Red Lambda, Inc.

Akamai Technologies, Inc.

Robert Bird

Matthew Whitlock

Adam Leko

Mark M Ingerman

Tushar Mulkar

Aniruddha Bohra

Vinay Kanitkar

Michael G Merideth

Yash Pradhan

Greg Barton

A system and method for managing distributed shaping in a computer network. The system including: a plurality of logic nodes, each logic node including: a packet processor configured to determine metrics associated with traffic flows in the computer network; a control processor configured to determine a quality value based on the determined metrics and communicate with each of the plurality of logic nodes; and a shaper object configured to shape the network traffic. The method including: initializing each logic node of a plurality of logic nodes; determining metrics associated with traffic flows in the computer network; determining a quality value based on the determined metrics; communicating the metrics with each of the plurality of logic nodes; and shaping the network traffic flows based on the quality value.

System and method for managing distributed shaping in a computer network

A distributed computing system provides a distributed data store for network enabled devices at the edge. The distributed database is partitioned such that each node in the system has its own partition and some number of followers that replicate the data in the partition. The data in the partition is typically used in providing services to network enabled devices from the edge. The set of data for a particular network enabled device is owned by the node to which the network enabled device connects. Ownership of the data (and the data itself) may move around the distributed computing system to different nodes, e.g., for load balancing, fault-resilience, and/or due to device movement. Security/health checks are enforced at the edge as part of a process of transferring data ownership, thereby providing a mechanism to mitigate compromised or malfunctioning network enabled devices.

Fast, secure, and scalable data store at the edge for connecting network enabled devices

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.

Intelligently pre-positioning and migrating compute capacity in an overlay network, with compute handoff and data consistency

A system, a method and a computer readable medium for storing data elements and related data provenance information. The data elements may be represented in a hyper-table having rows and columns which may be indexed. The data-values of the corresponding data-elements in the hyper-cells may be retrieved based on the indices. Snapshots of the indices may be generated at pre-determined time periods. Checkpoints of the hyper-table may be generated at time periods that are based on transactions on the hyper-table. The hyper-table is capable of being queried as the hyper-table existed at certain time-periods, and data-values of the data-elements may be retrieved as the data-elements existed at such time-periods.

System and method for storing stream data in distributed relational tables with data provenance

A system, method and computer readable medium for indexing streaming data. Data may be received from distributed devices connected via a network. Data elements may be stored and allocated to data blocks and events of the block stores. Non-text data may be converted into a text representation. The data may be split into terms, and term frequencies of each term within each of the event may be calculated. Block-level term frequency statics may be calculated based on the term frequencies. Tree index structures, such as the Y-tree index, may be generated based on the block-level term frequency data. The Y-tree index structures may use the terms as keys and pointers to the corresponding data blocks and block-level term frequency data. A search query may be performed over the tree index structures.

System and method for indexing streams containing unstructured text data

A data classification system comprising a server adapted to receive data elements from a computer network, compute a distance between the elements and a plurality of representative elements such that where the distance is less than a threshold, the input elements are associated with the representative element and where the distance is not less than the threshold, the input element is stored as a new representative element. A method of classifying network traffic is further disclosed that can include providing a server adapted to receive data from a network, compute a distance between the data elements and representative data elements such that where the distance is less than a threshold, the input elements are associated with the representative element and where the distance is not less than the threshold, the input element is stored as a new representative element.

System, method and apparatus for classifying digital data

Systems and methods for storing and retrieving data elements transmitted via data streams received from distributed devices connected via a network. The received data elements may be stored in block stores on the distributed devices. The stored data-elements may be allocated to data-blocks of a block-store that have assigned block-identifiers and further allocated to events of the data-blocks that have assigned token-names. Stream-schema of the received data-streams may comprise a list of token-names and an index-definition for each corresponding data-stream. Indices may be generated for the event-allocated data-elements. A query may be executed in order to retrieve data-elements of the received data-streams based on the indices.

Systems for storing data streams in a distributed environment

A system and method for storing data-files stored on distributed devices connected to a network. Data-elements of the data-files are allocated to data-blocks stored on the distributed devices. Key-identifiers are calculated for each of the data-blocks based on the allocated data-elements. The key-identifiers are stored in distributed tables stored on the distributed devices. Index-nodes are generated for the data-files based on the data-blocks. A Paxos algorithm is executed for the index-nodes based on the key-identifiers to provide a consensus of the data-files that are stored on the plurality of distributed devices.

Systems for storing files in a distributed environment

Systems, methods and computer readable medium for storing data elements transmitted via data streams received from distributed devices connected via a network. The received data elements may be stored in block stores on the distributed devices. The stored data elements may be allocated to data blocks of a block store that have assigned block identifiers and further allocated to events of the data blocks. Stream schema of the received plurality of data streams may have the same stream schema, and indices may be generated indices based on the order of the event allocated data elements. Stream schema of the received data streams may comprise a list of token names. Token names may be assigned to the event allocated data elements. Indices may be generated for the event allocated data elements based on the stream schema.

Growing community of inventors

Longwood, FL, United States of America

Robert Bird