Distribution Manager for Dynamic Platform Server Scaling
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Solution Overview
Problem
Mobile applications face challenges in efficiently managing platform capacity, as different applications have varying usage profiles, leading to potential overload during peak demand and underutilization during off-peak times, necessitating dynamic deployment and deprecation of platforms to maintain efficient resource allocation.
Innovation Solution
A distribution manager system that dynamically selects, instantiates, or deprecates platform servers based on capacity data and usage thresholds, routes mobile applications to optimal servers, and manages load balancing to ensure efficient resource utilization and service availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional platforms are deployed to support peak usage, then service availability is improved, but resource utilization efficiency deteriorates due to underutilization during off-peak times
Solution Approach 1:
The patent implements dynamic platform deployment and deprecation based on real-time usage monitoring. The system automatically scales platform capacity up during peak demand periods and scales down during off-peak periods, transforming the static infrastructure into a dynamic system that adapts to changing load conditions. This resolves the contradiction by ensuring service availability during peaks while maintaining resource efficiency during off-peaks.
Solution Approach 2:
The system changes the operational parameters of platform capacity based on usage patterns. By monitoring usage metrics and adjusting the number of active platforms accordingly, the system optimizes the balance between service availability and resource utilization. This parameter adjustment mechanism allows the system to respond to demand fluctuations without permanent over-provisioning.
2Reliability
If platforms are statically deployed to handle maximum demand, then service reliability is improved, but cost efficiency deteriorates due to persistent underutilization
Solution Approach 1:
The patent replaces static platform deployment with dynamic scaling mechanisms. Instead of maintaining fixed platform capacity to handle maximum demand, the system continuously adjusts platform capacity based on real-time usage monitoring. This dynamic approach ensures service reliability during peak periods while reducing resource consumption and costs during off-peak periods when full capacity is not needed.
Solution Approach 2:
The system implements self-service automation where the platform automatically monitors its own usage metrics and triggers deployment or deprecation actions based on predefined thresholds. This eliminates the need for manual intervention and enables the system to self-optimize its resource allocation, balancing reliability requirements with cost efficiency automatically.
3Measurement precision
If manual platform deployment is used, then control precision is improved, but operational efficiency deteriorates due to delayed response to demand changes
Solution Approach 1:
The patent implements automated feedback loops where usage metrics are continuously monitored and fed back to the platform management system. Based on this feedback, the system automatically triggers platform deployment or deprecation actions when usage thresholds are exceeded or fall below. This closed-loop control mechanism maintains precise control over platform capacity while dramatically improving operational efficiency by eliminating manual intervention delays.
Solution Approach 2:
The system introduces an automated intermediary layer between usage monitoring and platform deployment decisions. This intermediary automatically processes usage data, evaluates threshold conditions, and executes deployment actions without human intervention. This intermediary mechanism preserves the precision of threshold-based control while significantly improving operational response speed and efficiency.
Data Source
AI summary
Embodiments of systems and methods for a distribution manager are presented herein. Specifically, embodiments may receive a request for support for a mobile application and determine a platform server to support the mobile application based on capacity data associated with a set of platform servers in an application table associated with the mobile application. Embodiments may also deliver identification of the platform server over the network, the identification of the platform server comprises connectivity information configured to allow the mobile application to connect to the platform server.