Streaming Platform Traffic Spike Migration via Predictive Pre-provisioning
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Solution Overview
Problem
High demand for newly released digital media content can overwhelm streaming platforms, leading to service failures, degraded performance, and temporary outages, causing user frustration and disaffection.
Innovation Solution
A system and method for dynamically migrating traffic spikes using a trained machine learning model to predict user demand and pre-provision authentication and playback data for likely users, prioritizing geo-location and user history to manage resource allocation and reduce peak load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the streaming platform allows concurrent access to newly released content for all users, then user satisfaction is improved, but platform resources become overloaded leading to service failures
Solution Approach 1:
The system performs preliminary actions by predicting which users are likely to access newly released content and pre-provisioning authentication data and playback information for those users before the content release. This advance preparation ensures that when the content is released, these pre-identified users can access it immediately without contributing to peak traffic spikes, thereby maintaining platform reliability while satisfying user demand.
2Reliability
If the platform rejects user requests when traffic exceeds capacity, then service stability is maintained, but user experience deteriorates
Solution Approach 1:
The system identifies and pre-provisions authentication data for users likely to access new content before release, ensuring these users can access content immediately without experiencing rejection or delays. This preliminary action maintains service stability by filtering out predictable demand while preserving excellent user experience for the pre-identified user group.
3Reliability
If the platform shuts down entirely during high traffic spikes, then resource protection is achieved, but service continuity is lost
Solution Approach 1:
By predicting user demand and pre-provisioning authentication data before content release, the system removes predictable traffic spikes in advance. This prevents the need for complete platform shutdowns while protecting resources, as the pre-provisioned users can access content without generating peak traffic loads that would otherwise force service outages.
4Reliability
If the platform recovers from service failures after several hours, then system stability is restored, but user frustration increases
Solution Approach 1:
The system performs demand prediction and authentication data pre-provisioning before content release, eliminating the need for service failures and lengthy recovery periods. Users experience no interruption or frustration because their access is guaranteed in advance through pre-provisioning, completely avoiding the recovery time loss that would otherwise occur.
Data Source
AI summary
A system includes a computing platform having processing hardware and a memory storing software code. The processing hardware executes the software code to receive content data identifying new content and a future release date for the new content, provide a prediction identifying a subset of the users likely to request the new content upon its release, and prioritize, based on the prediction, a schedule for pre-provisioning authentication data for accessing the new content to the subset of users. The processing hardware further executes the software code to pre-provision, using the prioritized schedule, the authentication data to the subset of users prior to the release of the new content.