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ARXIV:2602.23935 · CLOUD OPTIMIZATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2602.23935CLOUD OPTIMIZATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
LACE-RL optimizes serverless computing efficiency by balancing latency and carbon emissions using deep reinforcement learning.
Opportunity summary
Pain LACE-RL optimizes serverless computing efficiency by balancing latency and carbon emissions using deep reinforcement learning.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
LACE-RL optimizes serverless computing efficiency by balancing latency and carbon emissions using deep reinforcement learning. Reducing cold-start latency requires retaining warm function instances, while minimizing carbon emissions favors reclaiming idle resources.
Serverless computing simplifies cloud deployment but introduces new challenges in managing service latency and carbon emissions. Reducing cold-start latency requires retaining warm function instances, while minimizing carbon emissions favors reclaiming idle resources.
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Using the Huawei Public Cloud Trace, we show that LACE-RL reduces cold starts by 51.69% and idle keep-alive carbon emissions by 77.08% compared to…
Cloud Optimization moved forward this cycle; last verified April 2026. Public score 3.0/10.
Continue into Read for claims, analysis, references, and neighboring papers.
Opportunity summary
Score3.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
LACE-RL optimizes serverless computing efficiency by balancing latency and carbon emissions using deep reinforcement learning.
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WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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BUZZ
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Paper Pack
10.48550/arXiv.2602.23935LACE-RL optimizes serverless computing efficiency by balancing latency and carbon emissions using deep reinforcement learning.
Abstract
Serverless computing simplifies cloud deployment but introduces new challenges in managing service latency and carbon emissions. Reducing cold-start latency requires retaining warm function instances, while minimizing carbon emissions favors reclaiming idle resources. This balance is further complicated by time-varying grid carbon intensity and varying workload patterns, under which static keep-alive policies are inefficient. We present LACE-RL, a latency-aware and carbon-efficient management framework that formulates serverless pod retention as a sequential decision problem. LACE-RL uses deep reinforcement learning to dynamically tune keep-alive durations, jointly modeling cold-start probability, function-specific latency costs, and real-time carbon intensity. Using the Huawei Public Cloud Trace, we show that LACE-RL reduces cold starts by 51.69% and idle keep-alive carbon emissions by 77.08% compared to Huawei's static policy, while achieving better latency-carbon trade-offs than state-of-the-art heuristic and single-objective baselines, approaching Oracle performance.
Source availability
PDF linkedThe paper record includes a public PDF URL.
Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 3.0
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Using the Huawei Public Cloud Trace, we show that LACE-RL reduces cold starts by 51.69% and idle keep-alive carbon emissions by 77.08% compared to Huawei's static policy, while achieving better latency-ca...
PROBLEM
LACE-RL optimizes serverless computing efficiency by balancing latency and carbon emissions using deep reinforcement learning. Reducing cold-start latency requires retaining warm function instances, while minimizing carbon emissions favors reclaiming idle resources.
METHOD
Serverless computing simplifies cloud deployment but introduces new challenges in managing service latency and carbon emissions. Reducing cold-start latency requires retaining warm function instances, while minimizing carbon emissions favors reclaiming idle resources.
WHY NOW
Cloud Optimization moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
LACE-RL optimizes serverless computing efficiency by balancing latency and carbon emissions using deep reinforcement learning. Reducing cold-start latency requires retaining warm function instances, while minimizing carbon emissions favors reclaiming idle resources.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Serverless computing simplifies cloud deployment but introduces new challenges in managing service latency and carbon emissions. Reducing cold-start latency requires retaining warm function instances, while minimizing carbon emissions favors reclaiming idle resources.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Using the Huawei Public Cloud Trace, we show that LACE-RL reduces cold starts by 51.69% and idle keep-alive carbon emissions by 77.08% compared to Huawei's static policy, while achieving better latency-carbon trade-offs than state-of-the-art heuristic and single-objective baselines, approaching Oracle performance.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Cloud Optimization moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Foundation
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Concepts
Methods
Materials
Markets
Competitors
LACE-RL optimizes serverless computing efficiency by balancing latency and carbon emissions using deep reinforcement learning.
Segment
Cloud Optimization
Adoption evidence
No public code link in the paper record yet
Commercial read
3.0/10 public viability
Direct
Adjacent
Substitute
Unknown
No indexed public discussion is attached to 2602.23935 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.
Hacker News
Not indexed yet
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Bluesky
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Showing 20 of 28 references
CITED BY
No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.
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}Canonical route, proof status, last verified, refs, sources, and coverage.
Page Freshness
Canonical route: /paper/green-or-fast-learning-to-balance-cold-starts-and-idle-carbon-in-serverless-computing
This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.
Endpoint list, payload shape, route context, and copyable handoff data.
Agent Handoff
Canonical ID green-or-fast-learning-to-balance-cold-starts-and-idle-carbon-in-serverless-computing | Route /paper/green-or-fast-learning-to-balance-cold-starts-and-idle-carbon-in-serverless-computing
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/green-or-fast-learning-to-balance-cold-starts-and-idle-carbon-in-serverless-computingMCP example
{
"tool": "get_paper",
"arguments": {
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}
}source_context
{
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"mode": "paper",
"query": "Green or Fast? Learning to Balance Cold Starts and Idle Carbon in Serverless Computing",
"normalized_query": "2602.23935",
"route": "/paper/green-or-fast-learning-to-balance-cold-starts-and-idle-carbon-in-serverless-computing",
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"topic_slug": null,
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}Verdict, compute envelope, blockers, signature state, and receipt links.
Paper proof page receipt window
/buildability/green-or-fast-learning-to-balance-cold-starts-and-idle-carbon-in-serverless-computing
Subject: Green or Fast? Learning to Balance Cold Starts and Idle Carbon in Serverless Computing
Verdict
Ignore
Verdict is Ignore because current viability and proof state do not clear the buildability gate.
Time to first demo
Insufficient data
No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.
Structured compute envelope
Insufficient data
Visual citations from the paper document graph.
The application/ld+json payload rendered for agents.
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}No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.
Receipt path
/buildability/green-or-fast-learning-to-balance-cold-starts-and-idle-carbon-in-serverless-computing
Paper ref
green-or-fast-learning-to-balance-cold-starts-and-idle-carbon-in-serverless-computing
arXiv id
2602.23935
Generated at
2026-04-02T02:30:40.136Z
Evidence freshness
stale
Last verification
2026-04-02T02:30:40.136Z
Sources
0
References
0
Coverage
17%
Lineage hash
87d7defcbd8aa55f9af966eddc91e91f7d52bda126a893ed22a822a319e1092b
Canonical opportunity-kernel lineage hash.
External signature
unsigned_external
No founder, registry, pilot, or production-adoption signature is attached to this receipt.
Verification
not_verified
Verification is blocked until an external signature is provided.
Verification pending / evidence receipt incomplete
repo_url
references
0/3 checks · 0%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
No checklist artifact is attached to the Build Passport payload.
Derived signals show verified:false until source-backed receipts exist.
Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 0 sources / 17% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
No named person assigned.
Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
ARTIFACTS
No public artifacts yet.
DEFENSIBILITY
Defensibility and confidence evidence pending.
Evidence
0 references, 0 sources, 17% evidence coverage.
Gaps
Next test
Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
Build tab has no CRM, procurement, or operator source.
Gaps
Next test
Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
Gaps
Next test
Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
Cost passport has no observed_usd value.
Gaps
Next test
Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
Gaps
Next verification path
Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
Next verification path
No GTM owner verified.
No CRM or outreach source attached.
People
No named person assigned.
Gaps
Next verification path
Regulatory need unclassified.
No clinical or regulatory source attached.
People
No named person assigned.
Gaps
Next verification path