Medical Task Flow Load Balancing via Intermediary Server Selection
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Solution Overview
Problem
Existing load balancing methods in medical engineering are not tailored to specific medical task flows and do not effectively account for server-specific features, leading to inefficiencies and potential misallocation of tasks.
Innovation Solution
A method for load-balanced allocation of medical task flows that records configurable request conditions and load information from servers, identifying a target application server that meets these conditions, allowing for automatic and efficient task flow distribution based on medical task flow requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If software-based load balancing (e.g., Microsoft NLB) is used to distribute tasks across servers, then task distribution is achieved, but data traffic increases significantly due to packet forwarding to all servers
Solution Approach 1:
The patent introduces a load balancer as an intermediary component that sits between clients and the server farm. This load balancer receives client requests, evaluates server states using multiple algorithms, and forwards requests only to the most suitable server. This intermediary approach eliminates the need for all servers to receive and process every packet, thereby reducing overall data traffic while maintaining effective task distribution.
2Loss of energy
If hardware-based load balancing (e.g., NitroSwitch) is used to forward packets only to the target server, then data traffic is reduced, but the system cannot adapt to specific medical task flow requirements and server features
Solution Approach 1:
The patent implements dynamic load balancing by continuously monitoring server states and adapting algorithm selection based on current conditions. The system evaluates multiple load balancing algorithms (round-robin, least-connections, weighted-random, etc.) and selects the most appropriate one based on real-time server performance metrics and task requirements. This dynamic approach allows the system to maintain low data traffic while adapting to specific medical task flow requirements and server characteristics.
Solution Approach 2:
The system changes operational parameters by adjusting which load balancing algorithm is active based on server load, task type, and performance metrics. Different algorithms are applied under different conditions—for example, using round-robin for evenly distributed loads or least-connections for high-traffic scenarios. This parameter change strategy enables the system to optimize both data traffic reduction and medical task adaptability.
3Ease of manufacture
If existing load balancing algorithms are used with fixed hardware-based criteria, then implementation is simple, but the criteria do not match specific medical engineering task flow characteristics
Solution Approach 1:
The patent creates a universal load balancing system that can handle multiple types of medical tasks and server configurations through a single platform. The system incorporates multiple load balancing algorithms and evaluation criteria that can be applied across different medical imaging modalities (MRI, CT, PET, etc.) and task types (diagnosis, treatment planning, research). This multi-functional approach maintains implementation simplicity while achieving broad adaptability to medical-specific requirements.
Data Source
AI summary
A method, a system and a computer program are disclosed for the load-balanced allocation of computer-aided medical task flows on at least one application server of a server farm. In at least one embodiment, request conditions and load information are configured in a configuration phase. The request conditions are then recorded in a load balancing phase. In addition the load information is recorded via load calculation agents. A load balancing service can then calculate a target application server, which according to the load information determined meets all recorded request conditions.