Edge Device Task Data Transfer via Coordinator
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Solution Overview
Problem
Current edge computing systems face challenges in ensuring efficient task data transfer between edge devices, particularly when containers on these devices are overloaded or crashed, leading to potential data loss and increased latency, which affects the overall performance and reliability of edge computing operations.
Innovation Solution
A method and system for dynamically transferring task data from one edge device to another within a cluster, utilizing a task data transferring coordinator that identifies error statuses of containers, selects suitable edge devices based on resource availability, and coordinates the transfer of task data from one local storage to another, ensuring seamless task continuation even if the original container fails.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If task data is stored locally on edge devices, then data access speed is improved, but data loss risk increases when containers crash
Solution Approach 1:
The system performs preliminary actions by maintaining replicas of task data on other edge devices before container failures occur. When a container crashes, the task data can be immediately retrieved from pre-positioned replicas, eliminating both data loss risk and retrieval latency.
Solution Approach 2:
The system introduces a coordination mechanism that acts as an intermediary between local storage and task execution. This coordinator monitors container health and manages data replication across edge devices, enabling seamless failover without direct access to primary storage.
2Reliability
If task data is transferred between edge devices, then fault tolerance is improved, but transfer latency increases
Solution Approach 1:
Task data is replicated to multiple edge devices in advance before failures occur. This preliminary replication ensures that when container failures happen, the system can immediately switch to using pre-positioned data replicas without incurring transfer latency.
Solution Approach 2:
The system combines local storage benefits with distributed redundancy by maintaining data both locally and on peer edge devices. This merging approach provides fault tolerance while keeping data access fast through local availability.
3Adaptability or versatility
If containers are used for task execution, then resource isolation is improved, but system complexity increases for managing container failures
Solution Approach 1:
The system implements self-service mechanisms where edge devices automatically detect container failures, retrieve backup task data from other devices, and resume execution without human intervention. This automation reduces the complexity burden on operators while maintaining container-based isolation.
Solution Approach 2:
The coordination mechanism continuously monitors container health and provides feedback about failure states. This feedback loop enables automatic triggering of data retrieval and task resumption processes, simplifying failure management through reactive automation.
Data Source
AI summary
Edge device task management by receiving an indicator corresponding to a first container running a task on a first edge device of a cluster of edge devices, wherein the indicator indicates an error status of the first container, and wherein task data of the task is stored in a first local storage of the first edge device, selecting a second edge device from the cluster of edge devices, wherein a second container on the second edge device is to run the task, instructing the first and second edge devices to transfer the task data from the first local storage of the first edge device to a second local storage of the second edge device, and in response to receiving a notification that indicates the task data has been transferred from the first local storage to the second local storage, sending the task to the second container.