Abstract: A method for dynamic and extensible creation of an extensible wireless network, using a set of drones that individually support server processes. The drones interact with one another, exchanging information, type of coverage, type and amount of throughput, location, etc. A control node connects to a wired network. The node operates a leader election protocol, captures state information from the drones, and positions/re-positions the drones as necessary. Drones are flown in to position and then engaged as necessary to stretch/adapt the coverage as necessary. The drone's power utilization is monitored and its coverage area modified as necessary to optimize power utilization. The control node performs drone-based coverage/power utilization computations, and attempts to apply the appropriate location assignments to provide maximum network coverage (extensibility) while also preserving drone-specific power (battery) utilization.

Abstract: A method for dynamic and extensible creation of an extensible wireless network, using a set of drones that individually support server processes. The drones interact with one another, exchanging information, type of coverage, type and amount of throughput, location, etc. A control node connects to a wired network. The node operates a leader election protocol, captures state information from the drones, and positions/re-positions the drones as necessary. Drones are flown in to position and then engaged as necessary to stretch/adapt the coverage as necessary. The drone's power utilization is monitored and its coverage area modified as necessary to optimize power utilization. The control node performs drone-based coverage/power utilization computations, and attempts to apply the appropriate location assignments to provide maximum network coverage (extensibility) while also preserving drone-specific power (battery) utilization.

Abstract: Edge server compute capacity demand in an overlay network is predicted and used to pre-position compute capacity in advance of application-specific demands. Preferably, machine learning is used to proactively predict anticipated compute capacity needs for an edge server region (e.g., a set of co-located edge servers). In advance, compute capacity (application instances) are made available in-region, and data associated with an application instance is migrated to be close to the instance. The approach facilitates compute-at-the-edge services, which require data (state) to be close to a pre-positioned latency-sensitive application instance. Overlay network mapping (globally) may be used for more long-term positioning, with short-duration scheduling then being done in-region as needed. Compute instances and associated state are migrated intelligently based on predicted (e.g., machine-learned) demand, and with full data consistency enforced.

Abstract: Edge server compute capacity demand in an overlay network is predicted and used to pre-position compute capacity in advance of application-specific demands. Preferably, machine learning is used to proactively predict anticipated compute capacity needs for an edge server region (e.g., a set of co-located edge servers). In advance, compute capacity (application instances) are made available in-region, and data associated with an application instance is migrated to be close to the instance. The approach facilitates compute-at-the-edge services, which require data (state) to be close to a pre-positioned latency-sensitive application instance. Overlay network mapping (globally) may be used for more long-term positioning, with short-duration scheduling then being done in-region as needed. Compute instances and associated state are migrated intelligently based on predicted (e.g., machine-learned) demand, and with full data consistency enforced.

Abstract: A method for dynamic and extensible creation of an extensible wireless network, using a set of drones that individually support server processes. The drones interact with one another, exchanging information, type of coverage, type and amount of throughput, location, etc. A control node connects to a wired network. The node operates a leader election protocol, captures state information from the drones, and positions/re-positions the drones as necessary. Drones are flown in to position and then engaged as necessary to stretch/adapt the coverage as necessary. The drone's power utilization is monitored and its coverage area modified as necessary to optimize power utilization. The control node performs drone-based coverage/power utilization computations, and attempts to apply the appropriate location assignments to provide maximum network coverage (extensibility) while also preserving drone-specific power (battery) utilization.

Abstract: A content delivery network (CDN) comprises a set of edge servers, and a domain name service (DNS) that is authoritative for content provider domains served by the CDN. The CDN is extended into one or more mobile or wireline networks that cannot or do not otherwise support fully-managed CDN edge servers. In particular, an “Extender” is deployed in the mobile or wireline network, preferably as a passive web caching proxy that is beyond the edge of the CDN but that serves CDN-provisioned content under the control of the CDN. The Extender may also be used to transparently cache and serve non-CDN content. An information channel is established between the Extender and the CDN to facilitate the Extender functionality.

Abstract: Edge server compute capacity demand in an overlay network is predicted and used to pre-position compute capacity in advance of application-specific demands. Preferably, machine learning is used to proactively predict anticipated compute capacity needs for an edge server region (e.g., a set of co-located edge servers). In advance, compute capacity (application instances) are made available in-region, and data associated with an application instance is migrated to be close to the instance. The approach facilitates compute-at-the-edge services, which require data (state) to be close to a pre-positioned latency-sensitive application instance. Overlay network mapping (globally) may be used for more long-term positioning, with short-duration scheduling then being done in-region as needed. Compute instances and associated state are migrated intelligently based on predicted (e.g., machine-learned) demand, and with full data consistency enforced.

Abstract: A content delivery network (CDN) comprises a set of edge servers, and a domain name service (DNS) that is authoritative for content provider domains served by the CDN. The CDN is extended into one or more mobile or wireline networks that cannot or do not otherwise support fully-managed CDN edge servers. In particular, an “Extender” is deployed in the mobile or wireline network, preferably as a passive web caching proxy that is beyond the edge of the CDN but that serves CDN-provisioned content under the control of the CDN. The Extender may also be used to transparently cache and serve non-CDN content. An information channel is established between the Extender and the CDN to facilitate the Extender functionality.