Network Node Energy Optimization via Virtual Flow Integer Programming
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current communication networks face significant energy wastage due to underutilization, as it is challenging to determine which network elements can be powered down without impacting traffic or violating Quality of Service (QoS) constraints, leading to inefficient energy consumption even during low network loads.
Innovation Solution
The method introduces virtual source and destination nodes with virtual flows to optimize energy consumption by determining which network nodes can be powered down while ensuring all throughput demands are met, using Integer Programming to maximize the number of routed flows and minimize active nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network elements are kept running to handle potential traffic, then network reliability is improved, but energy consumption increases
Solution Approach 1:
The system performs preliminary computation offline to determine the optimal set of active network nodes for given traffic matrices. By pre-calculating which nodes can be powered down while maintaining QoS requirements, the system enables energy savings without compromising network reliability during actual operation.
Solution Approach 2:
The invention introduces virtual flows that replicate real traffic patterns. These virtual flows are used in computational models to simulate and evaluate different node activation scenarios, allowing the system to identify energy-efficient configurations without actually disrupting real traffic or network reliability.
2Use of energy by moving object
If more network nodes are powered down to save energy, then energy consumption is reduced, but network performance may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms that monitor actual traffic patterns and QoS requirements. The offline computation adjusts the set of active nodes based on observed traffic matrices, ensuring that power-down decisions are made only when they will not degrade network performance. This feedback loop maintains reliability while achieving energy savings.
Solution Approach 2:
The invention dynamically changes the operational state parameter of network nodes (active/inactive) based on computed optimal configurations. By adjusting which nodes are powered down according to traffic demand patterns and QoS constraints, the system reduces energy consumption while maintaining network performance through intelligent parameter optimization.
3Use of energy by moving object
If complete global knowledge is used to optimize load concentration, then energy efficiency is improved, but computational complexity increases
Solution Approach 1:
The system separates computation into offline and online phases. Complex computations involving complete global knowledge of traffic matrices and QoS requirements are performed offline when computational resources are abundant. During online operation, only simple routing decisions based on pre-computed results are needed, dramatically reducing real-time computational complexity while maintaining energy efficiency.
Solution Approach 2:
The invention segments the optimization problem into manageable components: traffic matrix analysis, QoS constraint evaluation, and node selection. By breaking down the complex global optimization into these segments that can be processed offline, the system reduces the computational burden during actual network operation while still achieving energy-efficient load concentration.
4Reliability
If QoS constraints are strictly enforced to maintain service quality, then network reliability is improved, but flexibility in powering down nodes is reduced
Solution Approach 1:
The system performs preliminary analysis of QoS requirements and traffic patterns offline to identify which nodes can be safely powered down. By pre-determining the minimum set of active nodes needed to satisfy QoS constraints for given traffic matrices, the system maintains service quality while maximizing flexibility in node power management decisions.
Data Source
AI summary
A method for operating a network, wherein said network (1) includes a plurality of network nodes (5), and wherein traffic flows - real flows - are routed from network node (5) to network node (5) within said network (1), is characterized in the steps of introducing in said network (1) a virtual source node (2) and a virtual destination node (3), and creating virtual links between said virtual source node (2) and each of said network nodes (5) as well as between each of said network nodes (5) and said virtual destination node (3), introducing a number k of virtual flows originating at said virtual source node (2) and terminating at said virtual destination node (3) in such a way that a network node (5) can either route a virtual flow or any real flow, formulating an IP (Integer Programming) problem that aims at maximizing the number of routed flows - real flows and virtual flows - and solving said problem wherein throughput demands are taken into account, and those network nodes (5) which are occupied with routing a virtual flow are powered down.