gRPC Client Detection Using Queue Staleness Thresholds
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Solution Overview
Problem
gRPC clients that become unresponsive due to network issues are not detected promptly, leading to accumulation of outbound messages and potential out-of-memory conditions on the server, which can be exploited for denial-of-service attacks and negatively impact other clients.
Innovation Solution
Implementing separate queues for each client and using time thresholds to assess data staleness, allowing only fresh data entries and purging stale data to prevent backlog and resource exhaustion, with alarms for timely client intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If gRPC clients maintain persistent connections with TCP keep-alive messages, then connection stability is improved, but unresponsive clients cannot be detected promptly leading to message accumulation
Solution Approach 1:
The patent introduces an intermediary detection mechanism that operates independently from the persistent gRPC connection. This mediator monitors client responsiveness by attempting to send messages and track acknowledgments, separate from the main data communication channel. When the intermediary detects unresponsiveness through missing acknowledgments, it triggers client isolation without disrupting the stable TCP connection infrastructure.
2Loss of information
If the server continues sending messages to unresponsive clients, then message delivery completeness is improved, but server memory consumption increases leading to out-of-memory conditions
Solution Approach 1:
The system implements self-service through automatic client detection and isolation mechanisms. The server monitors acknowledgment patterns and autonomously identifies unresponsive clients, then automatically isolates them by stopping message delivery. This self-service approach eliminates the need for manual intervention while preventing memory exhaustion from accumulating messages to dead endpoints.
Solution Approach 2:
The patent implements feedback loops where the server sends messages and waits for acknowledgments. When acknowledgments are not received within expected timeframes, the feedback mechanism triggers detection of unresponsive clients. This continuous feedback cycle allows the system to adapt message delivery based on actual client responsiveness, stopping delivery to unresponsive clients before memory exhaustion occurs.
3Productivity
If the server isolates unresponsive clients immediately, then system performance is improved, but false positives may occur disrupting legitimate clients
Solution Approach 1:
The patent applies preliminary action by implementing a detection phase before isolation. The system first identifies potentially unresponsive clients through acknowledgment monitoring, then applies isolation only after confirmation. This preliminary detection step reduces false positives by distinguishing between temporarily slow clients and truly unresponsive ones, maintaining system performance while protecting legitimate clients from premature isolation.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, identifying a first client that is associated with a first data item, identifying a first time when the first data item is obtained, identifying a second time associated with a second data item, wherein a receipt of the second data item has not been acknowledged by the first client when the first data item is obtained, computing a first time difference between the first time and the second time, comparing the first time difference to a first threshold, resulting in a first comparison, and initiating a first action based on the first comparison. Other embodiments are disclosed.