Abstract
The fastest policy is the one the hot path barely notices.
Research question / RQ-01
Can a web-cache policy preserve useful objects under hostile churn while making coordination and bookkeeping nearly disappear from the hot path?
01 / Context
Dong et al. (2025) introduced Mobius as a lock-free cache-eviction design for throughput-sensitive systems. Hyx uses a production adaptation as the in-memory engine behind its response cache and other latency-sensitive subsystems.
02 / Measured answer
1.2×
read throughput
2.7×
low-concurrency puts
Meaningful on reads. Dramatic on the mutation path. TinyUFO is the measured baseline.
03 / Publication boundary
This study covers observable behavior, contractual semantics, controlled measurements, and production constraints. It excludes internal mechanics that could be reconstructed from the description.
Evidence over implementation
Research lineage
The original paper defines Mobius. Hyx evaluates a production adaptation.
Published study / Dong et al. 2025
Lock-free eviction as a throughput problem.
Dong et al. (2025) present Mobius as a lock-free design derived from SIEVE and evaluate it with synthetic workloads and four large real-world traces.
The authors report 1.2×–8.5× concurrent-throughput improvements over state-of-the-art comparison policies while maintaining comparable hit ratios. Their RocksDB experiments report average gains of 4% for point reads and 14% for range reads over the LRU-backed system (Dong et al., 2025).
The paper also reports that Mobius and SIEVE share identical single-thread hit ratios under its synthetic evaluation, while Mobius improves throughput and tail latency through its concurrency design.
Hyx study / production evidence
Same lineage. Independently measured implementation.
Hyx’s engine follows the paper’s throughput-first research direction but is not presented as a byte-for-byte reproduction. Repository evidence records that a faithful version of the paper’s second optimization was implemented and measured before the production design intentionally diverged.
This public study reports only externally meaningful behavior: relative throughput, bounded capacity, scan resistance, TTL semantics, update guarantees, and concurrency invariants. It withholds the mechanics and constants needed to reconstruct the production path.
Baseline warning: Dong et al. (2025) use a different comparison system. Hyx’s ~1.2× read and ~2.7× write figures compare against its previous cache. The results use different baselines and are not interchangeable.
Evidence / E1 · E8
Comparative result
Mobius leads TinyUFO most sharply where caches change.
R-01 / controlled comparison
higher is better / relative throughput
Largest measured lead
2.7×
low-concurrency puts / TinyUFO baseline
The mutation path is where Mobius most clearly separates itself in the recorded comparison.
| Dimension | Mobius | TinyUFO | Evidence boundary |
|---|---|---|---|
| Read path | ~1.2× relative throughput | Baseline | Controlled, core-pinned internal microbenchmark |
| Put path | ~2.7× relative throughput | Baseline | Low-concurrency internal microbenchmark |
| Update semantics | Existing value is deterministically replaced | Admission can reject a candidate | Production rules-cache contract |
| Pressure model | Bounded by entry count or explicit weight | Capacity-bounded | Repository invariant tests |
E1 / internal controlled microbenchmark · relative ratios are repository-recorded · not an independent third-party reproduction · not end-to-end CDN throughput
Repository experiments
Speed is interesting. Staying correct under pressure is decisive.
Adversarial scan
A resident hot object is repeatedly accessed while 49,999 distinct newcomers pass through a cache sized far below the stream.
Result / The hot object remains resident. This establishes scan resistance for the tested workload, not universal optimality.
Concurrent mutation
Eight workers perform 800,000 mixed reads, writes, and removals across a keyspace five times larger than the configured capacity.
Result / The test completes without worker panic and the reported cache weight remains within its configured bound.
Weighted pressure
A large newcomer enters a cache already filled with many lightweight objects; separate tests also restore entries at larger weights.
Result / The newcomer is retained while total weight remains bounded, covering non-uniform object sizes.
Sustained soak
Sixteen workers run mixed reads, writes, removals, and short TTLs against a keyspace twice the capacity for five seconds.
Result / The ignored release-mode soak asserts bounded weight and panic-free completion. It is a stress invariant, not a latency benchmark.
Production relevance
A cache engine is valuable when the surrounding system can trust its behavior.
O-01 / integration surface
one engine / multiple hot paths
Response delivery
Fresh, stale, variant-aware, range-capable response serving with a disk reserve beneath memory.
Compiled policy
Capacity-bounded caches for domain rules and negative lookups, with deterministic replacement on updates.
Optimized assets
Weighted storage for transformed image outputs where object size, rather than object count, defines pressure.
Revalidation
Stale-while-revalidate and request coalescing reduce duplicate origin work around cache misses.
Interpretation
What these results establish, and where they stop.
Internal measurement
The TinyUFO ratios are controlled measurements recorded in the Hyx source. They are not presented as independent third-party results.
Host-specific absolute cost
Nanosecond-scale cache measurements vary with CPU, allocator, compiler, pinning, object type, and contention. Relative results are more portable than absolute timings.
No universal workload winner
The tests cover selected scans, mixed mutation, weights, TTLs, and integration semantics. They do not prove optimal hit ratio for every trace or object distribution.
The results support a specific conclusion. Under Hyx’s production-shaped test requirements, Mobius preserves the required cache invariants and materially outperforms the previous TinyUFO engine, with the largest improvement on writes.
Evidence register
Every Hyx claim has a repository anchor.
Controlled comparison record
The Mobius engine’s repository documentation records core-pinned measurements of ~1.2× TinyUFO reads and ~2.7× TinyUFO low-concurrency puts. Measurements use the deployed allocator. Raw host output is not published on this page.
Production integration record
The response cache identifies Mobius as the sole in-memory engine after concurrency soak validation. The compiled-rules cache documents deterministic replacement behavior and hot lookups.
Scan-resistance test
The repository test keeps one hot object alive through a 50,000-object scan against a 2,000-entry capacity.
Concurrent stress test
Eight threads each execute 100,000 mixed operations; the final assertion requires measured weight to remain at or below capacity.
Weighted-capacity suite
Heavy-newcomer, restore-at-larger-weight, count-mode, and flood tests assert that capacity accounting remains bounded.
Mixed-workload soak
A release-mode, 16-thread soak combines TTL and mutation pressure and checks boundedness throughout and at completion.
Cache semantics suite
Separate tests cover hit, miss, expiry, stale reads, removal, borrowed-key lookup, duplicate prevention, and queue compaction bounds.
Research-divergence record
The engine documentation records a measured faithful implementation of the paper’s consecutive-detection design and the evidence-led decision to use a faster production adaptation. Mechanics are intentionally omitted here.
References / Harvard style
Original Mobius study
Dong, C., Wang, F., Jiang, H. and Feng, D. (2025) ‘Using Lock-Free Design for Throughput-Optimized Cache Eviction’, Proceedings of the ACM on Measurement and Analysis of Computing Systems, 9(2), Article 44, pp. 1–28. doi: 10.1145/3727136.
Open source ↗Reference / 02SIEVE
Zhang, Y., Yang, J., Yue, Y., Vigfusson, Y. and Rashmi, K.V. (2024) ‘SIEVE is Simpler than LRU: An Efficient Turn-Key Eviction Algorithm for Web Caches’, in 21st USENIX Symposium on Networked Systems Design and Implementation. Santa Clara, CA: USENIX Association.
Open source ↗Reference / 03TinyLFU
Einziger, G., Friedman, R. and Manes, B. (2017) ‘TinyLFU: A Highly Efficient Cache Admission Policy’, ACM Transactions on Storage, 13(4), Article 35. doi: 10.1145/3149371.
Open source ↗Reference / 04TinyUFO crate
TinyUFO (n.d.) tinyufo crate documentation, version 0.8.1. Available at: docs.rs/TinyUFO (Accessed: 11 July 2026).
Open source ↗Research archive
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