System and method for distributed computation using heterogeneous computing nodes

Patent metadata

Field	Value
Publication number	US-11062047-B2
Application number	US-201414900061-A
Country	US
Kind code	B2
Filing date	Jun 9, 2014
Priority date	Jun 20, 2013
Publication date	Jul 13, 2021
Grant date	Jul 13, 2021

Abstract

This disclosure relates generally to the use of distributed system for computation, and more particularly, relates to a method and system for optimizing computation and communication resource while preserving security in the distributed device for computation. In one embodiment, a system and method of utilizing plurality of constrained edge devices for distributed computation is disclosed. The system enables integration of the edge devices like residential gateways and smart phone into a grid of distributed computation. The edged devices with constrained bandwidth, energy, computation capabilities and combination thereof are optimized dynamically based on condition of communication network. The system further enables scheduling and segregation of data, to be analyzed, between the edge devices. The system may further be configured to preserve privacy associated with the data while sharing the data between the plurality of devices during computation.

First claim

We claim:

1. A system for distributed computation in a communication network, the system comprising:
a processor;
a memory coupled to the processor , wherein the memory comprises a plurality of modules
capable of being executed by the processor, and wherein the plurality of modules
comprising:
a cluster monitoring module configured to receive information pertaining to a plurality of edge devices in a grid, the received information used for estimating computation capabilities of one or more non-dedicated edge devices, wherein the cluster monitoring module is further configured to create a partition list for data and maps the partition list with the plurality of edge devices, and wherein the cluster monitoring module captures one or more usage patterns of the plurality of edge devices and updates the grid when one or more new participating edge devices join the grid;
a privacy module configured to
provide privacy measurement by performing sensitivity analysis on the computation capability of at least one edge device of the plurality of edge devices and sensitive data content, wherein the privacy measurement is based on at least one factor selected from a computation capability of the at least one edge device from the plurality of edge devices, a privacy utility requirement and sensitivity of the data;
a data partitioning module configured to segregate the data into smaller data sets and apply a privacy preservation mechanism based on the privacy measurement of the data, wherein the data partitioning module facilitates optimization of bandwidth usage for the plurality of edge devices and appropriates mapping of the smaller data sets with each edge device among the plurality of edge devices based upon the partition list created by the cluster monitoring module; and
a communication module configured to
transfer the data to the plurality of edge devices for efficient load distribution during computation and to preserve privacy;

optimize bandwidth usage and energy consumption of the plurality of edge devices, during the transfer of the data over the communication network, the plurality of edge devices including one or more residential gateways, wherein the bandwidth usage and energy consumption is optimized by compressing the data by network encoding the data prior to the transfer of data, and
preserve a relation between length of coded data and number of edge devices receiving the data, wherein the communication module is further configured to reduce traffic via a group communication based communication scheme, to collaborate among the plurality of edge devices, and to dynamically utilize unused network capacity of the communication network;
a cluster interface module configured to provide feedback in at least one of a real time and a delayed mode, wherein the feedback is related to at least of usage of network connectivity and computation for the plurality of edge devices; and
a core scheduler module configured to allocate one or more smaller data sets to the plurality of edge devices, wherein the core scheduler module is further configured to schedule computation of data based on a list of partitions and size of the one or more smaller data sets, and wherein the allocation comprises scheduling of a data schedule for analysis based on the list of partitions, available edge devices, available bandwidth and latency.

2. The system of claim 1 , wherein the cluster monitoring module is further configured to create a list of partitions and the size for the smaller data sets, and detect failure of edge devices from the plurality of edge devices.

3. A processor implemented method for distributed computation in a communication network, the method comprising:
receiving by a processor information pertaining to a plurality of edge devices in a grid, the received information used for estimating computation capabilities of one or more non-dedicated edge devices;
creating, by the processor a partition list for data and mapping the partition list with the plurality of edge devices, and further capturing one or more usage patterns of the plurality of edge devices and updating the grid when one or more new participating edge devices join the grid;
providing privacy measurement by the processor, wherein the privacy measurement is determined by performing sensitivity analysis on the computation capability of at least one edge device of the plurality of edge devices and sensitive data content, wherein the privacy measurement is based on at least one factor selected from a computation capability of the at least one edge device from the plurality of edge devices, a privacy utility requirement and sensitivity of data;
segregating, by the processor the data into smaller data sets and apply a privacy preservation mechanism based on the privacy measurement of the data, wherein the segregation facilitates optimization of bandwidth usage for the plurality of edge devices and appropriates mapping of the smaller data sets with each edge device among the plurality of edge devices based upon the partition list;
transferring the data to the plurality of edge devices for efficient load distribution during computation and preserving privacy;
optimizing, by the processor, bandwidth usage and energy consumption of the plurality of edge devices, during the transfer of the data over the communication network the plurality of edge devices including one or more residential gateways, wherein the bandwidth usage and energy consumption is optimized by compressing the data by network encoding the data prior to the transfer of data; and
preserving, by the processor a relation between length of coded data and number of edge devices receiving the data;
reducing, by the processor, traffic via a group communication based communication scheme, to collaborate among the plurality of edge devices, and dynamically utilizing unused network capacity of the communication network;
providing, by the processor feedback in at least one of a real time and a delayed mode, wherein the feedback is related to at least of usage of network connectivity and computation for the plurality of edge devices ( 104 ); and
allocating one or more smaller data sets to the plurality of edge devices by the core scheduler module, wherein the core scheduler module is further configured to schedule computation of data based on a list of partitions and size of the one or more smaller data sets, and wherein the allocation comprises scheduling of a data schedule for analysis based on the list of partitions, available edge devices, available bandwidth and latency.

Assignees

• Tata Consultancy Services Ltd

Inventors

• Mukherjee Arijit
• Bandyopadhyay Soma
• Ukil Arijit
• Bhattacharyya Abhijan
• Dey Swarnava
• Pal Arpan
• Paul Himadri Sekhar

Classifications

• G06F21/6263 Primary

  during internet communication, e.g. revealing personal data from cookies · CPC title

• H04L63/0407

  wherein the identity of one or more communicating identities is hidden (cryptographic mechanisms or cryptographic arrangements for anonymous credentials or for identity based cryptographic systems H04L9/00) · CPC title

• H04W12/02

  Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII] · CPC title

• G06F21/6245 Primary

  Protecting personal data, e.g. for financial or medical purposes · CPC title

• H04L67/10

  in which an application is distributed across nodes in the network (software deployment G06F8/60; multiprogramming arrangements G06F9/46) · CPC title