Computational Task Scheduling via Edge Processing

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Cloud computing services face latency and privacy concerns due to limited network bandwidth and server compute capacity, leading to inefficiencies in processing computational tasks, especially when handling sensitive user data.

Innovation Solution

A method that forwards computational tasks from a cloud server to a processing device, such as a residential gateway, if certain conditions are met, allowing for faster execution and improved data security by reducing data transfer times and maintaining privacy by processing sensitive data locally.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If computational tasks are processed by cloud servers, then computing resources can be shared and scalability is improved, but latency increases due to network bandwidth limitations and data round trip times

Engineering Contradiction:

Improvecomputing resource sharingVSAvoidlatency

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The patent segments the computing system into multiple components: cloud servers for resource sharing, edge servers for intermediate processing, and local devices for immediate processing. This segmentation allows tasks to be processed at different levels of the hierarchy, reducing latency while maintaining resource sharing capabilities.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces edge servers as intermediary components between cloud servers and local devices. These edge servers act as mediators that can process computational tasks locally, reducing the need for data to travel to distant cloud servers and back, thereby decreasing latency while still enabling resource sharing.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If computational tasks are processed by cloud servers, then compute capacity can be scaled, but latency increases due to limited server compute capacity

Engineering Contradiction:

Improvecompute capacity scalingVSAvoidprocessing latency

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The computing infrastructure is segmented into cloud servers for bulk compute capacity and edge servers for rapid local processing. This segmentation allows the system to scale compute capacity through cloud servers while using edge servers to handle time-sensitive tasks locally, reducing processing latency.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent adds a spatial dimension to the computing architecture by distributing processing across multiple locations (cloud data centers and edge locations closer to users). This dimensional expansion allows the system to provide both scaled compute capacity and reduced latency by processing tasks nearer to the user.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

3Productivity

If user data is uploaded to cloud servers for processing, then computational resources can be utilized, but data privacy concerns arise

Engineering Contradiction:

Improvecomputational processingVSAvoiddata privacy risk

Core Design Contradiction:

ProductivityVSObject-affected harmful factors

Solution Approach 1:

The patent applies local quality by allowing data to be processed locally at edge servers or local devices when possible, rather than always uploading to cloud servers. This enables computational processing to occur in locations with different privacy characteristics, reducing data privacy risks for sensitive operations while maintaining productivity.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS10951521B2Method for scheduling a computational task, a method for processing a computational task, a computer readable storage medium, a computer program, a residential gateway, and a server

Publication Date: 2021.03.16 MAXLINEAR INC

AI summary

A method for scheduling a computational task is proposed. The method includes receiving, at a server, a request for executing a computational task from a client device. The method further includes forwarding the computational task to a processing device if a predetermined condition is fulfilled. The predetermined condition can be based on an execution time or on a security level of data of the computational task, for example.