Cloud Print System Dynamic Instance Scaling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cloud resources are not properly utilized, leading to wastage and increased costs due to inefficient allocation and usage, particularly in scenarios where devices with low print speeds request image processing, resulting in stacked unprinted content in the cloud.
Innovation Solution
A print system that includes a server computer group with a request receiver, backend processor, and queue service, which generates queue messages and manages instances dynamically based on device performance and queue lengths to optimize resource allocation and usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud resources are allocated to devices with low print speed, then image processing can be performed, but cloud resources are wasted due to stacking of processed content that cannot be printed in time
Solution Approach 1:
The system dynamically adjusts the number of cloud instance resources based on real-time queue lengths and device print speeds. When a device with low print speed is detected, the system reduces or suspends cloud resource allocation to prevent wastage of processed content that cannot be printed in time. This dynamic adaptation resolves the contradiction by making resource allocation flexible rather than static.
Solution Approach 2:
The system implements feedback mechanisms where the cloud server receives information about device print speeds and queue statuses. Based on this feedback, the server adjusts resource allocation decisions. For example, when the printer queue length exceeds a threshold or print speed is low, the system reduces cloud resource allocation, thereby preventing wastage while maintaining productivity where appropriate.
2Adaptability or versatility
If multiple devices are connected to the cloud, then service coverage is improved, but resource wastage increases without proper usage rules
Solution Approach 1:
The system applies different resource allocation strategies to different devices based on their specific characteristics such as print speed, queue length, and usage patterns. Instead of uniform resource allocation, each device receives customized resource allocation decisions. This local quality approach allows multiple devices to be supported while preventing wastage by tailoring resource allocation to each device's actual needs and capabilities.
3Productivity
If cloud resources are allocated based on high demand, then processing capacity is improved, but costs increase due to pay-as-you-use pricing
Solution Approach 1:
The system dynamically scales cloud resource allocation based on real-time demand and device performance metrics. Rather than allocating resources statically or always at maximum capacity, the system adjusts instance numbers up or down based on actual processing needs, queue lengths, and device print speeds. This dynamic approach maintains high processing capacity when needed while reducing resource consumption and costs during periods of lower demand or when device performance limits utilization.
Data Source
Figure 1
Figure 2A~2B
Figure 3
AI summary
The present invention provides an information processing system that uses a computer resource more properly. The number of print services is adjusted depending on whether the number of jobs acquired by an acquiring unit is equal to or larger than a predetermined value for the number of jobs set in identification information and depending on whether the number of print services that process the jobs stored in a queue is equal to or smaller than a predetermined value for the number of print services set in the identification information.