Optimal deployment of fog computations in IoT environments

Patent metadata

Field	Value
Publication number	US-10439890-B2
Application number	US-201715653190-A
Country	US
Kind code	B2
Filing date	Jul 18, 2017
Priority date	Oct 19, 2016
Publication date	Oct 8, 2019
Grant date	Oct 8, 2019

Abstract

This disclosure relates to managing Fog computations between a coordinating node and Fog nodes. In one embodiment, a method for managing Fog computations includes receiving a task data and a request for allocation of at least a subset of a computational task. The task data includes data subset and task constraints associated with at least the subset of the computational task. The Fog nodes capable of performing the computational task are characterized with node characteristics to obtain resource data associated with the Fog nodes. Based on the task data and the resource data, an optimization model is derived to perform the computational task by the Fog nodes. The optimization model includes node constraints including battery degradation constraint, communication path loss constraint, and heterogeneous computational capacities of Fog nodes. Based on the optimization model, at least the subset of the computational task is offloaded to a set of Fog nodes.

First claim

What is claimed is:

1. A processor-implemented method for dynamically managing Fog computations between a coordinating node and a plurality of Fog nodes, the method at the coordinating node comprising:
receiving, a request for allocation of at least a subset of a computational task, and a task data associated with the computational task, via one or more hardware processors, the task data comprising data subset and one or more task constraints associated with at least the subset of the computational task;
characterizing the plurality of Fog nodes capable of performing the computational task with a plurality of node characteristics to obtain a resource data associated with the plurality of Fog nodes, via the one or more hardware processors;
deriving, based on the task data and the resource data, an optimization model for performing the computational task by the plurality of Fog nodes, via the one or more hardware processors, the optimization model comprises a plurality of node constraints including battery degradation constraint, communication path loss constraint, and heterogeneous computational capacities of the plurality of Fog nodes;
offloading at least the subset of the computational task to a set of Fog nodes from the plurality of Fog nodes based on the optimization model, via the one or more hardware processors; and
collating output of performing of the subset of computational task from the set of Fog nodes to obtain result of offloaded subset of the computational task.

2. The method of claim 1 , further comprising communicating with the plurality of Fog nodes while receiving the request and offloading at least the subset of the computational task using one of a proxy-based communication topology, peer-based communication topology and clone-based communication topology.

3. The method of claim 2 , wherein the resource data comprises node location, current drawn during performing the computational task and communication, total battery capacity, number of CPU cores and CPU operating frequency associated with the plurality of Fog nodes.

4. The method of claim 1 , wherein deriving the optimization model comprises deriving a battery optimization model for optimizing battery consumed in performing the computational task and a communication between the coordinating node and the plurality of Fog nodes, based on the resource data.

5. The method of claim 4 , wherein deriving the battery optimization model comprises minimizing an objective function associated with the battery consumption based on an equation:
min P 1 C .r 1+Σ i=2 N ( P i C +2. P 1 t {f,d 1 i }). r i

Such that, Σ i=1 N r i =C

P i .r i ≤P i Cap ,∀i∈N
r i ≥0,∀ i∈N

where, P i c is battery consumed as a result of computation C for ith fog node, and P i cap is the battery capacity.

6. The method of claim 1 , wherein deriving the optimization model comprises deriving a latency optimization model for optimizing a time to complete the computational task based on the resource data by the set of Fog nodes.

7. The method of claim 6 , wherein deriving the latency optimization model comprises minimizing an objective function associated with a transmission throughput of the resource data based on an equation:
min ⁡ ( 1 CC 1 · h 1 ) · r 1 + ∑ i = 2 N ⁢ ( 1 CC i · h i + 1 t 1 i ) · r i + Such that, Σ i=1 N r i =C

r i ≥0,∀ i∈N 3,

where, CC i represents number of CPU cores and corresponding operating frequency h i associated with ith Fog node, and t j i represents transmission throughput.

8. The method of claim 1 , wherein deriving the optimization model comprises:
deriving a battery optimization model for optimizing battery consumed in performing the computational task based on the resource data by the set of Fog nodes; and
deriving a latency optimization model for optimizing a time to complete the computational task based on the resource data by the set of Fog nodes, wherein deriving the battery optimization model and the latency optimization model comprises minimizing an objective function defined as:
min ⁢ ⁢ P 1 C · r 1 + ∑ i = 2 N ⁢ ( P i C + P i C + 2 · P 1 t ⁢ { f , d 1 i } ) · r i Such that, Σ i=1 N r i =C

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Assignees

• Tata Consultancy Services Ltd

Inventors

• Kattepur Ajay
• Rath Hemant Kumar
• Simha Anantha

Classifications

• H04L67/10

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

• H04W52/0261

  managing power supply demand, e.g. depending on battery level · CPC title

• H04L43/0852

  Delays · CPC title

• H04L41/0823

  characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability (for optimising operational conditions of wireless networks H04W24/02) · CPC title

• H04L41/145 Primary

  involving simulating, designing, planning or modelling of a network · CPC title