Systems and methods are provided for optimizing network resources. The method includes setting a first resource usage threshold for wireless devices connected to an access node. The method additionally includes monitoring resource usage of the connected wireless devices and comparing the monitored resource usage to the first resource usage threshold. The method further includes dynamically restricting wireless device access to at least one area characterized by signal performance parameters in a first predetermined range when the monitored resource usage meets the first resource usage threshold.

Systems and methods are provided for optimizing network resources. The method includes setting a first resource usage threshold for wireless devices connected to an access node. The method additionally includes monitoring resource usage of the connected wireless devices and comparing the monitored resource usage to the first resource usage threshold. The method further includes dynamically restricting wireless device access to at least one area characterized by signal performance parameters in a first predetermined range when the monitored resource usage meets the first resource usage threshold.

A software designed protocol (SDP) network node includes a receiver, and a processor operatively coupled to the receiver. The receiver receives instructions, and receives packets. The processor updates a configuration of the SDP network node in accordance with the received instructions, and processes the received packets.

The present disclosure relates to an apparatus and method of altruistic scheduling based on reinforcement learning. An altruistic scheduling apparatus according to an embodiment of the present disclosure includes: an external scheduling agent for determining a basic resource share for each process based on information of a resource management system; an internal scheduling agent for determining a basic resource allocation schedule for each process based on information including the basic resource share and a resource leftover based on the basic resource allocation schedule; and a leftover scheduling agent for determining a leftover resource allocation schedule based on information including the resource leftover. According to an embodiment of the present disclosure, it may be expected that reinforcement learning will not only mitigate the diminution of fairness of an altruistic scheduler but also further improve other performance indicators such as completion time and efficiency.

A software defined networking (SDN)-based distributed denial of service (DDoS) attack prevention method, an apparatus, and a system, where a controller delivers a traffic statistics collection instruction to a first packet forwarding device. The traffic statistics collection instruction instructs the first packet forwarding device to perform traffic statistics collection, and carries a destination Internet Protocol (IP) address. The controller collects statistical data reported by the first packet forwarding device, obtains, according to the statistical data, a statistical value of global traffic flowing to the destination IP address, and delivers a DDoS prevention policy to a second packet forwarding device based on a determining result that the statistical value of the global traffic exceeds the preset threshold. Correspondingly, the second packet forwarding device receives the DDoS prevention policy from the controller, and performs, according to the DDoS prevention policy, prevention process on the traffic flowing to the destination IP address.

Methods, systems, and computer-readable media are disclosed for utilizing unused network capacity for prefetch requests. One method includes: receiving, over a network, network traffic information from a network provider of the network; determining a threshold value for prefetch request fulfillment based on the received network traffic information; receiving, over the network, a plurality of prefetch requests from an application running on a mobile device connected to the network of the network provider; determining, for each prefetch request of the plurality of prefetch requests, a score for the prefetch request based on the received plurality of prefetch requests; and responding to, for each prefetch request of the plurality of prefetch requests, the prefetch request based on the determined threshold value and the determined score for the prefetch request.

Methods, systems, and computer-readable media are disclosed for utilizing unused network capacity for prefetch requests. One method includes: receiving, over a network, network traffic information from a network provider of the network; determining a threshold value for prefetch request fulfillment based on the received network traffic information; receiving, over the network, a plurality of prefetch requests from an application running on a mobile device connected to the network of the network provider; determining, for each prefetch request of the plurality of prefetch requests, a score for the prefetch request based on the received plurality of prefetch requests; and responding to, for each prefetch request of the plurality of prefetch requests, the prefetch request based on the determined threshold value and the determined score for the prefetch request.

A method includes polling, via a service specific manager operating on a software container in a cloud infrastructure, usage of different application resources and parameters for each service of a plurality of services provided in the cloud infrastructure to yield respective polled data for each service, collating, at the service specific manager, the respective polled data for each service to yield a collation, and based on the collation, deriving a respective weight for each service which a container manager can use to create multiple instances of a new service. The method further includes communicating the respective weight for each service to the container manager and determining, via the container manager, whether to scale up or scale down container services based on the respective weight for each service.

A provider network hosting multiple network-based services that implement different resources for a client may provide automated management of resource attributes across the multiple network-based services. A client may send a request to a resource attribute service implemented at the provider network to add a resource attribute to different resources implemented among different network-based services that satisfy resource metadata selection criteria. In response to receiving the request, resource metadata maintained for the different resources implemented among the different network-based resources, which may include one or more previously applied resource attributes, may be evaluated to identify those resources that satisfy the resource metadata selection criteria. For those resources that satisfy the resource metadata selection criteria, the resource attribute may be added to the resource metadata maintained for the different resources.

A provider network hosting multiple network-based services that implement different resources for a client may provide automated management of resource attributes across the multiple network-based services. A client may send a request to a resource attribute service implemented at the provider network to add a resource attribute to different resources implemented among different network-based services that satisfy resource metadata selection criteria. In response to receiving the request, resource metadata maintained for the different resources implemented among the different network-based resources, which may include one or more previously applied resource attributes, may be evaluated to identify those resources that satisfy the resource metadata selection criteria. For those resources that satisfy the resource metadata selection criteria, the resource attribute may be added to the resource metadata maintained for the different resources.