Cache Memory Balancing via Vehicle Movement Prediction

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Solution Overview

Problem

In IoT environments, cache memory imbalances occur when large numbers of moving objects concentrate in specific regions, leading to computational latency due to rapid data swapping between servers, which is exacerbated by unforeseen events like traffic congestion or vehicle migrations.

Innovation Solution

A cache memory balancing engine tracks vehicle movements and predicts direction trends to determine if a regional server is likely to have an unbalanced cache, allowing for proactive rebalancing by shifting cache data between servers, thereby maintaining optimal load distribution and reducing latency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If cache memory data is rapidly swapped between servers to handle moving vehicles, then real-time query processing capability is improved, but computational latency increases due to cache memory imbalances

Engineering Contradiction:

Improvereal-time query processing speedVSAvoidcomputational latency

Core Design Contradiction:

SpeedVSLoss of time

Solution Approach 1:

The system performs preliminary actions by predicting vehicle migration trends using velocity analysis before actual migrations occur. The cache balancing engine proactively identifies vehicles likely to migrate and pre-loads their required cache data into target servers, ensuring data is ready when vehicles arrive, thus avoiding latency during actual migration events

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements dynamic cache management by continuously monitoring vehicle velocity, direction, and regional distribution. The cache balancing engine dynamically adjusts data placement decisions based on real-time vehicle movement patterns, transitioning from static regional cache allocation to adaptive, movement-aware cache distribution that optimizes for current traffic conditions

Inventive Principle:

Principle #15Dynamics

2Adaptability or versatility

If vehicles migrate unexpectedly between regions, then system adaptability is improved, but cache memory balance deteriorates causing server overload

Engineering Contradiction:

Improvesystem adaptability to vehicle migrationVSAvoidcache memory balance

Core Design Contradiction:

Adaptability or versatilityVSStability of the object's composition

Solution Approach 1:

The system implements feedback mechanisms where the cache balancing engine continuously monitors actual vehicle migration patterns and compares them against predictions. This feedback loop allows the system to learn from past migrations, refine velocity-based prediction accuracy, and adjust cache balancing strategies to better handle unexpected migration events while maintaining overall cache balance

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

By analyzing current vehicle velocity and direction, the system takes preliminary actions to prepare for upcoming migrations before they happen. The engine identifies vehicles with high migration probability and pre-distributes their cache data to appropriate regional servers, preventing cache imbalances before they occur rather than reacting after migrations happen

Inventive Principle:

Principle #10Preliminary action

3Reliability

If continuous real-time communication is implemented for moving objects, then operational decision quality is improved, but network bandwidth consumption increases

Engineering Contradiction:

Improveoperational decision qualityVSAvoidnetwork bandwidth consumption

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The system applies local quality by maintaining different communication strategies for different vehicle regions. The cache balancing engine identifies concentrated vehicle groups and routes their queries to regional servers with appropriate cache data, while dispersed vehicles communicate directly with central servers. This localized approach reduces overall bandwidth consumption by serving data from the nearest appropriate cache rather than forcing all vehicles to use the same communication path

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS10171614B2Cache memory balancing based on moving object prediction

Publication Date: 2019.01.01 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A computer-implemented method for cache memory management in a distributed computing environment includes tracking, via a cache balancing engine, a plurality of vehicles operating in a first region. The method further includes executing, via the cache balancing engine, an analysis for a subset of the plurality of vehicles in the first region to determine a weighted mean velocity for each vehicle in the subset of the plurality of vehicles in the first region. The method further includes determining, via the cache balancing engine, based on the analysis, whether a second regional server tracking a second plurality of vehicles in a second region is likely to have an unbalanced cache memory, and rebalancing, via the cache balancing engine, cache memory data in the first regional server and cache memory data in the second regional server responsive to determining that the second regional server is likely to have an unbalanced cache memory.