Network Slice Reallocation Using Data-Aware Edge and Cloud Management
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Solution Overview
Problem
Current 5G systems lack data-centric services for application optimization and load balancing, leading to inefficiencies in processing and resource management, particularly in high computational applications requiring real-time processing and ultra-low latency.
Innovation Solution
Implementing a data-aware computing and storage management function (DCSMF) that dynamically reallocates network and computing resources based on application requirements, resource availability, and network conditions, using machine learning and AI to optimize traffic and storage policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If heavy or material-rich devices with high processing capacity are used, then processing capability is improved, but manufacturing cost and device complexity increase
Solution Approach 1:
The patent segments processing tasks between edge devices and cloud data centers. Edge devices handle local real-time processing while non-critical tasks are offloaded to cloud, avoiding the need for every device to have high-end hardware while maintaining overall system processing capability.
Solution Approach 2:
The patent introduces edge computing nodes as intermediaries between end devices and cloud data centers. These edge nodes provide local processing capacity for time-sensitive tasks, reducing the need for expensive high-end devices at every endpoint while maintaining low latency.
2Speed
If more processing tasks are handled locally on devices, then processing speed is improved, but energy consumption increases
Solution Approach 1:
The patent implements dynamic task allocation that adjusts in real-time based on device energy levels, network conditions, and task priority. Critical time-sensitive tasks are processed locally when energy is available, while non-critical tasks are offloaded to cloud, creating a dynamic balance between speed and energy consumption.
Solution Approach 2:
The patent applies partial processing locally and partial processing in the cloud rather than all-or-nothing local processing. This partial action approach processes only the most time-critical portions locally, reducing overall energy consumption while maintaining acceptable processing speeds for urgent tasks.
3Loss of time
If data is processed closer to the source in distributed computing, then latency is reduced, but network complexity increases
Solution Approach 1:
The patent implements local quality by deploying edge computing nodes at strategic locations in the network. These nodes provide local processing capabilities closer to data sources, reducing latency for regional tasks while maintaining centralized cloud connectivity for non-time-critical operations.
4Productivity
If computing resources are dynamically reallocated based on demand, then resource utilization efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where edge computing nodes continuously monitor local processing demands, device capabilities, and energy levels. This feedback information is used to dynamically allocate tasks between edge and cloud, optimizing resource utilization while maintaining manageable system complexity through automated decision-making.
Solution Approach 2:
The patent enables self-service through automated task allocation algorithms that make decisions based on predefined policies and real-time conditions. The system autonomously determines which tasks to process locally versus offloading to cloud, reducing the need for complex centralized control while improving resource utilization efficiency.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, obtaining data, the data including first data indicative of a first amount of resources available as part of a first network slice of a plurality of network slices of a communication network, analyzing the data to determine that at least a first portion of a service provisioned via the first network slice is to be reallocated to at least a second network slice of the plurality of slices, and reallocating, based on the analyzing, the at least a first portion of the service to the at least a second network slice. Other embodiments are disclosed.