Telemetry Primitive Contract

• Telemetry Primitive Contract is a formal framework that defines minimal operational guarantees, data types, and configurations for telemetry mechanisms in modern monitoring environments.
• It specifies API-level operations between reporters and collectors, enabling precise end-to-end measurement aggregation and probabilistic collision recovery using checksums.
• The contract supports adaptive SLA-aware resource allocation with tunable knobs and rigorous performance metrics, ensuring scalable, efficient, and compliant monitoring deployments.

A telemetry primitive contract codifies the minimal formal guarantees, data types, semantics, and configurability required of telemetry mechanisms in modern data-plane and system monitoring environments. It serves as the logical boundary between telemetry reporters and collectors, defining precisely how fine-grained system and network measurements are represented, written, queried, and controlled. The contract specifies both the API-level primitives and the underlying protocol, with rigorous probabilistic and semantic guarantees that enable resource-efficient, scalable, and analyzable monitoring across distributed, high-throughput infrastructures.

1. Formal Specification and Semantic Guarantees

The telemetry primitive contract rigorously defines the core operations and semantics required of telemetry mechanisms. In the context of network slice monitoring, the contract comprises a triple $(X, E(\cdot), \Gamma(\cdot))$ where $X$ is the set of tunable operating points (knobs) for each slice $s$ and metric $m$, $E(x)$ is a calibrated upper bound on the expected end-to-end monitoring error, and $\Gamma(x)$ is the corresponding overhead (e.g., bits per packet). Key requirements include:

• Per-slice/per-metric tunability: Each primitive exposes a runtime-configurable knob $x_{s,m}$, selected from $X_{s,m}$, which can be adjusted by the control plane without recompilation or pipeline reinstallation.
• Composable end-to-end semantics: Per-hop measurements and per-packet annotations must aggregate into predictable end-to-end estimates, with analytical bounds enforceable by the control logic.
• Predictable accuracy-overhead trade-offs: For each knob setting, the contract provides explicit trade-off curves $E(x)$ vs. $\Gamma(x)$ that are learned or estimated at runtime (Saha et al., 13 Dec 2025).

The contract formalizes closed-loop resource allocation, allowing monitoring to be dynamically adjusted to enforce slice-level SLA constraints under budget.

2. Primitive Operations and API Interfaces

At the API layer, the telemetry primitive contract specifies distinct and atomic operations for both reporters (e.g., switches) and collectors. In zero-CPU telemetry systems:

• Switch-side write primitive: On trigger (telemetry report $(k, v)$), $N$ independent hashes $h_i(k)$ are computed. For each $i$, the switch emits a one-sided RDMA_WRITE to collector $C_i$ at offset $A_i$, writing the payload $(c \| v)$ where $c$ is a $b$-bit checksum.
• Collector-side query primitive: Given key $k$, the collector computes the same $N$ hashes, reads the $N$ memory locations, filters by checksum, and returns the consistent value (if any) (Langlet et al., 2021).

No lock, handshake, or atomic synchronization is permitted, ensuring stateless, coordination-free operation.

3. Shared-Memory Layout and Collision Recovery

The shared-memory architecture is defined by the contract:

• Flat cell array: Each collector exposes $M$ cells of fixed size $S = b + w$ bits.
• Uniform partitioning: Keys map via $N$ hashes into $M$ cells (no per-switch or per-key reservations).
• Redundancy and collision recovery: Each key writes $N$ copies to $N$ distinct cells. Write conflicts are resolved probabilistically; overwritten cells are detected at query time using checksums. No per-key state or lock is maintained at the switch.

This probabilistic, stateless model is analytically tractable, permitting formal bounds on overwrite, error, and query failure rates.

4. Probabilistic Performance and Resource Formulas

The contract is equipped with precise mathematical formulas governing performance, collision rates, and resource usage:

• Load factor: $\alpha = K/M$ (number of keys since last update divided by collector cell array size).
• Probability formulas:
  • Single cell overwrite: $p_{\text{ovw}} = 1 - e^{-\alpha N}$
  • All $N$ cells overwritten: $P_{\text{all\_ovw}} = (1 - e^{-\alpha N})^N$
  • Empty return lower-bound: $$P_{\text{empty}} \geq (1 - e^{-\alpha N})^N (1 - 2^{-b})^N$$
  • Return error lower/upper bounds as exact expressions in $b$, $N$, $\alpha$ (Langlet et al., 2021).
• Query success probability: $$P_{\text{success}} \approx 1 - P_{\text{empty}} - P_{\text{error}}$$
• Expected per-key memory usage: $N \cdot S$ bits, or $M / F$ bytes for $F$ distinct concurrent keys.

These formulas yield concrete memory/error trade-off decisions for contract parameterization.

5. Data Model Contracts in System Telemetry

For system-level telemetry, the contract specifies:

• Primitive types: Entities (processes, files, containers), Events (atomic actions), Flows (aggregates of actions over time).
• Schema: JSON-Schema for types and fields; EBNF grammar; LaTeX-form cardinality constraints.
• Graph semantics: The telemetry log forms a directed graph (entities as vertices, events/flows as edges), enabling provenance and causality analysis.
• Invariants: Strict parent-child consistency for process trees, immutable 5-tuples for flows, non-overlapping flows per resource/thread (Taylor et al., 2021).
• Composition rules: Formal aggregation of atomic events into volumetric flows, with explicit timeouts and resource binding.

This precise data-model contract ensures interoperability and extensibility for big-data analytics scenarios.

6. SLA-Aware Allocations and Dynamic Control

Telemetry primitive contracts are instrumental in SLA-driven, budget-aware telemetry deployments:

• Closed-loop control: The contract supports per-slice, per-metric dynamic knob selection via integer linear programming, subject to SLA error tolerances and resource constraints.
• Predictive analytics: The trade-off curves $(X, E(\cdot), \Gamma(\cdot))$ supply the control plane with real-time predictions of monitoring error and bandwidth for adaptive reallocation (Saha et al., 13 Dec 2025).
• Evaluation highlights: Adaptive primitives yield up to $4\times$ fewer SLA violations for critical slices, demonstrating provable improvements over static, slice-agnostic mechanisms.

This approach is central to enabling differentiated, SLA-nuanced telemetry in heterogeneous network slices and large-scale monitoring platforms.

7. Applications and Example Deployments

Concrete instantiations of telemetry primitive contracts include:

Example System	Primitive Contract Feature	Scalability/Guarantee
DART (Zero-CPU Collection) (Langlet et al., 2021)	Write/query API, probabilistic memory layout	99.9% trace fidelity at <300 B/flow, lock-free
SysFlow (System Behavior) (Taylor et al., 2021)	Entity/event/flow schema; invariants	Order-of-magnitude trace compression and guaranteed provenance
SliceScope (SLA-Aware Slicing) (Saha et al., 13 Dec 2025)	Tunable knob, trade-off curves, closed-loop	Up to 4× fewer SLA violations, predictable resource use

As evidenced in INT path tracing cases, 5-hop fat-trees with 100 million flows reach $>99.9\%$ query success at attainable DRAM budgets; system-level telemetry achieves scalable analytics; slice monitoring enables dynamic SLA conformance at bounded error/overhead.

A plausible implication is that formal, analyzable telemetry primitive contracts will be central to next-generation resource-aware, SLA-compliant network and system monitoring frameworks, providing both implementation tractability and rigorous operator controls across diverse monitoring use-cases.