An apparatus, including a plurality of devices, a network operations center (NOC), and a plurality of control nodes. Each device consumes a portion of the resource when turned on, and performs a corresponding function within an acceptable operational margin by cycling on and off. The NOC generates a plurality of run time schedules that coordinates run times for the each of the plurality of devices to control the peak demand of the resource. Each of the control nodes is coupled to a corresponding one of the devices. The plurality of control nodes transmits sensor data and device status to the NOC for generation of the plurality of run time schedules, and executes selected ones of the run time schedules to cycle the plurality of devices, and employs the sensor data and device status in a model to detect exceptions to normal operation of a facility.

Some demonstrative embodiments include apparatuses, systems and/or methods of communicating in a data path group. For example, an apparatus may include logic and circuitry configured to cause a Neighbor Awareness Networking (NAN) device to communicate during one or more Discovery Windows (DW) of a NAN cluster; and to communicate with one or more NAN devices of at least one data path group having a data path topology according to a scheduling scheme corresponding to the data path topology, the data path group including two or more NAN devices of the NAN cluster.

Embodiments of the present disclosure provide a method and an apparatus for data movement among distributed data centers in a peer network. The method comprises: reducing an amount of data to be moved by pre-processing the data; generating a torrent file for the data; distributing the torrent file to a peer data center, and in response to receiving a data request from the peer data center, transmitting a segment of the data to the peer data center. Compared with the prior art, the embodiments of the present disclosure can realize reliable and fast data movement among distributed data centers over an unreliable and low-bandwidth network, can support an adaptive network topology among distributed data centers without complex pre-configuration or cumbersome runtime coordination, can support different types of data storage, and facilitates coordinated cloud computing and data aggregation among distributed data centers.

An approach for sharing an asset in a peer-to-peer network is provided. After determining a locally stored first list does not include meta data specifying the asset, a new node is identified. In response to receiving a subscription from the new node, a second list locally stored at the new node is received. The second list includes the meta data and an identification of a source node that has the asset. The first list is updated to include the meta data and the identification of the source node. The updated first list is searched and in response, the meta data and the identification of the source node are detected. Based on the detected meta data and identification, the source node is identified. A request to retrieve the asset is sent to the source node, and in response, the asset is received.

A method scalably authorizes requests. A request to authorize access to a resource is received. A plurality of policies controlling the request is identified. The plurality of policies are concurrently processed. A decision for a policy is received. The decision is of a plurality of decisions corresponding to the plurality of policies. The policy is of the plurality of policies. The decision is determined using a machine learning model and the request. An aggregate decision is generated from the plurality of decisions. A token to access the resource is transmitted in response to the aggregate decision.

Disclosed are various embodiments for managing the state of client devices using device-driven management workflows. A computing device can be evaluated to determine the current state of the computing device. Then, the current state of the computing device is compared to an expected state of the computing device. The expected state of the computing device may be based at least in part on a result of execution of at least one device-driven management workflow by the computing device. In response to a determination that the current state of the computing device fails to match the expected state of the computing device, the device-driven management workflow can be executed to resolve the discrepancy between the expected state and the current state.

Embodiments of the invention are directed to an architecture modifier for intelligent node transfer and review processing. The engine collects user activity data from IoT devices and non-IoT applications to identify user activity and stores user workstation availability metrics. Upon receiving a request for review and consensus, the engine develops various user data routing configuration and schema based on live data feed for identification of nodes for transaction consensus and immediate review posting without any delay architectural delay.

Disclosed is a sharding block chain generation method based on a tree structure. Method comprises: dividing a server system into at least one group, and respectively selecting a leader of each group; packaging to generate at least one block by leader according to an own local database state, and broadcasting at least one block to all members of group in which leader is located; verifying consistency of at least one block, executing block according to a verification result of consistency, and synchronizing block to other groups. According to method, by means of tree grouping architecture, efficiency of generating block by a block chain framework is improved, block is generated according to own local database state of the leader, a resource waste phenomenon when block is generated in system is reduced, and delay when the block is generated is reduced.

A method of correlating probability assertions and resource allocations includes receiving a first probability assertion and a first resource allocation from a first client device; receiving a second probability assertion and a second resource allocation from a second client device; correlating the first probability assertion with the second probability assertion by matching characteristics of the first probability assertion with characteristics of the second probability assertion; and creating a peer-to-peer match between the first request on the second request from the client devices.

Methods, apparatus, systems and articles of manufacture to dynamically control devices based on distributed data are disclosed. An example apparatus includes a comparator to compare a first measurement measured by a first peer device to a second measurement, the second measurement being measured locally by the apparatus; and an operation adjuster to, when the comparison satisfies a threshold, adjust a measurement protocol of the first peer device.