Dynamic Work Transfer Server for Cloud-Edge Load Balancing
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Solution Overview
Problem
The increasing number of IoT devices connected to networks overloads cloud computing resources, leading to challenges in bandwidth, storage, and CPU computation, necessitating a more efficient distribution of workloads between cloud and edge nodes.
Innovation Solution
A method and server for dynamic work transfer that regularly collects and records network resources across nodes, calculates costs for configuring jobs based on current network conditions, and determines optimal node reconfiguration to balance workload distribution and reduce transfer costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If computation is carried out in a local network (edge computing), then network bandwidth cost and waiting time are reduced, but not all data are suitable for local computation and some data need to be transmitted to cloud server
Solution Approach 1:
The system segments computation tasks into two categories: those suitable for edge computing (local processing) and those requiring cloud computing (remote processing). Edge nodes handle local computation for suitable data, while cloud server handles complex analysis and long-term accessibility requirements, creating a segmented architecture that optimizes both speed and capability.
Solution Approach 2:
The patent introduces a work transfer mechanism that acts as an intermediary between edge nodes and cloud server. This mediator dynamically determines whether to keep work at edge nodes or transfer to cloud server based on data properties and current network conditions, enabling flexible adaptation without fixed architecture constraints.
2Productivity
If network nodes are re-configured based on current job requirements, then overall computation and network transmission performance are facilitated, but dynamic work transfer and reconfiguration complexity increases
Solution Approach 1:
The system implements dynamic node configuration where edge nodes and cloud server can be re-configured based on current job requirements and network conditions. The work transfer mechanism enables flexible assignment of computation tasks to different nodes dynamically, optimizing performance for each specific workload while maintaining adaptability to changing conditions.
Solution Approach 2:
The patent changes the parameter of node configuration from static to dynamic based on job properties and network resource availability. By evaluating data properties, computation requirements, and current network state, the system adjusts which nodes handle which tasks, transforming the configuration state to match optimal performance requirements for each scenario.
3Quantity of substance
If a large number of IoT apparatuses are connected to the network, then more data sources are available for computation, but network resources such as bandwidth, storage space, and CPU computation capability are consumed
Solution Approach 1:
The system applies local quality by enabling edge nodes to process data locally whenever possible, avoiding unnecessary network transmission. By making computation capability available at the local network level rather than centralizing all processing at the cloud server, the system reduces network bandwidth consumption and distributes resource usage more efficiently across the network infrastructure.
Data Source
AI summary
A method and a server for dynamic work transfer are provided. The method includes following steps: regularly collecting and recording network resources of multiple nodes including a cloud node and multiple edge nodes in a network; receiving a request of a first job at a first time point, calculating a cost for configuring the first job to each node according to the network resource of each node at the first time point, and configuring the first job to a first target node; receiving a request of a second job at a second time point, calculating a cost for configuring the second job to each node according to the network resource of each node at the first time point, and determining a second target node suitable for configuring the second job and whether to transfer the first job; and accordingly configuring the second job and maintaining or transferring the first job.