System for real-time monitoring of code performance with AI-supported bottleneck detection

Abstract

A computer-implemented system for real-time monitoring of code performance and AI-supported bottleneck detection, the system comprising: a probe orchestrator configured to activate, adjust, and roll back runtime and kernel instrumentation within a predefined overhead budget; a secure instrumentation layer configured to capture execution and resource events using sandboxed user-space probes and verifier-checked kernel programs; a telemetry acquisition pipeline configured to aggregate events via zero-copy ring buffers with schema-versioned datasets and backpressure signaling; a path vectorizer that converts call stacks, spans, and profiles into streaming embeds for real-time inference; a causal graph generator that fuses time, trace, and resource metadata into an attributed performance graph; and a bottleneck detector that analyzes the attributed performance graph to identify and assess bottlenecks and suggest corrective actions; The coordinated collaboration of the components detects bottlenecks in real time without interrupting the ongoing service.

Description

AREA OF INVENTION

[0001] The invention relates to computer-implemented systems for software performance monitoring. In particular, it relates to a cloud-native system for real-time monitoring of code execution and for the automatic detection, attribution, and derivation of remedial measures for performance bottlenecks across heterogeneous runtime environments and distributed services. BACKGROUND OF THE INVENTION

[0002] Conventional Application Performance Monitoring (APM) and profilers rely on periodic sampling, coarse-grained metric thresholds, or manual trace inspection. These approaches struggle to (i) attribute on-CPU and off-CPU delays to precise code locations, (ii) infer causality across microservices and asynchronous execution, (iii) operate with low overhead in production environments, and (iv) translate observations into actionable corrective actions with minimal risk. Static thresholds are fragile in the face of workload drift; multilingual stacks complicate consistent instrumentation; and data privacy requirements limit the centralized collection of fine-grained traces.Therefore, there is a need for a unified, low-overhead, explainable, and client-aware system that continuously monitors code execution, establishes causal relationships between events, identifies emerging bottlenecks in real time, and synthesizes safe, testable remedial actions—without requiring restarts or invasive instrumentation.

[0003] The system provides a real-time system that detects performance bottlenecks at functional, path, and service boundaries with explainable attribution.

[0004] The system offers low-overhead, safety-tested runtime instrumentation that can be activated, fine-tuned, and rolled back without service interruption.

[0005] The system constructs causal performance graphs from heterogeneous telemetry including traces, profiles, logs and resource signals—with sub-second latency.

[0006] The system uses AI models that identify and rank bottlenecks based on learned code path representations and utilize uncertainty-aware outlier detection.

[0007] The system generates corrective actions and machine-readable change intents that can be validated through progressive rollouts.

[0008] The system maintains client isolation, confidentiality and data minimization through data protection-compliant learning. SUMMARY OF THE INVENTION

[0009] In one aspect, the invention comprises a system with: (a) a probe orchestrator that coordinates hot-attach instrumentation across runtimes and kernels under an overhead budget; (b) a safe instrumentation layer that implements secure user-space probes and kernel event capture via eBPF-like mechanisms with policy-driven sampling and throttling; (c) a telemetry acquisition and aggregation pipeline with zero-copy ring buffers, schema-versioned datasets, and backpressure signaling; (d) a path vectorizer that converts call stacks, spans, and profiles into numerical embeddings (CodePath2Vec) for streaming inference; and (e) a causal graph generator that fuses logical time, trace context, and resource dependencies into an attributed performance graph.(f) a bottleneck detector employing a hybrid model with a graph neural network including attention over code path embeddings and conformal prediction for threshold-free anomaly indicators; (g) an action synthesizer proposing patch diffs, configuration adjustments, and code-level hints with predicted risk / benefit and automatically generated verification checks; and (h) a policy and privacy module for tenant assignment, masking, and auditing. In a further aspect, the system comprises a computer program product on a non-volatile, machine-readable storage medium containing instructions that, when executed, cause processors to perform the functions of the aforementioned components. DETAILED DESCRIPTION OF THE INVENTION

[0010] The system deploys lightweight agents within processes and / or as sidecars, along with a control plane in one or more regions. The agents instrument application code (e.g., managed and native runtimes) and operating system boundaries (e.g., syscalls, scheduler events) to capture timing, contention, allocation, I / O, and network interactions. The control plane manages policies, model weights, and remediation catalogs, and receives aggregated summaries—not raw, sensitive payloads—while maintaining tenant isolation. Sample Orchestra

[0011] The probe orchestrator maintains a versioned catalog of probes for supported runtimes (e.g., bytecode Weaver for managed languages ​​and tracepoints for native code) as well as kernel events. Probes are dynamically activated based on observed risk and user policies; a target overhead budget is enforced through adaptive sampling. If health indicators or SLOs deteriorate, the orchestrator automatically reduces probe intensity. The probes are idempotent and hot-swappable, eliminating the need for reboots. Secure instrumentation layer

[0012] The SIL isolates probe execution in a sandbox and validates the probe bytecode before loading. For kernel-side acquisition, the SIL uses eBPF-like programs with verifier-like checks and rate limiters. User-space probes attach to functions via runtime attach APls or debug interfaces. All events are normalized to a schema that includes code position, timestamps, resource identifiers, and correlation tokens. Telemetry recording and aggregation

[0013] Agents write to lock-free ring buffers, which are cleared by a user-space collector using zero-copy reads. Datasets are condensed into sketch-based summaries (e.g., quantile digests) and periodic flamegraph deltas to limit bandwidth. Backpressure is fed back to the probes via the orchestrator to stay within the overhead budget. The pipeline preserves tenant labels and enforces masking policies at the source. Path vectorizer (CodePath2Vec)

[0014] The path vectorizer converts sequences of frames—identified by function, module, line, CPU residence (on / off), wait reasons, and allocation points—into fixed-length embeddings using tokenization and positional encoding. For long paths, a streaming encoder incrementally updates the embeddings as new spans arrive, enabling scoring with millisecond latency. The embeddings feed both the bottleneck detector and the similarity search for known patterns. Causal graph generator

[0015] The causal graph generator creates an attributed graph whose nodes represent code locations and resources, and whose edges represent potential causal relationships (e.g., lock acquisition, RPC call, scheduler queue placement). A time synchronization process fuses hybrid logic clocks with disciplined reference clocks to limit skew. Edge weights capture the marginal latency contribution and uncertainty. The graph supports queries such as "Primary cause of tail latency for endpoint X". Bottleneck detector

[0016] The bottleneck detector executes a hybrid model: (i) a graph neural network with message passing and attention over the causal graph to identify high-impact nodes / edges; (ii) a conformal anomaly scorer that generates calibrated, threshold-free flags with prediction intervals; and (iii) a drift monitor that updates baselines online. Outputs include an impact score per code position and an explanation vector that maps to features (e.g., context switching, cache misses, lock hold time), enabling human-verifiable root cause reports. Action synthesizer

[0017] Based on a prioritized set of suspected bottlenecks, the action synthesizer retrieves suitable remedies from a library (e.g., batching, pool limits, memoization, adding an SQL index) and generates machine-readable change intents. Where safe, it creates minimal diffs (e.g., configuration patches or toggles at the bytecode level) along with verification checks (e.g., invariants, expected latency deltas). The control plane can then deploy these incrementally as canary rollouts via policy gates. Guidelines, data protection and client isolation

[0018] All telemetry data is assigned to a client and region; applied policies are cryptographically verified. Dynamic masking redacts sensitive fields at the source. To improve models across clients, the system supports federated learning on anonymized, differentially private aggregates and avoids sharing raw traces. EXAMPLE IMPLEMENTATION SCENARIOS Example 1: Lock competition in a managed runtime

[0019] A sudden increase in tail latency is attributed to a critical section. The system's causal graph links the delays to a lock in a utility module; the action synthesizer recommends more granular locking and an adjustment to the thread pool. A canary rollout shows reduced competition without an increase in the error rate. Example 2: N+1 query pattern in a web service

[0020] The path vectorizer finds an embedding similar to known N+1 patterns. The system suggests query batching and adding an index; verification checks confirm reduced database round trips and improved p95 latency. Example 3: Off-CPU I / O stall in an asynchronous service

[0021] Kernel-side probes show frequent short wait phases for the drive; the detector indicates an off-CPU stall. The system recommends asynchronous buffering and tuning of file descriptors; canary validation passes the SLO gates. INDUSTRIAL APPLICABILITY

[0022] Usable in fintech, e-commerce, healthcare, telecommunications, gaming backends, and any enterprise software platform that requires continuous performance assurance under production load. PREFERRED FORM OF EXECUTION OF THE INVENTION

[0023] A preferred embodiment uses: sandboxed user-space probes and verifier-checked kernel probes; zero-copy ring buffer capture; streaming path vectorization; a graph neural network with attention and conformal prediction; and a control plane that synthesizes remediation intents and automates policy-driven canary rollouts. Alternative embodiments with equivalent mechanisms are covered by the scope of protection.

Claims

[1] A computer-implemented system for real-time monitoring of code performance and AI-supported bottleneck detection, wherein the system comprises: a probe orchestrator configured to activate, adjust, and roll back runtime and kernel instrumentation within a predefined overhead budget; a secure instrumentation layer configured to capture execution and resource events using sandboxed user-space probes and verifier-checked kernel programs; a telemetry acquisition pipeline configured to aggregate events via zero-copy ring buffers with schema-versioned datasets and backpressure signaling; a path vectorizer that converts call stacks, spans, and profiles into streaming embeds for real-time inference; a causal graph generator that fuses time, trace, and resource metadata into an attributed performance graph; and a bottleneck detector that analyzes the attributed performance graph to identify and assess bottlenecks and suggest corrective actions; The coordinated collaboration of the components detects bottlenecks in real time without interrupting the ongoing service. [2] System according to claim 1, wherein the probe orchestrator enforces the overhead budget by dynamically throttling the probe sampling rates based on service-level objectives and observed health indicators, and performs hot-attach and hot-rollback probe operations without restarting application processes. [3] System according to claim 1, wherein the telemetry acquisition pipeline generates sketch-based quantile summaries and flamegraph deltas from lock-free ring buffer entries and reports backpressure to the probe orchestrator to stay within the overhead budget. [4] System according to claim 1, wherein the causal graph generator performs time synchronization using hybrid logic clocks in combination with disciplined reference clocks to limit time offset, assigns edge weights specifying the marginal latency contribution and uncertainty, and provides queries that identify the main causes of tail latency for selected endpoints. [5] System according to claim 1, further comprising an action synthesizer that selects remedies from a library, including batching, pooling limits, memoization, query indexing and asynchronous buffering, and outputs machine-readable remedy intents with minimal configuration or code diffs and verification checks for policy-driven canary rollouts.