The disclosure is directed to providing content access control in information centric networking (ICN) networks. Methods and systems include hardware and/or software that perform operations for sending to a content provider of an ICN network an access request for content in response to receiving a first content request from a client. The operations also include receiving from the content provider access control information for the content. The operations further include sending to the client a challenge. Additionally, the operations include receiving from the client an authorization of the content provider that includes information obtained by the client from the content provider based on the challenge. Furthermore, the operations include verifying the authorization received from the client using the access control information received from the content provider. Moreover, the operations include sending to the client the content.

A system is provided for automated cross-network monitoring of computing hardware and software status. In particular, the system may track the status of various computing resources using process automation-based operations to simulate calls made by users to the various resources that the users are authorized to access. Based on said operations, the system may assess whether the authorized pathways to the resources and/or their respective components are properly functioning by capturing information regarding the resource, its associated components, and the current status of the resource. The results of these operations may be aggregated to provide an overview of which resources and/or systems are functioning and which are not. In this way, the system may provide a detailed view of the statuses of the individual resources and components within an entity's complex computing network.

Disclosed are various implementations of cluster capacity management for infrastructure updates. In some examples, cluster hosts for a cluster can be scheduled for an update. A component of a datacenter level resource scheduler can analyze cluster specific resource usage data to identify a cluster scaling decision for the cluster. The datacenter level resource scheduler transmits an indication that the resource scheduler is successfully invoked. Cluster hosts can then be updated.

An example method for storing data includes providing a plurality of physical storage pools, each storage pool including a plurality of storage nodes coupled to a network. The method also includes mapping a partition of a plurality of partitions to a set of physical storage pools, where each physical storage pool of the set of physical storage pools is located in a different availability zone, and the storage nodes within an availability zone are subject to a correlated loss of access to stored data. The method further includes receiving a data management request over the network, the data management request being associated with a data object. The method also includes identifying a first partition of the plurality of partitions corresponding to the received data management request and manipulating the data object in the physical storage pools mapped to the first partition in accordance with the data management request.

A system for applying metadata tags based on metadata tag groups is described. The system includes a plurality of compute nodes configured to provide a plurality of computing resources for clients of a provider network and a resource tag manager for the provider network. The resource tag manager is configured to receive, from a first client of an account of the provider network, a creation request to establish a resource attribute group comprising a plurality of resource keys and a plurality of resource values, wherein respective metadata keys correspond to respective resource values. The resource tag manager may receive, from a second client of the account, an application request to apply the resource attribute group to a computing resource and apply the plurality of resource keys and the plurality of resource values to metadata maintained for the computing resource.

A method of performing resource discovery includes receiving, by a processing device, a resource identifier describing a resource of a computing cluster to be accessed by a workflow application. The method also includes sending the resource identifier to a container orchestration API to determine whether an endpoint exists for the resource. The method also includes, in response to an indication from the container orchestration API that the endpoint exists, receiving, from the container orchestration API, endpoint details used to access the resource, identifying an endpoint address based on the endpoint details, and binding the endpoint address to the resource in a runtime version of the workflow application to enable the workflow application to access the resource.

There are disclosed a method of and a system for selecting a network resource as a source of a content item, the content item to be analyzed by a recommendation system as part of a plurality of content items to generate a set of recommended content items as a recommendation for a given user of the recommendation system. The method comprises, for a network resource, receiving, by the server, a plurality of features associated with a network resource to be processed; generating given network resource profile for the network resource, the given network resource profile being based on the plurality of features; executing a machine learning algorithm in order to determine a source suitability parameter for the network resource, selecting at least one content item from the network resource if the source suitability parameter is determined to be above a pre-determined threshold.

A system includes at least one server that is configured to provide a multi-client network service to a plurality of existing users. When the server receives requests to join the multi-client network service from new users, the server may issue timestamps to each new user, obtain load metric based on the requests or timestamps, and collect the load metric to obtain historical data characterizing a demand in the multi-client network service over time. Further, based on the historical data, the server can predict a future load demand in the multi-client network service and selectively enable to join the multi-client network service by at least one of the plurality of new users based on the future load demand.

A method includes intercepting a first data packet being transmitted from a domain name system (DNS) server to a first client device, the first data packet being a DNS response, extracting a first internet protocol (IP) address and a first hostname from the first data packet, and storing the first IP address and the first hostname in a first entry of an identification table.

A method includes intercepting a first data packet being transmitted from a domain name system (DNS) server to a first client device, the first data packet being a DNS response, extracting a first internet protocol (IP) address and a first hostname from the first data packet, and storing the first IP address and the first hostname in a first entry of an identification table.