Self-Organized SBC Cluster for Scalable SIP Load Balancing
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Solution Overview
Problem
Current SIP load balancing methods across clusters of Session Border Controllers (SBCs) face challenges in scalability, uneven load distribution, and compatibility with standards, particularly in dynamic environments and virtualized setups, leading to inefficiencies and hot-spotting.
Innovation Solution
A self-organized cluster of SBCs dynamically shares session transaction load state information to perform probabilistic retargeting of SIP transactions, using a mesh network for load balancing, which includes both count-based and rate-based slack capacity calculations to optimize resource utilization and distribute load effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If simplistic load balancing schemes (such as multiple routes in the peers) are used, then the solution is easy to implement, but it cannot scale to support hundreds or thousands of SBC entities
Solution Approach 1:
The patent implements feedback mechanisms where SBCs exchange load state information and use this feedback to dynamically adjust routing decisions. The load balancing entity receives feedback about SBC availability and load conditions, then uses this information to make informed routing decisions that scale effectively across large clusters of SBCs
Solution Approach 2:
The system dynamically adapts its load balancing behavior based on real-time conditions. SBCs can be dynamically added or removed from the cluster, and the load balancing mechanism responds by updating routing tables and selecting optimal paths based on current availability and load state, enabling scalability without manual reconfiguration
2Device complexity
If initial load balancing schemes not coupled to actual loading are used, then the implementation is simpler, but it results in uneven distribution of load within the SBC cluster
Solution Approach 1:
The load balancing entity receives feedback about actual SBC loading conditions and uses this information to adjust routing decisions. This feedback loop ensures that load is distributed uniformly across the cluster by directing traffic away from overloaded SBCs and toward underutilized ones, while the system remains relatively simple to implement
Solution Approach 2:
The system performs preliminary actions by proactively monitoring and exchanging load state information between SBCs before load imbalance occurs. This allows the load balancing mechanism to preemptively adjust routing decisions to maintain uniform load distribution, rather than reacting after imbalance has occurred
3Productivity
If proprietary behavior is required for load balancing, then the load balancing can be optimized for specific scenarios, but it prevents interoperability with other vendors' equipment
Solution Approach 1:
The patent implements universal load balancing mechanisms that work across different vendor equipment and network configurations. The load balancing entity uses standard SIP protocols and generic routing mechanisms that can be applied universally, allowing efficient load balancing without requiring proprietary behavior or vendor-specific implementations
Solution Approach 2:
The load balancing entity acts as an intermediary between clients and SBCs, using standard SIP routing mechanisms to distribute load. This intermediary approach allows the system to optimize load balancing efficiency while maintaining compatibility with various vendors' equipment through standardized protocol interactions
4Quantity of substance
If the number of SBCs in the cluster increases to meet high capacity requirements, then the system can support more sessions and traffic, but the performance may degrade due to increased complexity
Solution Approach 1:
The system segments the load balancing function by having each SBC independently manage its own load state and participate in a distributed feedback mechanism. This segmentation allows the system to scale to many SBCs without central management complexity, as each node operates autonomously while contributing to the overall load balancing effort
Solution Approach 2:
The feedback mechanism allows the system to manage large clusters efficiently by using distributed information exchange. Each SBC provides feedback about its load state, and the load balancing entity uses this information to make routing decisions, avoiding the need for complex centralized management while maintaining performance as the number of SBCs increases
Data Source
AI summary
Methods, apparatus and systems for load balancing Session Initiation Protocol session transactions among a self-organized cluster of SIP processing devices. An exemplary method embodiment includes the steps of dynamically forming a load balancing cluster of SIP processing devices from a plurality of SIP processing devices, said cluster of SIP processing devices being self-organized; dynamically building a communications network, by said cluster of SIP processing devices, for distributing session transaction load state information among the SIP processing devices in the cluster; and each of the SIP processing devices of the cluster asynchronously determining session transaction load state information on a recurring basis reflecting its current session transaction load state. In some embodiments, the SIP processing devices are session border controllers.