Pith Number

pith:LWDKY4MI

pith:2026:LWDKY4MI64RYCSMIT7TYGFYX6D

not attested not anchored not stored refs resolved

Scale: Deep Reinforcement Learning for Container Scheduling in Serverless Edge Computing

Andrea Sabbioni, Chen Chen, Lei Jiao, Reza Farahani, Zihan Jia

A deep reinforcement learning scheduler for serverless edge containers stays within 1.15 times of optimal while deciding up to 99 percent faster.

arxiv:2605.15704 v1 · 2026-05-15 · cs.DC

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\usepackage{pith}
\pithnumber{LWDKY4MI64RYCSMIT7TYGFYX6D}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Scale achieves solutions within a factor of 1.11 to 1.15 of a state of the art Integer Linear Programming solver, while reducing decision making time by up to 99%.

C2weakest assumption

The policy-based deep reinforcement learning algorithm can jointly incorporate SLO constraints, end-to-end latency, and data locality to balance system stability and performance under dynamic workloads (abstract).

C3one line summary

Scale applies policy-based deep reinforcement learning to SLO-aware container scheduling in serverless edge computing, achieving near-optimal results with drastically reduced decision time in simulations.

References

30 extracted · 30 resolved · 0 Pith anchors

[1] Cross-edge orchestration of serverless functions with probabilistic caching.IEEE Transactions on Services Computing, 17(5):2139–2150, 2024 2024

[2] Sebs-flow: Benchmarking server- less cloud function workflows 2025

[3] Octopus: Decentralized workflow-granular scheduling for serverless workflow 2025

[4] S-cache: Function caching for serverless edge computing 2023

[5] Fasei: Fast serverless edge inference with synergistic lazy loading and layer-wise caching 2025

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:01:13.461258Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

5d86ac7188f7238149889fe7831717f0ee23a2f8f234cd64c389e4549bc35f18

Aliases

arxiv: 2605.15704 · arxiv_version: 2605.15704v1 · doi: 10.48550/arxiv.2605.15704 · pith_short_12: LWDKY4MI64RY · pith_short_16: LWDKY4MI64RYCSMI · pith_short_8: LWDKY4MI

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/LWDKY4MI64RYCSMIT7TYGFYX6D \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 5d86ac7188f7238149889fe7831717f0ee23a2f8f234cd64c389e4549bc35f18

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "421ff1898e8738d4ec38430cc31aff21cb8834ece5137b36ac30e9bd17015599",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.DC",
    "submitted_at": "2026-05-15T07:49:44Z",
    "title_canon_sha256": "12d14100026e1ec4c61e446e5df432cbe9e57558874c7bed58872cb05421c8c9"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15704",
    "kind": "arxiv",
    "version": 1
  }
}