Networked Resource Provisioning System for Telecommunications
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Solution Overview
Problem
Current resource allocation methods in telecommunications networks face inefficiencies due to peak demand congestion and underutilization during off-peak periods, leading to high operational costs and service blockages, as usage-based pricing does not effectively manage resource distribution across time.
Innovation Solution
A resource provisioning system that dynamically schedules resource allocation based on baseline capacity, time-shifting elastic requests to off-peak periods, and adjusts pricing to optimize resource utilization, ensuring minimum capacity is maintained during peak and off-peak times, using a forecasting engine and decision management module to manage resource allocation and pricing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resources are made available to mitigate congestion during peak periods, then service quality during peak periods is improved, but resource utilization during off-peak periods decreases
Solution Approach 1:
The system dynamically adjusts resource allocation based on time-dependent pricing signals. Resources are allocated flexibly between peak and off-peak periods through automated scheduling that responds to changing demand conditions, allowing the same resources to serve different customer groups at different times rather than being statically dedicated to peak periods
Solution Approach 2:
The system changes the pricing parameter over time to influence demand distribution. By implementing time-dependent pricing with lower rates during off-peak periods, the system modifies the economic parameter that drives resource allocation decisions, encouraging elastic customers to shift their demand to off-peak times and thereby increasing resource utilization during those periods
2Productivity
If time-dependent pricing is implemented to incentivize off-peak usage, then resource utilization during off-peak periods increases, but system complexity increases
Solution Approach 1:
The system employs automated scheduling algorithms that self-manage the complex task of dynamic resource allocation. The scheduling system automatically processes pricing signals, identifies elastic customers, and reallocates resources without manual intervention, thereby managing system complexity through automation rather than simplification
Solution Approach 2:
The system uses feedback loops where pricing decisions are based on real-time resource utilization data and demand patterns. The scheduling system continuously monitors resource usage and adjusts pricing and allocation decisions accordingly, managing complexity through closed-loop control rather than simplified open-loop approaches
3Productivity
If elastic requests are time-shifted to off-peak periods, then overall resource utilization increases, but response time for elastic requests increases
Solution Approach 1:
The system applies time-shifting only to elastic (delay-tolerant) requests rather than all requests. By selectively identifying and shifting only those requests that can tolerate delays, the system achieves partial action that improves overall resource utilization without excessively impacting response times for time-critical operations
Solution Approach 2:
The system performs preliminary identification and classification of elastic requests before implementing time-shifting. By pre-identifying which requests are suitable for shifting based on their elasticity characteristics, the system prepares the groundwork for efficient resource allocation while minimizing unnecessary delays
Data Source
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AI summary
In response to requests for their use of the resources by the applications, resources are first allocated to "inelastic" requests specified for performance at a specific time slot. A baseline allocation of resources is then determined which is the minimum amount of capacity that has to be used for efficient operation of the resources required to meet the requests. This baseline will include some additional capacity as the minimum efficient capacity is less than the maximum capacity. "Elastic" requests, specified for performance within a predetermined time range comprising two or more of the set of time slots, are then allocated so as to prioritise the use of this spare capacity, bringing actual usage up to at least the baseline allocation of resources. If further capacity is required to meet all the inelastic requests, further capacity is allocated and the baseline redefined to include it.