Dynamic Content Steering via Pre-calculated Server Rankings
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Solution Overview
Problem
Existing media distribution systems perform numerous server lookups for each media item request, leading to high resource consumption and inefficiency, as they dynamically determine the best server for each request without pre-calculating server rankings or optimizing request distribution.
Innovation Solution
Pre-calculating server node rankings based on media item provisioning factors and using a directed distribution algorithm to steer media item requests to optimally ranked server nodes, reducing the need for frequent lookups and improving resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dynamic server lookup is performed for each media item request, then optimal server selection is achieved, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent pre-calculates and stores server node rankings before requests arrive, performing the optimal server selection work in advance. When a request comes in, the system simply retrieves the pre-computed ranking rather than performing a full lookup, thus maintaining optimal server selection while dramatically reducing processing time.
2Productivity
If random distribution algorithm is used to spread requests, then server utilization is improved, but request routing efficiency decreases due to constant algorithm adjustments
Solution Approach 1:
The system pre-calculates server node rankings based on current server capabilities and request patterns before requests arrive. This pre-computed ranking information is then used to route requests efficiently, eliminating the need for constant algorithm adjustments while maintaining good server utilization through directed distribution.
3Measurement precision
If multiple server lookups are performed per request, then best server identification is achieved, but network bandwidth consumption increases
Solution Approach 1:
The patent pre-computes and caches server node rankings that identify the optimal server for each electronic device based on multiple factors (latency, proximity, bit rate, content availability). When a request arrives, the system retrieves the pre-determined optimal server from the cache rather than performing multiple lookups, thus maintaining accurate server identification while significantly reducing network bandwidth consumption.
Data Source
AI summary
The disclosed computer-implemented method includes determining that incoming media item requests are to be skewed from a random distribution among server nodes, using a random distribution algorithm, to a directed distribution among the server nodes. The method then includes identifying, in a loading assignment, which media items are to be loaded onto specific server nodes to produce the directed distribution of media item requests. The method next includes preloading the identified media items onto the server nodes according to the loading assignment and receiving media item requests for the preloaded media items. The method then includes routing the received media item requests to the server nodes using the random distribution algorithm, where the random distribution algorithm is skewed to the directed distribution based on the preloading of the media items according to the identified loading assignment. Various other methods, systems, and computer-readable media are also disclosed.