Transaction Capping in Distributed Service Integrators
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Solution Overview
Problem
In stateful conversations within distributed computing environments, accurately balancing the rate of incoming transactions across multiple servers is complex due to inhomogeneous traffic patterns, leading to potential overloading and instability in traffic management, which can impact Quality of Service (QoS) and Service Level Agreements (SLA) for clients.
Innovation Solution
A method involving a server allocation engine, monitoring engine, broadcasting engine, limit computation engine, and transaction capping engine to dynamically allocate and cap incoming transactions across conversation servers, ensuring each server handles a maximum defined transaction rate based on global and local traffic values, preventing overloading and maintaining QoS.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If stateful conversations are balanced equally among multiple servers, then conversation distribution is simplified, but transaction rate balancing becomes extremely complex due to inhomogeneous traffic patterns
Solution Approach 1:
The patent segments the transaction rate capping problem into per-server local limits rather than a single global limit. Each server independently calculates and enforces its own transaction rate cap based on its specific traffic conditions, transforming the complex centralized balancing problem into multiple simpler decentralized decisions.
Solution Approach 2:
The patent implements dynamic adjustment of local transaction rate limits based on real-time monitoring of incoming traffic. The limits are not fixed but adapt to changing traffic patterns, allowing the system to respond to inhomogeneous traffic distributions as they occur rather than relying on static balancing rules.
2Device complexity
If a single global transaction limit is applied to all servers, then implementation is simple, but it cannot account for inhomogeneous traffic patterns causing some servers to be overloaded while others are underutilized
Solution Approach 1:
The patent transitions from a uniform global limit to differentiated local limits for each server. Each server receives a customized transaction rate cap tailored to its specific traffic characteristics and capacity, allowing the system to accommodate inhomogeneous traffic patterns while maintaining overall reliability.
Solution Approach 2:
The patent implements a feedback mechanism where servers monitor their own incoming traffic and report to a central controller. Based on this feedback, the system dynamically adjusts local transaction limits, creating a closed-loop control system that adapts to changing traffic conditions and maintains reliable load distribution.
3Productivity
If transaction rate capping is not implemented, then clients can send unlimited transactions ensuring maximum productivity, but the traffic management module becomes overloaded affecting QoS and SLA
Solution Approach 1:
The patent dynamically changes the transaction rate parameter (limit) based on system conditions and client characteristics. Rather than using a fixed limit, the system adjusts the allowed transaction rate per server based on real-time traffic monitoring, ensuring that clients receive sufficient resources to meet SLA requirements while preventing overloading of the traffic management module.
Data Source
AI summary
The present invention relates to a method and a system for capping incoming transactions in inbound stateful conversations established between a client and a plurality of conversation servers of a service integrator. For each client, a group of conversation servers is allocated to handle the inbound stateful conversations initiated. Each conversation server in the group broadcasts a local traffic value periodically to the other conversation servers in the group. Each conversation server in the group calculates a transaction capping limit based on a client global transaction capping limit and the broadcasted local traffic values from the other conversation servers in the group. Each conversation server limits the rate of incoming transactions received by the client when exceeding the local transaction capping limit.


