The invention relates to a device, system and method for controlling application components in an application control network (300) comprising at least a first network component (110, 180, 301) connected to a first data port of a first data forwarding device (102, 110, 180) of the application control network. A configuration profile for the at least one network component (110, 180, 301) is determined in accordance with an application plan comprising timing and interaction information of the application components within the application control network. The configuration profile is transmitted to a network component (102, 110, 180) communicatively close to the at least first network component, in particular the data forwarding device the first network component is attached to, for storage and for subsequent transmission to the first network component (110, 180, 301) after the first network component (110, 180, 301, L1-L3) has been rebooted.

One example method includes creating an ILP model that includes a delay model, an energy model, and a QoS model, and modeling, using the integer linear programming model, a VNF placement problem as an ILP problem, and the modeling includes: using the delay model to identify propagation, transmission, processing, and queuing, delays implied by enabling an instance of the VNF at an edge node to accept a user VNF call; using the energy model to identify energy consumption implied by enabling an instance of the VNF at an edge node to accept a user VNF call; and using the QoS model to identify end-to-end delay, bandwidth consumption, and jitter, implied by enabling an instance of the VNF at an edge node to accept a user virtual network function call. The problem modeled by the ILP model may be resolved by a heuristic method.

An energy conservation management method includes selecting, by a policy server, a target scenario model from a plurality of scenario models based on a plurality of scenario attribute values of a target network, and configuring, by the policy server, a network device in the target network based on an energy conservation policy of the target scenario model. At least one of the plurality of scenario attribute values is an attribute value of a scenario attribute related to a network power consumption of the target network. The at least one of the plurality of scenario attribute values includes a plurality of attribute values of a corresponding scenario attribute, wherein each of the plurality of attribute values is collected at a different time from other attribute values of the plurality of attribute values that are collected at different times.

An energy conservation management method includes selecting, by a policy server, a target scenario model from a plurality of scenario models based on a plurality of scenario attribute values of a target network, and configuring, by the policy server, a network device in the target network based on an energy conservation policy of the target scenario model. At least one of the plurality of scenario attribute values is an attribute value of a scenario attribute related to a network power consumption of the target network. The at least one of the plurality of scenario attribute values includes a plurality of attribute values of a corresponding scenario attribute, wherein each of the plurality of attribute values is collected at a different time from other attribute values of the plurality of attribute values that are collected at different times.

A multi-tenant service platform provides network services, such as content delivery, edge compute, and/or media streaming, on behalf of, or directly for, a given tenant. The service platform offers a policy layer enabling each tenant to specify levels of acceptable performance degradation that the platform may incur so that the platform can use electricity with desirable characteristics to service client requests associated with that tenant. Service nodes in the platform (e.g., edge servers) enforce the policy layer at the time of a service request. Preferably, the ‘quality’ of the electricity is a measurement of source of the energy, e.g., whether it is sourced from high-carbon fossil fuels (low-quality) or low-carbon renewables (high-quality). If the desired quality of electricity cannot be achieved, the node can resort to using less electricity to handle the request, which is achieved in a variety of ways.

A method can include obtaining information on at least one of the following: resource occupation of a reconfigurable functional unit associated with hardware accelerator resources or GPP resources, power consumption of a hardware accelerator associated with hardware accelerator resources, and power consumption of a server associated with GPP resources. The method can also include performing processing on the reconfigurable functional unit based on the obtained information, the processing including at least one of configuration, reconfiguration, and migration. The method and apparatus of certain embodiments may increase efficiency of resource management of the edge cloud, lower system energy consumption, and/or enable more efficient virtualization mechanisms for hardware accelerator resources.

Technologies for implementing edge intelligence for utility communication networks are provided. For example, a system includes a mesh network and a utility fog configured to manage the mesh network. The utility fog includes a secure utility system configured for executing a private utility application and a first edge intelligence device configured for executing a first subset of software applications. Each software application is configured to manage endpoints in the mesh network or process data collected by the mesh network. The mesh network includes the endpoints and an edge intelligence device configured for executing a second subset of the software applications that is different from the first subset of software applications.

Technologies for implementing edge intelligence for utility communication networks are provided. For example, a system includes a mesh network and a utility fog configured to manage the mesh network. The utility fog includes a secure utility system configured for executing a private utility application and a first edge intelligence device configured for executing a first subset of software applications. Each software application is configured to manage endpoints in the mesh network or process data collected by the mesh network. The mesh network includes the endpoints and an edge intelligence device configured for executing a second subset of the software applications that is different from the first subset of software applications.

The disclosure is directed to a network planning tool for planning a topology of a computer network, e.g., for provisioning network capacity. The network planning tool evaluates various factors, e.g., demand projections between a pair of nodes, existing network topology, existing circuits, failure scenarios, and other constraints, and generates a set of circuits that satisfies various demand projections. The set of circuits is robust under failure scenarios and minimizes latency, costs and/or power consumption involved in satisfying the demand projections. The tool assigns each of the circuits to a spectral resource of a physical communication link, e.g., a wavelength of a fiber optic cable, using which it can propagate data traffic between the pair of nodes.

A CPU of an image forming apparatus controls, according to establishment of a sleep change condition, a MAC/PHY in a network unit to change the image forming apparatus from a link-up state to a link-down state. The CPU controls the image forming apparatus to change from a normal mode to a deep sleep mode. A CPU of the network unit controls, according to the change of the image forming apparatus from the normal mode to the deep sleep mode, the MAC/PHY to change the link-down state of the image forming apparatus to the link-upstate. The CPU controls the MAC/PHY to transmit a MAC address necessary for causing the image forming apparatus to participate in VLAN to a switching hub.