Abstract: Techniques are provided for logical network resource allocation and creation. One method comprises obtaining a representation of virtualized resources to allocate for a logical network within a physical network, wherein the physical network comprises physical nodes and physical links, wherein the virtualized resources comprise virtual nodes and virtual links, and wherein the physical nodes host one or more virtual nodes and one or more of the physical links host a given virtual link; obtaining a first ordered list of the plurality of virtual nodes; obtaining a second ordered list of the plurality of physical nodes; for a virtual node in the first ordered list: selecting and allocating a physical node from the second ordered list as a host for the virtual node; and allocating a physical path for the virtual links associated with the virtual node. The logical network can be created using the allocated physical nodes and physical links.

Abstract: Placing virtual network functions (VNFs) in nodes of an infrastructure are disclosed. A request for services may identify a series of VNFs. Requests may be grouped by time windows such that all requests in a time window are placed together. Placing the requests includes identifying which nodes host which VNFs. Some of the VNFs may already exist in the infrastructure. Placing the requests may include generating a placement plan that accounts, for example, for available resources, links in the infrastructure, node restrictions, service level agreements, quality of service requirements, and user priorities. Once generated, the plan is executed. Chains of VNFs can be replaced when service degrades or based on user location.

Abstract: A method for managing a service function chain (SFC) includes obtaining, by a SFC orchestrator of a first domain, a SFC creation request for a SFC, in response to the SFC creation request: generating a domain participation list of a set of potential domains that may service the SFC creation request, sending a bid request to each of the set of potential domains, receiving bid responses from the set of potential domains, ranking the set of potential domains, sending a SFC placement request to a second domain of the set of potential domains based on the ranking, and obtaining a SFC placement response from the second domain, wherein the SFC placement response specifies success of SFC placement in the second domain.

Abstract: 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.

Abstract: Market-based distributed resource allocation techniques are provided for edge-cloud systems. One method comprises obtaining an application request at a given edge node in a multi-tier environment comprising cloud resources and multiple edge nodes. The edge nodes host a plurality of virtual nodes to process the application request. The application request is assigned to at least one of the virtual nodes based on a utility value of each virtual node. The utility value of each virtual node is based on a cost value representing a total cost incurred by each virtual node to process the application request. The utility value of each virtual node is optionally further based on a priority value of the application request. Master nodes from different edge node groups can collaborate to identify a given edge node group that can serve the application request when local edge nodes are unable to process the at least one application request.

Abstract: A resource management framework is provided for edge-cloud systems that supports heterogeneity of devices and application requirements. One method comprises obtaining an application request in a multi-tier environment comprising cloud resources and edge nodes, wherein the edge nodes host virtual nodes to process the application requests, wherein each of the plurality of virtual nodes corresponds to a given predefined virtual node type; providing data from a given data source to at least two virtual nodes based on a data type of the given data source and the virtual node type of the at least two virtual nodes; and providing a given application request to at least one virtual node based on a data type of the given application request and the virtual node type of the at least one virtual node, wherein the at least one virtual node provides data in response to the application requests to corresponding applications and/or additional virtual nodes.

Abstract: Techniques are provided for resource allocation and provisioning in a multi-tier edge-cloud virtualization environment. An exemplary method comprises: obtaining an application request for processing a given data type in a multi-tier environment; processing application requests received within a decision window to allocate resources for virtual nodes to process the application requests received within the decision window, wherein the allocated resources for each virtual node is on a corresponding one of cloud resources and a given edge node; instantiating the allocated virtual nodes to process the application requests; and providing the application requests to the instantiated virtual nodes, wherein the instantiated virtual nodes obtain the data of the given data type from a data repository.

Abstract: Market-based distributed resource allocation techniques are provided for edge-cloud systems. One method comprises obtaining an application request at a given edge node in a multi-tier environment comprising cloud resources and multiple edge nodes. The edge nodes host a plurality of virtual nodes to process the application request. The application request is assigned to at least one of the virtual nodes based on a utility value of each virtual node. The utility value of each virtual node is based on a cost value representing a total cost incurred by each virtual node to process the application request. The utility value of each virtual node is optionally further based on a priority value of the application request. Master nodes from different edge node groups can collaborate to identify a given edge node group that can serve the application request when local edge nodes are unable to process the at least one application request.

Abstract: A resource management framework is provided for edge-cloud systems that supports heterogeneity of devices and application requirements. One method comprises obtaining an application request in a multi-tier environment comprising cloud resources and edge nodes, wherein the edge nodes host virtual nodes to process the application requests, wherein each of the plurality of virtual nodes corresponds to a given predefined virtual node type; providing data from a given data source to at least two virtual nodes based on a data type of the given data source and the virtual node type of the at least two virtual nodes; and providing a given application request to at least one virtual node based on a data type of the given application request and the virtual node type of the at least one virtual node, wherein the at least one virtual node provides data in response to the application requests to corresponding applications and/or additional virtual nodes.

Abstract: Techniques are provided for resource allocation and provisioning in a multi-tier edge-cloud virtualization environment. An exemplary method comprises: obtaining an application request for processing a given data type in a multi-tier environment; processing application requests received within a decision window to allocate resources for for virtual nodes to process the application requests received within the decision window, wherein the allocated resources for each virtual node is on a corresponding one of cloud resources and a given edge node; instantiating the allocated virtual nodes to process the application requests; and providing the application requests to the instantiated virtual nodes, wherein the instantiated virtual nodes obtain the data of the given data type from a data repository.

Abstract: An architecture, methods and apparatus are provided for managing sensor data. Sensor networks comprised of a plurality of sensors are managed by obtaining measurement data and context data from the plurality of sensors; storing the obtained measurement data and context data using a Massively Parallel Processing Database Management System (MPP DBMS); and managing the sensor network from outside of the sensor network using the MPP DBMS. Context-aware adaptation of sensors is based on context regarding a state of the sensor network and context regarding a state of one or more applications. The sensor nodes are optionally clustered based on semantic similarities among sensor readings from different sensor nodes and a distance among the sensor nodes. A subset of the sensor nodes is optionally selected to be active based on a residual energy of the sensor nodes and a relevance of the sensor nodes to an application. Data prediction models are generated and employed for data sensing and analytics.

Abstract: An architecture, methods and apparatus are provided for managing sensor data. Sensor networks comprised of a plurality of sensors are managed by obtaining measurement data and context data from the plurality of sensors; storing the obtained measurement data and context data using a Massively Parallel Processing Database Management System (MPP DBMS); and managing the sensor network from outside of the sensor network using the MPP DBMS. Context-aware adaptation of sensors is based on context regarding a state of the sensor network and context regarding a state of one or more applications. The sensor nodes are optionally clustered based on semantic similarities among sensor readings from different sensor nodes and a distance among the sensor nodes. A subset of the sensor nodes is optionally selected to be active based on a residual energy of the sensor nodes and a relevance of the sensor nodes to an application. Data prediction models are generated and employed for data sensing and analytics.