Cloud Resource Manager Adjusting Media Processing Speed
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Solution Overview
Problem
Cloud computing introduces delays in processing real-time workflows due to path delays, sequential software processes, and transmission delays, which can negatively impact streaming applications like media production, where timely assembly of media packages is critical.
Innovation Solution
A method for non-linear management of real-time sequential data in cloud instances using time constraints, involving the receipt of component media flows, identification of processes, adjustment of performance to meet time constraints, and orchestration through a media production facility and cloud network facility with a resource manager to ensure timely assembly and transmission of media packages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud computing is used to process real-time workflows in parallel across multiple machines, then processing speed and productivity are improved, but path delays, processing delays, and transmission delays are introduced that worsen real-time performance
Solution Approach 1:
The system performs preliminary actions by pre-calculating estimated processing times for all procedures in the workflow before execution. Time constraints are established in advance for each procedure and the overall workflow. This allows the system to proactively identify potential timing issues and adjust resource allocation or procedure sequencing before delays occur, rather than reacting after delays have already impacted real-time performance.
Solution Approach 2:
The system dynamically adjusts the execution of procedures based on real-time conditions. The resource manager can modify the degree to which procedures are performed (e.g., reducing processing depth, skipping non-critical steps, or parallelizing tasks) to ensure the overall workflow completes within time constraints. This dynamic adjustment allows the system to maintain real-time performance while still utilizing cloud computing resources for parallel processing.
2Reliability
If sequential software processes are used in the cloud environment, then processing reliability is improved, but processing delays occur that worsen real-time workflow performance
Solution Approach 1:
The workflow is segmented into multiple independent procedures, each with its own time constraint. This allows the resource manager to identify which specific procedures are causing bottlenecks and adjust only those procedures rather than reconfiguring the entire sequential process. Critical procedures can be executed with higher priority or additional resources while non-critical procedures can be delayed or simplified.
Solution Approach 2:
The system changes parameters of procedure execution by adjusting the degree of performance for each procedure. This can include modifying processing quality levels, changing execution priorities, or adjusting resource allocation parameters. By dynamically changing these parameters, the system can balance reliability requirements with speed requirements for different procedures in the workflow.
3Productivity
If data is transmitted to remote cloud facilities and back, then processing capacity is improved, but path delays and transmission delays are introduced that worsen real-time performance
Solution Approach 1:
The system performs preliminary actions by establishing time constraints for data transmission and processing before the actual workflow execution. Estimated times are calculated for all procedures including data transmission paths. This allows the system to pre-identify long transmission paths and potentially cache data locally, use edge computing resources, or pre-stage data closer to the processing location to reduce actual transmission delays during real-time execution.
Data Source
AI summary
Non-linear management of real time sequential data in cloud instances via time constraints is provided by: receiving, at a cloud network facility from a media production facility, a selection of component media flows for assembly into an assembled media package; identifying a time constraint for assembling component media flows into the assembled media package; identifying processes to perform with respect to the component media flows during assembly of the assembled media package; in response to an estimated time to perform the processes with respect to the component media flows exceeding the time constraint, adjusting performance of the processes with respect to the component media flows to increase a speed of performance; and performing, in the cloud network facility, the processes with respect to the component media flows as adjusted to produce the assembled media package.