Distributed Resource Allocation Framework for Latency Reduction

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Data centers face challenges in efficiently allocating computing resources across different geographic regions due to varying communication latencies and increased costs associated with data transfer, which can impact customer cost and performance constraints.

Innovation Solution

A resource distribution framework that analyzes customer allocations and automatically configures master resources to maintain cost and performance constraints, reducing data transfer and latency by optimizing the placement of master and slave resources across regions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If computing resources are allocated across different geographic regions, then resource availability and scalability are improved, but communication latency and data transfer costs increase

Engineering Contradiction:

Improveresource availabilityVSAvoidcommunication latency

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system segments computing resources into master instances and slave instances, allowing them to be distributed across different geographic regions. Master instances are strategically placed to minimize latency for slave instances while maintaining overall resource availability and scalability across the distributed architecture.

Inventive Principle:

Principle #1Segmentation

2Adaptability or versatility

If computing resources are allocated across different geographic regions, then resource availability and scalability are improved, but data transfer costs increase

Engineering Contradiction:

Improveresource availabilityVSAvoiddata transfer cost

Core Design Contradiction:

Adaptability or versatilityVSLoss of energy

Solution Approach 1:

The system applies local quality by colocating slave instances with their corresponding master instances in the same geographic region. This localization minimizes cross-region data transfer and associated costs while maintaining the ability to scale resources across multiple regions when needed.

Inventive Principle:

Principle #3Local quality

3Adaptability or versatility

If master resources are distributed across multiple regions, then system scalability is improved, but system complexity increases

Engineering Contradiction:

Improvesystem scalabilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system introduces a resource distribution framework that acts as an intermediary to automatically manage the placement and configuration of master and slave instances across regions. This framework simplifies the complexity of distributed resource management by providing automated decision-making logic for resource allocation while maintaining scalability.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS10069693B1Distributed resource allocation

Publication Date: 2018.09.04 AMAZON TECH INC

AI summary

In a computing environment, a request to fulfill a computational task and a constraint for fulfilling the computational task is received from an entity. The request is independent of the quantity or type of resource slot to fulfill the computation task. The quantity or type of resource slots sufficient to fulfill the request is determined in accordance with a constraint. The resource slots are associated with the entity and allocated a plurality of geographically separate computing environments. Master resource slots are allocated in the geographically separate computing environments based on the criterion and the quantity or type of resource slots. The master resource slots can be reallocated based on changes to the allocated resources slots.