Task Offloading Control via Cooperative Multi-Agent Reinforcement Learning
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Solution Overview
Problem
Existing studies on task offloading in cloud and edge computing do not adequately consider multiple cloud servers, network bandwidth, and backbone network topology, leading to inefficiencies and potential congestion.
Innovation Solution
A control apparatus that observes task information and network usage status, calculates the optimal node for task offloading based on these observations, and transfers the task to that node, using cooperative multi-agent deep reinforcement learning to optimize task allocation across multiple cloud and edge servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If tasks are offloaded to cloud servers located far away from end devices, then computing resource availability is improved, but communication delay increases
Solution Approach 1:
The system segments the centralized cloud computing architecture into a distributed cloud-edge computing system. Edge servers are deployed at multiple locations closer to end devices, dividing the computing resource pool into distributed segments. This allows tasks to be offloaded to nearby edge servers, reducing communication delay while maintaining access to cloud computing resources.
Solution Approach 2:
The system introduces a new spatial dimension by deploying edge servers at multiple geographic locations between end devices and central cloud servers. This creates a multi-dimensional offloading architecture where tasks can be routed to edge servers in different spatial locations, optimizing the balance between computing resource availability and communication delay.
2Quantity of substance
If multiple cloud servers are used for task offloading, then computing capacity is improved, but system complexity increases
Solution Approach 1:
The system merges the functionality of multiple cloud servers and edge servers into a unified cloud-edge computing system. The cooperative multi-agent deep reinforcement learning framework integrates resource management across multiple servers, allowing them to work together as a coordinated system rather than independent entities, thus managing complexity while maintaining expanded computing capacity.
Solution Approach 2:
The system creates universal agent models that can operate across different cloud servers and edge servers. Each agent is designed with multi-functional capabilities to handle various task types and make decisions based on shared learning, reducing the complexity that would arise from having specialized systems for each server.
3Use of energy by stationary object
If independent learning is used for each agent, then learning cost is reduced, but selfish actions occur leading to server overload
Solution Approach 1:
The system implements feedback mechanisms where agents share their learned experiences and observations with other agents. Through this feedback loop, agents become aware of the overall system state and can adjust their decisions to avoid overload conditions, maintaining system stability while keeping individual learning costs low.
Solution Approach 2:
The system introduces a coordination mechanism as an intermediary that mediates between independent agents. This intermediary facilitates cooperative behavior by enabling agents to consider the impact of their actions on other servers, preventing selfish overload while preserving the benefits of independent learning.
Data Source
AI summary
The present disclosure aims to improve the efficiency of task offloading in consideration of network usage statuses such as network topology and bandwidth. To this end, the present disclosure provides a control apparatus that controls allocation of a task to a physical network constructed and modeled by nodes having edge nodes and cloud nodes, the control apparatus including: an observation unit that observes task information regarding the task requested by an end device and network usage information indicating a usage status of the physical network; a calculation unit that calculates an optimal specific node for offloading the task based on the observation result of the observation unit; and a transfer unit that transfers the task to the specific node.