At&t Intellectual Property I, L.p.

At&t Mobility Ii LLC

Purdue Research Foundation

Washington University

Yu Xiang

Zihui Ge

Yih-Farn Robin Chen

Ajay Mahimkar

Shomik Pathak

Xuan Liu

Yusef Shaqalle

Kaustubh Joshi

Vaneet Aggarwal

Jennifer Yates

Keitaro Uehara

Matti A Hiltunen

Richard Dale Schlichting

Tian Lan

James Reichel

Changhan Ge

A processing system may obtain a data set with records of network parameter changes, each record including at least one network parameter change and at least one attribute associated with a first aspect of a communication network, and a corresponding network performance indicator change. A first record may include a plurality of network parameter change groups. The processing system may next perform a de-confusion process by identifying a second record comprising a single network parameter change group, determining that a corresponding network performance indicator change is different from that of the at least the first record, and updating the data set to replace the first record with at least two replacement records. The processing system may apply at least one of the network parameter change groups to a second aspect of the communication network based upon a decision output of a classifier that is trained using the updated data set.

Performance-driven network parameter changes in a communication network

A method performed by a processing system including at least one processor includes grouping a plurality of nodes of a telecommunications network into a plurality of reference groups, based on a plurality of configuration attributes and on a plurality of load, mobility, radio frequency attributes for the plurality of nodes, selecting a first reference group of the plurality of reference groups, where the first reference group includes a subset of the plurality of nodes, selecting a first configuration parameter of the first reference group to be tuned, identifying a first value for the first configuration parameter that is most prevalent among the subset of the plurality of nodes, and setting the first configuration parameter for all nodes in the subset of the plurality of nodes to the first value.

Optimization of long term evolution/fifth generation service through conformity-based recommendations

Cloud-based orchestration may be leveraged to create flexible storage solutions that use continuous adaptation to tailor themselves to their target application workloads that made provide efficiencies in performance, cost, or scalability over conventional designs.

Adaptive software-defined storage for cloud storage workloads

Encoding a file into a plurality of chunks, wherein a subset of the plurality of chunks may be used to create a functional equivalent of the file. At least one additional chunk is created from the plurality of chunks. The at least one additional chunk is directed to be stored in a cache memory and the plurality of chunks are directed to be stored on at least one storage node. Upon demand for the file, at least one additional chunk is cased to be retrieved from the cache and at least a portion of the plurality of chunks are caused to be retrieved from the at least one storage node and the functional equivalent of the file is constructed through utilization of the at least one additional chunk and the portion of the plurality of chunks.

Functional caching in erasure coded storage

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Somerset, NJ, United States of America

Yu Xiang