Medical Image Processing System Load Prediction
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Solution Overview
Problem
Conventional medical image processing systems face challenges in predicting and managing non-steady load conditions, leading to inefficient resource utilization and increased costs due to uncertainties in the number of reserved scans and varying image data volumes, resulting in suboptimal server allocation and processing capacity utilization.
Innovation Solution
A medical image processing system comprising a reservation manager, throughput calculator, and request unit that dynamically allocates image processing tasks across multiple servers based on real-time throughput calculations and load predictions, allowing for efficient distribution of processing loads during peak and non-peak periods by identifying and offloading tasks to supporting servers when the main server's capacity is exceeded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If the number of servers is increased to handle peak load, then the processing capacity is improved, but the cost and resource waste increase
Solution Approach 1:
The system dynamically determines the number of required servers based on real-time load predictions rather than statically allocating fixed resources. The server allocation is adjusted according to actual demand patterns, allowing the system to scale up during peak periods and scale down during off-peak periods, thereby optimizing the balance between processing capacity and resource utilization.
Solution Approach 2:
The system performs preliminary actions by predicting future load patterns and pre-allocating servers accordingly. By analyzing historical data and forecasting future demand, the system can prepare the appropriate number of servers in advance, avoiding both over-provisioning and under-provisioning, thus optimizing resource allocation before peak loads occur.
2Reliability
If more servers are installed to accommodate uncertain non-ready state load, then the reliability is improved, but the cost benefit decreases
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring actual load patterns and comparing them against predictions. This feedback loop allows the system to refine its forecasting accuracy over time and adjust server allocation strategies accordingly, ensuring that servers are maintained only when necessary based on actual demand rather than uncertain predictions.
Solution Approach 2:
The system changes operational parameters dynamically by adjusting server allocation based on actual load conditions rather than fixed predictions. By monitoring real-time parameters such as image processing throughput, data volume, and scan patterns, the system can adapt its resource allocation to match actual needs, thereby maintaining reliability while optimizing cost efficiency.
3Productivity
If image processing is shared between multiple servers, then the throughput is improved, but the system complexity increases
Solution Approach 1:
The system segments the image processing workload and divides it among multiple servers based on predicted load patterns. By segmenting tasks according to time periods and demand forecasts, the system can distribute processing loads efficiently while maintaining manageable complexity through automated task allocation and server management.
Data Source
AI summary
A medical image processing system in which the generation of a non-steady load is made predictable based on the reservation of image processes, includes a reservation manager to manage the process commencement time and the process termination time of the reserved image processing and the amount of medical image data, which is the subject of image processing, a throughput calculating unit to calculate the throughput processed for each predetermined time width in the image processing regarding each image process carried out in the first server, and an analyzing unit to calculate the total throughput of the image processing carried out in parallel for each time width. When the calculated total is more than the predetermined throughput, the analyzing unit specifies at least one from among all image processes carried out in the time width such that the total throughput of a first server becomes less than the predetermined throughput.