Layered Network Control Architecture for Scalable Traffic Optimization
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Solution Overview
Problem
Existing data network management systems face challenges in optimizing network traffic and resource allocation due to limitations in scalability, handling noise and transient behaviors, and accommodating complex cost functions, especially in large-scale distributed systems.
Innovation Solution
A layered architecture framework combining Layering as Optimization (LAO) and distributed optimal control methodologies, featuring a tracking layer and a planning layer, where the tracking layer uses feedback control to track a reference trajectory generated by the planning layer, optimizing network utility and penalizing undesirable transient behaviors while accounting for system dynamics and information sharing constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If centralized control is used to manage network traffic, then network optimization and resource allocation improve, but system complexity and scalability deteriorate
Solution Approach 1:
The patent divides the centralized control system into multiple hierarchical layers (application layer, control layer, data layer). Each layer handles specific control functions independently, allowing the system to maintain optimization capabilities while reducing overall complexity through functional segmentation. The control plane is separated from the data plane, with controllers at different levels managing different aspects of network traffic.
Solution Approach 2:
The patent introduces a vertical dimensional structure by stacking multiple control layers above the data plane. This layered architecture transforms the flat centralized control into a multi-dimensional hierarchy, where each layer operates at a different abstraction level. The application layer handles high-level policy decisions, the control layer manages medium-term traffic optimization, and the data layer executes low-level packet forwarding, enabling scalability through dimensional expansion.
2Measurement precision
If detailed tracking of network state is implemented, then control precision improves, but information processing overhead and system response time deteriorate
Solution Approach 1:
The patent implements dynamic tracking where the level of detail in network state monitoring adapts based on current network conditions and control objectives. Controllers adjust their monitoring granularity dynamically, tracking only relevant state variables at each hierarchical level. This allows precise tracking when needed while reducing processing overhead during stable periods, maintaining response time performance.
Solution Approach 2:
The patent applies partial tracking by having each control layer monitor only the subset of network state variables relevant to its specific functions. The application layer tracks high-level traffic patterns, the control layer monitors medium-term queue states, and the data layer observes immediate packet-level conditions. This partial observation strategy achieves sufficient control precision without the excessive processing burden of complete state tracking.
3Adaptability or versatility
If multiple controllers are distributed across the network, then scalability improves, but coordination complexity and information sharing constraints worsen
Solution Approach 1:
The patent segments the distributed controller system into hierarchical layers where each layer has clearly defined responsibilities and information requirements. Controllers at the application layer handle policy-level coordination, control layer controllers manage traffic optimization coordination, and data layer controllers handle packet forwarding coordination. This segmentation reduces overall coordination complexity by localizing coordination requirements to specific layers.
Solution Approach 2:
The patent introduces intermediary controllers at each hierarchical level that mediate information exchange between controllers at different layers. These intermediary controllers aggregate information from lower layers, process it according to layer-specific rules, and forward relevant information upward. This intermediary structure simplifies coordination by filtering and structuring information flow, reducing the complexity of direct peer-to-peer coordination between all distributed controllers.
4Speed
If real-time control actions are computed, then network responsiveness improves, but computational complexity and energy consumption deteriorate
Solution Approach 1:
The patent implements dynamic control computation where the frequency and depth of control calculations adapt based on network conditions. During stable periods, controllers reduce computation frequency and use simplified models. During transient conditions or when performance degradation is detected, controllers increase computation frequency and apply more sophisticated control algorithms. This dynamic adaptation maintains responsive control when needed while reducing energy consumption during normal operation.
Solution Approach 2:
The patent applies partial control computation by having each hierarchical layer compute only the control actions necessary for its specific functions. The application layer computes policy-level control parameters, the control layer computes traffic optimization parameters, and the data layer computes packet forwarding decisions. This partial computation strategy achieves sufficient responsiveness for each control objective without the excessive computational and energy costs of computing all possible control actions at all layers.
Data Source
AI summary
Data network components and frameworks for the control and optimization of network traffic using layered architectures, according to embodiments of the invention, are disclosed. In one embodiment, a data network comprises a switch, a data link, a tracking processor, and a planning processor. The planning processor is configured to generate a reference trajectory by receiving a local system state from the tracking processor, calculating a reference trajectory by solving a planning problem, and sending the reference trajectory to the tracking processor. The tracking processor is configured to track the reference trajectory by determining a set of states of the switch over a time interval, sending a local system state to the planning processor, receiving the reference trajectory from the planning processor, computing a control action by solving a tracking problem based on the reference trajectory, determining an instruction based on the control action, and sending the instruction to the switch.


