Search arXivSearch

arXiv · 2608.01564

Source-Bounded Exact Recovery over Docker's Logs API

Abstract

Docker can retain records that a collector misses before attachment or during downtime. A persisted read position does not by itself ensure recovery after lifecycle changes. We study what exact recovery contract is achievable through Docker's supported Logs API. We define source-bounded exactness: every retained, distinguishable source record eventually appears exactly once in durable collector output. Our method uses a generation-aware multiset oracle that separates source truncation from collector omission and exposes simultaneous loss and replay. Applied to LogDeck, it uncovered a start-to-attachment race; a one-record attachment overlap, finite Docker-API reconciliation, and exact insertion closed the tested boundary. We compare the fixed revision with unmodified Grafana Alloy 1.18.0, which uses the same API and persists read positions; across 120 collector-runs, LogDeck was exact in 60/60 and Alloy in 20/60. Alloy succeeded at guarded startup and process pause but omitted retained history when recovery required discovering an exited or restarted source. In a causal control, a 5,000-record source exited before collection: stock discovery was exact in 0/20 trials and acquired nothing, while the same reader given the container ID recovered all records exactly in 20/20. This reproduced on OrbStack and independent Ubuntu hosts with Docker 29.4.0 and 24.0.9; both collectors recovered daemon restart, while neither recovered records after source removal. Exactness assumes distinct tuples of physical generation, timestamp, stream, and bytes; 200,000 byte-identical records across two drivers produced no observed collisions. Our results show that lifecycle reacquisition, not a persisted position alone, determines exact recovery within the retained-source horizon. This is a bounded interface claim, not a universal collector ranking or proof of collision freedom.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kelvin Amoaba. 2026-08-03. Source-Bounded Exact Recovery over Docker's Logs API. https://arxiv.org/abs/2608.01564

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Reforge: Low-Latency Distributed GNN Serving with Selective Embedding Recomputation

Graph Neural Networks (GNNs) have been widely adopted for their ability to compute expressive node representations in graph datasets. However, serving GNNs on large graphs is challenging due to the high communication, computation, and memory overheads of constructing and executing computation graphs, which represent information flow across large neighborhoods. Existing approximation techniques in training can mitigate the overheads but, in serving, still lead to high latency and/or accuracy loss. To this end, we propose Reforge, a system that enables low-latency GNN serving for large graphs with minimal accuracy loss through two key ideas. First, Reforge employs selective recomputation of precomputed embeddings, which allows for reusing precomputed computation subgraphs while selectively recomputing a small fraction to minimize accuracy loss. Second, we develop computation graph parallelism, which reduces communication overhead by parallelizing the creation and execution of computation graphs across machines. Our evaluation with large graph datasets and GNN models shows that Reforge significantly outperforms state-of-the-art techniques.

cs.DC

Agentic AI Workload Characteristics

Agentic AI shifts LLM serving from isolated prompt-generation requests to stateful, multi-turn executions that repeatedly invoke the model, call tools, and grow context over time. This paper characterizes ReAct-style agents from both the LLM-serving and tool-execution perspectives using an end-to-end tracing infrastructure across reasoning and non-reasoning Gemma and Qwen configurations on five agentic benchmarks. Our study shows that agentic workloads are not simply long-prompt workloads: with effective context caching, most input tokens are reused across turns, making execution decode-dominated while increasing dependence on long-lived KV-cache state. We also find that tool use has a clear temporal structure, with agents shifting from read/explore behavior early in execution to execute/write behavior later. These results show that efficient agentic serving must jointly manage repeated model re-entry, persistent context state, and workload-dependent tool behavior.

cs.DC

Byzantine Causal Reliable Broadcast (BCRB) with Constant-Size Message Metadata

Asynchronous Byzantine Reliable Broadcast (BRB) is a fundamental primitive that guarantees agreement and validity in distributed systems subject to Byzantine faults, but it lacks ordering guarantees. In this paper, we address Byzantine Causal Reliable Broadcast (BCRB), which builds on BRB to enforce causal message ordering. We present a novel BCRB protocol that decouples causal ordering from the BRB layer, achieving constant-size $\mathcal{O}(1)$ message metadata overhead and $\mathcal{O}(n^2)$ communication word complexity as against $\mathcal{O}(n^3)$ communication word complexity of existing protocols; here $n$ is the number of processes. We present two variants of our protocol: a cryptographic version using a threshold encryption scheme and sequence gating, and its non-cryptographic version. In the cryptographic version, senders broadcast ciphertexts immediately, and decryption shares are piggybacked on out-of-band ACKs, preventing early decryption and front-running. In both versions, causal safety is achieved probabilistically. We evaluate the probability of causal safety violations using a random variable path analysis under independent exponential link delay distributions. We show that both variants satisfy liveness and the probability of weak safety violation is bounded by $\mathcal{O}(f^{-3}\cdot\ln^3 f)$, where $f$ is the upper bound on the number of Byzantine processes, and $f < n/3$ and $f=\mathcal{O}(n)$. Further, for the crypto version, we show that the probability of strong safety violation is bounded by $\mathcal{O}(f^{-1} \cdot \ln^2 f)$. We also show how to modify our two protocols to guarantee 100\% weak safety keeping $\mathcal{O}(1)$ message space overhead but with $\mathcal{O}(n^3)$ messages and $\mathcal{O}(n^3)$ communication word complexity.

cs.DC