely to be deployed to deployments that meet their resource expectations. The resource expectations may be embedded as metadata in resources specific build files.

A protection system is provided for delivering runtime security to a task including a workload container. The protection system uses a sidecar to limit access of the workload container to a standard library of the operating system running the workload container by modifying the task so that the sidecar is executed before the workload container. The sidecar places a guard loader into a shared volume and binds the workload container, such that calls to the workload container are passed to an agent binary. The agent binary compares requested calls from the workload container to a policy to approve and/or deny the requested calls. If the requested call is approved, then the requested call is passed to the standard library.

A data processing system includes a phantom queue for each of a plurality of output ports each associated with an output link for outputting data. The phantom queues receive/monitor traffic on the respective ports and/or the associated links such that the congestion or traffic volume on the output ports/links is able to be determined by a congestion mapper coupled with the phantom queues. Based on the determined congestion level on each of the ports/links, the congestion mapper selects one or more non or less congested ports/links as destination of one or more packets. A link selection logic element then processes the packets according to the selected path or multi-path thereby reducing congestion on the system.

A computer implemented method of executing a plurality of discrete software modules each including a machine learning algorithm as an executable software component configurable to approximate a function relating a domain data set to a range data set; a data store; and a message handler as an executable software component arranged to receive input data and communicate output data for the module, wherein the message handler is adapted to determine domain parameters for the algorithm based on the input data and to generate the output data based on a result generated by the algorithm, each module having associated a metric of resource utilization by the module, the method including receiving a request for a machine learning task; and selecting a module from the plurality of modules for the task based on the metric associated with the module.

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.

Techniques are provided for managing resources among clusters of computing devices in a computing system. Resource reassignment message are generated for indicating that servers are reassigned and in response to resource compute loads exceed or fall below certain thresholds. Techniques also include establishing communications with the reassigned servers to assign compute loads without physically relocating the servers from one cluster to another cluster.

Techniques discussed herein relate to managing service provider resources. The techniques may include receiving a first request to organize a first workload and a second workload into a space. The first workload may be associated with a first computing resource of a first service provider and the second workload may be associated with a second computing resource of a second service provider. The techniques may import data associated with the first workload and the second workload into the space. The techniques may cause an action to be performed for the first workload and the second workload by implementing a first workflow for the first workload and implementing a second workflow for the second workload.

Example implementations relate to a role-based autoscaling approach for scaling of nodes of a stateful application in a large scale virtual data processing (LSVDP) environment. Information is received regarding a role performed by the nodes of a virtual cluster of an LSVDP environment on which a stateful application is or will be deployed. Role-based autoscaling policies are maintained defining conditions under which the roles are to be scaled. A policy for a first role upon which a second role is dependent specifies a condition for scaling out the first role by a first step and a second step by which the second role is to be scaled out in tandem. When load information for the first role meets the condition, nodes in the virtual cluster that perform the first role are increased by the first step and nodes that perform the second role are increased by the second step.