At&t Intellectual Property I, L.p.

Purdue Research Foundation

The State University of New York

University of Toronto

Shankaranarayanan Puzhavakath Narayanan

Gueyoung Jung

Kaustubh Joshi

Aleksandr Zelezniak

Bharath Balasubramanian

Vijay Gopalakrishnan

Subhabrata Bapi Sen

Matti A Hiltunen

Mithuna Shamabhat Thottethodi

Terani Nadadoor Vijaykumar

Eyal De Lara

Seyed Hossein Mortazavi

Sanjay Rao

Ashiwan Sivakumar

Joseph D'Andrea, Iii

Mohammad Saleheh

Muhammad Wajahat

Anshul Gandhi

A method includes creating a session token including a session identifier and a current node, capturing a set of queries in a cache from a client device at the current node associated with the session identification, receiving a request from the client for data access at a second node wherein the request includes the session token, transferring a subset of data records from the current node to the second node, wherein the subset of data records is selected based on the session identifier and the set of queries; and granting access to the client to the second node.

System and method for session-aware datastore for the edge

Described is centralized scheduling of baseband unit resources of a hub, including allocating and deallocating baseband unit resources of a distributed unit instance based on anticipated and/or actual demand for the resources. For example, when user equipment transitions to a connected state, a corresponding message can be detected and used to determine whether sufficient baseband unit resources exist to handle the traffic of the newly connecting user equipment. If not, additional baseband unit resources are allocated, coupled to a node (cell), and the node scheduled to handle the user equipment traffic. When user equipment transitions to an inactive state, the corresponding command can be detected and used to determine whether the baseband unit resources are still needed for other traffic. If not, the baseband unit resources are deallocated.

Baseband unit pooling using shared scheduler

Described is allocating and deallocating baseband unit resources of a distributed unit based on anticipated and/or actual demand for the resources. For example, when user equipment transitions to a connected state, a corresponding message can be detected and used to determine whether sufficient baseband unit resources are needed to handle the traffic of the newly connecting user equipment. If not, additional baseband unit resources are allocated. When user equipment transitions to an inactive state, the corresponding command can be detected and used to determine whether the baseband unit resources are still needed for other traffic. If not, baseband unit resources are deallocated; that is, if a distributed unit's resources are no longer needed, the distributed unit's resources are deactivated to reduce resource consumption. Deallocation can be performed by changing the resources to a power conservation (e.g., sleep) state, with allocation performed by changing such resources back to an active state.

Sharing of baseband units in fifth generation networks and beyond

Described is pooling baseband units into a hub and mapping radio units associated with the hub to baseband units of the group based on respective resource capacity data of the respective baseband units and an estimated resource usage data of a device that couples to a baseband unit. In one alternative, estimated peak resource usage data of a radio unit over an operation interval is used to select a baseband unit, generally based on which baseband unit of the pool has the least remaining resource capacity, to which the radio unit is mapped for the operation interval. In another alterative, estimated peak resource usage data resource (usage and duration) of a user equipment session is used to dynamically map the user equipment session to a baseband unit, generally selected by which baseband unit of the pool has the least remaining resource capacity.

Pooling of baseband units in fifth generation networks and beyond

A system that determines whether a trigger has occurred within a cloud infrastructure. The system, in response to determining that a trigger has occurred, extracts characteristics from one or more virtual network functions (VNFs) of a service chain. The system, in response to extracting characteristics from the one or more VNFs, determines rehoming actions for each of the one or more VNFs. The system, in response to determining rehoming actions, predicts a rehoming delay or a chain downtime for each of the rehoming actions for each of the one or more VNFs. The system determines an optimal rehoming action from the rehoming actions for at least one of the one or more VNFs using the rehoming delay or the chain downtime for each rehoming action of the rehoming actions. The system performs the optimal rehoming action for the at least one of one or more VNFs.

Model-driven technique for virtual network function rehoming for service chains

A whittling proxy system can receive a target web page that includes a plurality of Javascript functions and a plurality of objects needed to load the target web page. The system can determine a target Javascript function of the plurality of Javascript functions to be tested for a whittling capability. The system can generate a full version and a partial version of the target web page. The full version includes all of the plurality of Javascript functions. The partial version includes the plurality of Javascript functions excluding the target Javascript function. The system can conduct a test to determine a first page signature associated with the full version and a second page signature associated with the partial version. The system can determine if the first page signature matches the second page signature, and if so, can determine that the target Javascript function can be whittled.

Scalable whittled proxy execution for low-latency web over cellular networks

The concepts and technologies disclosed herein are directed to a policy-driven homing service system that can receive, from a master service orchestrator, a demand specifying a service component to be used to provide, at least in part, a service. The system can receive, from a policy system, a homing constraint. The system can determine, for the demand, an initial set of all potential candidates. The system can apply the homing constraint to each potential candidate in the initial set of all potential candidates. The system can determine a resultant set of potential solutions that satisfy the homing constraint. The system can determine a best solution from the resultant set of potential solutions. The system can send the best solution to the master service orchestrator, which instantiates the demand based upon the best solution.

Policy-driven homing service system

Growing community of inventors

Hillsborough, NJ, United States of America

Shankaranarayanan Puzhavakath Narayanan