Task Offloading in Mobile Edge Cloud Networks
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Solution Overview
Problem
Current mobile edge cloud networks face high latency and network resource overhead due to geographical distance between clients and cloud servers, and existing methods for task offloading do not consider processing and transmission delays when determining whether to offload tasks to edge cloud servers.
Innovation Solution
Implementing a method within network elements of the mobile edge cloud network to determine whether to offload tasks to edge cloud servers based on energy consumption, latency, processing delays, and transmission delays, using Big Packet Protocol (BPP) packets to carry metadata and instructions for task execution, and selecting the most suitable edge cloud server based on offloading metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If tasks are offloaded to cloud computing networks, then computation power is improved, but latency and network resource overhead increase due to geographical distance
Solution Approach 1:
The patent distributes cloud computing resources locally at edge networks close to clients, creating geographically distributed edge cloud servers. This localizes computation power delivery, reducing the geographical distance between clients and servers, thereby decreasing latency while maintaining improved computation power.
Solution Approach 2:
The patent introduces a new dimensional approach by moving cloud services from centralized remote data centers to distributed edge locations along the network path. This spatial redistribution across multiple dimensions enables computation power to be delivered closer to clients without requiring a complete architectural overhaul.
2Loss of time
If tasks are offloaded to edge cloud servers, then latency is reduced, but energy consumption increases due to transmission and processing delays
Solution Approach 1:
The patent performs preliminary calculations of energy consumption and latency metrics before making offloading decisions. By pre-computing these parameters and establishing decision criteria in advance, the system can make optimal offloading choices that balance energy consumption against latency reduction, avoiding unnecessary transmissions that would waste energy.
Solution Approach 2:
The patent implements feedback mechanisms where the network element continuously monitors actual energy consumption and latency outcomes from offloading decisions. This feedback is used to refine future offloading decisions, learning from past performance to optimize the balance between energy usage and latency reduction.
3Productivity
If task offloading decisions are made without considering processing and transmission delays, then decision speed is improved, but service quality deteriorates
Solution Approach 1:
The patent changes the parameters considered in offloading decisions to include processing delays and transmission delays alongside energy consumption and latency. By incorporating these additional parameters into the decision-making framework, the system maintains reasonable decision speed while significantly improving service quality through more comprehensive evaluation.
Data Source
AI summary
A method implemented by a network element (NE) in a mobile edge cloud (MEC) network, comprising receiving, by the NE, an offloading request message from a client, the offloading request message comprising task-related data describing a task associated with an application executable at the client, determining, by the NE, whether to offload the task to an edge cloud server of a plurality of edge cloud servers distributed within the MEC network based on the task-related data and server data associated with each of the plurality of edge cloud servers, transmitting, by the NE, a response message to the client based on whether the task is offloaded to the edge cloud server.