From bb014f47d22fcd7c66a2125db4976dd5d7d6c899 Mon Sep 17 00:00:00 2001 From: Teleo Pipeline Date: Sun, 15 Mar 2026 15:52:12 +0000 Subject: [PATCH 1/6] extract: 2016-00-00-cambridge-staffing-non-poisson-non-stationary-arrivals Pentagon-Agent: Ganymede --- ...ess-adjustment-for-non-poisson-arrivals.md | 36 ++++++++++++++++ ...namic-staffing-not-constant-max-workers.md | 42 +++++++++++++++++++ ...ing-non-poisson-non-stationary-arrivals.md | 7 +++- 3 files changed, 84 insertions(+), 1 deletion(-) create mode 100644 domains/internet-finance/square-root-staffing-formula-requires-peakedness-adjustment-for-non-poisson-arrivals.md create mode 100644 domains/internet-finance/time-varying-arrival-rates-require-dynamic-staffing-not-constant-max-workers.md diff --git a/domains/internet-finance/square-root-staffing-formula-requires-peakedness-adjustment-for-non-poisson-arrivals.md b/domains/internet-finance/square-root-staffing-formula-requires-peakedness-adjustment-for-non-poisson-arrivals.md new file mode 100644 index 000000000..022959ee0 --- /dev/null +++ b/domains/internet-finance/square-root-staffing-formula-requires-peakedness-adjustment-for-non-poisson-arrivals.md @@ -0,0 +1,36 @@ +--- +type: claim +domain: internet-finance +description: "Bursty arrival processes require more safety capacity than Poisson models predict, scaled by variance-to-mean ratio" +confidence: proven +source: "Whitt et al., 'Staffing a Service System with Non-Poisson Non-Stationary Arrivals', Cambridge Core, 2016" +created: 2026-03-11 +--- + +# Square-root staffing formula requires peakedness adjustment for non-Poisson arrivals because bursty processes need proportionally more safety capacity than the Poisson baseline predicts + +The standard square-root staffing formula (workers = mean load + safety factor × √mean) assumes Poisson arrivals where variance equals mean. Real-world arrival processes violate this assumption through burstiness (arrivals clustered in time) or smoothness (arrivals more evenly distributed than random). + +Whitt et al. extend the square-root staffing rule by introducing **peakedness** — the variance-to-mean ratio of the arrival process — as the key adjustment parameter. For bursty arrivals (peakedness > 1), systems require MORE safety capacity than Poisson models suggest. For smooth arrivals (peakedness < 1), systems need LESS. + +The modified staffing formula adjusts the square-root safety margin by multiplying by the square root of peakedness. This correction is critical for non-stationary systems where arrival rates vary over time (daily cycles, seasonal patterns, or event-driven spikes). + +## Evidence + +- Whitt et al. (2016) prove that peakedness — the variance-to-mean ratio — captures the essential non-Poisson behavior for staffing calculations +- Standard Poisson assumption (variance = mean) fails empirically for bursty workloads like research paper dumps, product launches, or customer service spikes +- Using constant staffing (fixed MAX_WORKERS) regardless of queue state creates dual failure: over-provisioning during quiet periods (wasted compute) and under-provisioning during bursts (queue explosion) + +## Relevance to Pipeline Architecture + +Teleo's research pipeline exhibits textbook non-Poisson non-stationary arrivals: research dumps arrive in bursts of 15+ sources, futardio launches come in waves of 20+ proposals, while other days see minimal activity. The peakedness parameter quantifies exactly how much extra capacity is needed beyond naive square-root staffing. + +This directly informs dynamic worker scaling: measure empirical peakedness from historical arrival data, adjust safety capacity accordingly, and scale workers based on current queue depth rather than using fixed limits. + +--- + +Relevant Notes: +- domains/internet-finance/_map + +Topics: +- core/mechanisms/_map diff --git a/domains/internet-finance/time-varying-arrival-rates-require-dynamic-staffing-not-constant-max-workers.md b/domains/internet-finance/time-varying-arrival-rates-require-dynamic-staffing-not-constant-max-workers.md new file mode 100644 index 000000000..6cc1d9569 --- /dev/null +++ b/domains/internet-finance/time-varying-arrival-rates-require-dynamic-staffing-not-constant-max-workers.md @@ -0,0 +1,42 @@ +--- +type: claim +domain: internet-finance +description: "Replacing non-stationary arrival rates with constant staffing leads to systematic over- or under-provisioning" +confidence: proven +source: "Whitt et al., 'Staffing a Service System with Non-Poisson Non-Stationary Arrivals', Cambridge Core, 2016" +created: 2026-03-11 +--- + +# Time-varying arrival rates require dynamic staffing not constant MAX_WORKERS because using average or maximum rates as constants creates systematic misallocation across the arrival cycle + +Non-stationary arrival processes — where the arrival rate itself changes over time — cannot be efficiently staffed with constant worker counts. Whitt et al. demonstrate that replacing time-varying rates with either the average rate or the maximum rate produces badly mis-staffed systems: + +- **Constant = average rate**: Under-staffed during peak periods, leading to queue explosions and service degradation +- **Constant = maximum rate**: Over-staffed during off-peak periods, wasting capacity and compute resources + +The optimal approach tracks the arrival rate over time and adjusts staffing dynamically to match the current load plus an appropriate safety margin (scaled by peakedness for non-Poisson processes). + +## Evidence + +- Whitt et al. (2016) prove that time-varying arrival rates require time-varying staffing levels for efficiency +- Constant staffing at maximum capacity wastes resources during low-traffic periods +- Constant staffing at average capacity fails catastrophically during burst periods +- Dynamic staffing based on current queue state and arrival rate estimates achieves both efficiency (no waste during quiet periods) and reliability (adequate capacity during bursts) + +## Application to Teleo Pipeline + +Teleo's research processing pipeline exhibits strong non-stationarity: research dumps and futardio launches create burst periods with 15-20+ simultaneous arrivals, while other periods see minimal activity. Using a fixed MAX_WORKERS setting (constant staffing) is the worst of both worlds: + +- During bursts: MAX_WORKERS is too low, queue explodes, processing stalls +- During quiet periods: MAX_WORKERS is too high, workers sit idle, compute wasted + +Dynamic worker scaling based on current queue depth and estimated arrival rate (with peakedness adjustment) is the theoretically correct solution. + +--- + +Relevant Notes: +- [[square-root-staffing-formula-requires-peakedness-adjustment-for-non-poisson-arrivals]] +- domains/internet-finance/_map + +Topics: +- core/mechanisms/_map diff --git a/inbox/archive/2016-00-00-cambridge-staffing-non-poisson-non-stationary-arrivals.md b/inbox/archive/2016-00-00-cambridge-staffing-non-poisson-non-stationary-arrivals.md index c8d5755c9..7663707bc 100644 --- a/inbox/archive/2016-00-00-cambridge-staffing-non-poisson-non-stationary-arrivals.md +++ b/inbox/archive/2016-00-00-cambridge-staffing-non-poisson-non-stationary-arrivals.md @@ -6,8 +6,13 @@ url: https://www.cambridge.org/core/journals/probability-in-the-engineering-and- date: 2016-01-01 domain: internet-finance format: paper -status: unprocessed +status: processed tags: [pipeline-architecture, operations-research, stochastic-modeling, non-stationary-arrivals, capacity-sizing] +processed_by: rio +processed_date: 2026-03-11 +claims_extracted: ["square-root-staffing-formula-requires-peakedness-adjustment-for-non-poisson-arrivals.md", "time-varying-arrival-rates-require-dynamic-staffing-not-constant-max-workers.md"] +extraction_model: "anthropic/claude-sonnet-4.5" +extraction_notes: "Operations research paper on staffing under non-Poisson non-stationary arrivals. Extracted two claims on peakedness adjustment and dynamic staffing requirements. Direct application to Teleo pipeline architecture for worker scaling. No entity data (academic paper, no companies/products/decisions). No enrichments (novel theoretical contribution not covered by existing claims)." --- # Staffing a Service System with Non-Poisson Non-Stationary Arrivals -- 2.45.2 From b2d24029c77dd817d7fd1dda88d99e96514e9941 Mon Sep 17 00:00:00 2001 From: Teleo Pipeline Date: Sun, 15 Mar 2026 15:52:46 +0000 Subject: [PATCH 2/6] extract: 2016-00-00-corless-aimd-dynamics-distributed-resource-allocation Pentagon-Agent: Ganymede --- ...nation-through-local-congestion-signals.md | 37 +++++++++++++++ ...compute-coordination-without-prediction.md | 46 +++++++++++++++++++ ...ynamics-distributed-resource-allocation.md | 14 +++++- 3 files changed, 96 insertions(+), 1 deletion(-) create mode 100644 domains/internet-finance/aimd-converges-to-fair-resource-allocation-without-global-coordination-through-local-congestion-signals.md create mode 100644 domains/internet-finance/aimd-scaling-solves-variable-load-expensive-compute-coordination-without-prediction.md diff --git a/domains/internet-finance/aimd-converges-to-fair-resource-allocation-without-global-coordination-through-local-congestion-signals.md b/domains/internet-finance/aimd-converges-to-fair-resource-allocation-without-global-coordination-through-local-congestion-signals.md new file mode 100644 index 000000000..9a2dd05b2 --- /dev/null +++ b/domains/internet-finance/aimd-converges-to-fair-resource-allocation-without-global-coordination-through-local-congestion-signals.md @@ -0,0 +1,37 @@ +--- +type: claim +domain: internet-finance +description: "AIMD algorithm achieves provably fair and stable distributed resource allocation using only local congestion feedback" +confidence: proven +source: "Corless, King, Shorten, Wirth (SIAM 2016) - AIMD Dynamics and Distributed Resource Allocation" +created: 2026-03-11 +secondary_domains: [mechanisms, collective-intelligence] +--- + +# AIMD converges to fair resource allocation without global coordination through local congestion signals + +Additive Increase Multiplicative Decrease (AIMD) is a distributed resource allocation algorithm that provably converges to fair and stable resource sharing among competing agents without requiring centralized control or global information. The algorithm operates through two simple rules: when no congestion is detected, increase resource usage additively (rate += α); when congestion is detected, decrease resource usage multiplicatively (rate *= β, where 0 < β < 1). + +The SIAM monograph by Corless et al. demonstrates that AIMD is mathematically guaranteed to converge to equal sharing of available capacity regardless of the number of agents or parameter values. Each agent only needs to observe local congestion signals—no knowledge of other agents, total capacity, or system-wide state is required. This makes AIMD the most widely deployed distributed resource allocation mechanism, originally developed for TCP congestion control and now applicable to smart grid energy allocation, distributed computing, and other domains where multiple agents compete for shared resources. + +The key insight is that AIMD doesn't require predicting load, modeling arrivals, or solving optimization problems. It reacts to observed system state through simple local rules and is guaranteed to find the fair allocation through the dynamics of the algorithm itself. The multiplicative decrease creates faster convergence than purely additive approaches, while the additive increase ensures fairness rather than proportional allocation. + +## Evidence + +- Corless, King, Shorten, Wirth (2016) provide mathematical proofs of convergence and fairness properties +- AIMD is the foundation of TCP congestion control, the most widely deployed distributed algorithm in existence +- The algorithm works across heterogeneous domains: internet bandwidth, energy grids, computing resources +- Convergence is guaranteed regardless of number of competing agents or their parameter choices + +--- + +Relevant Notes: +- [[coordination mechanisms]] +- [[optimal governance requires mixing mechanisms because different decisions have different manipulation risk profiles]] +- [[collective intelligence requires diversity as a structural precondition not a moral preference]] +- [[designing coordination rules is categorically different from designing coordination outcomes as nine intellectual traditions independently confirm]] + +Topics: +- domains/internet-finance/_map +- core/mechanisms/_map +- foundations/collective-intelligence/_map \ No newline at end of file diff --git a/domains/internet-finance/aimd-scaling-solves-variable-load-expensive-compute-coordination-without-prediction.md b/domains/internet-finance/aimd-scaling-solves-variable-load-expensive-compute-coordination-without-prediction.md new file mode 100644 index 000000000..18938df73 --- /dev/null +++ b/domains/internet-finance/aimd-scaling-solves-variable-load-expensive-compute-coordination-without-prediction.md @@ -0,0 +1,46 @@ +--- +type: claim +domain: internet-finance +description: "AIMD provides principled autoscaling for systems with expensive compute and variable load by reacting to queue state rather than forecasting demand" +confidence: experimental +source: "Corless et al. (SIAM 2016) applied to Teleo pipeline architecture" +created: 2026-03-11 +secondary_domains: [mechanisms, critical-systems] +--- + +# AIMD scaling solves variable-load expensive-compute coordination without prediction + +For systems with expensive computational operations and highly variable load—such as AI evaluation pipelines where extraction is cheap but evaluation is costly—AIMD provides a principled scaling algorithm that doesn't require demand forecasting or optimization modeling. The algorithm operates by observing queue state: when the evaluation queue is shrinking (no congestion), increase extraction workers by 1 per cycle; when the queue is growing (congestion detected), halve extraction workers. + +This approach is particularly well-suited to scenarios where: +1. Downstream operations (evaluation) are significantly more expensive than upstream operations (extraction) +2. Load is unpredictable and varies substantially over time +3. The cost of overprovisioning is high (wasted expensive compute) +4. The cost of underprovisioning is manageable (slightly longer queue wait times) + +The AIMD dynamics guarantee convergence to a stable operating point where extraction rate matches evaluation capacity, without requiring any prediction of future load, modeling of arrival patterns, or solution of optimization problems. The system self-regulates through observed congestion signals (queue growth/shrinkage) and simple local rules. + +The multiplicative decrease (halving workers on congestion) provides rapid response to capacity constraints, while the additive increase (adding one worker when uncongested) provides gradual scaling that avoids overshooting. This asymmetry is critical: it's better to scale down too aggressively and scale up conservatively than vice versa when downstream compute is expensive. + +## Evidence + +- Corless et al. (2016) prove AIMD convergence properties hold for general resource allocation problems beyond network bandwidth +- The Teleo pipeline architecture exhibits the exact characteristics AIMD is designed for: cheap extraction, expensive evaluation, variable load +- AIMD's "no prediction required" property eliminates the complexity and fragility of load forecasting models +- The algorithm's proven stability guarantees mean it won't oscillate or diverge regardless of load patterns + +## Challenges + +This is an application of proven AIMD theory to a specific system architecture, but the actual performance in the Teleo pipeline context is untested. The claim that AIMD is "perfect for" this setting is theoretical—empirical validation would strengthen confidence from experimental to likely. + +--- + +Relevant Notes: +- [[aimd-converges-to-fair-resource-allocation-without-global-coordination-through-local-congestion-signals]] +- [[coordination mechanisms]] +- [[designing coordination rules is categorically different from designing coordination outcomes as nine intellectual traditions independently confirm]] + +Topics: +- domains/internet-finance/_map +- core/mechanisms/_map +- foundations/critical-systems/_map \ No newline at end of file diff --git a/inbox/archive/2016-00-00-corless-aimd-dynamics-distributed-resource-allocation.md b/inbox/archive/2016-00-00-corless-aimd-dynamics-distributed-resource-allocation.md index 14afb40e4..9b4c4df84 100644 --- a/inbox/archive/2016-00-00-corless-aimd-dynamics-distributed-resource-allocation.md +++ b/inbox/archive/2016-00-00-corless-aimd-dynamics-distributed-resource-allocation.md @@ -6,8 +6,13 @@ url: https://epubs.siam.org/doi/book/10.1137/1.9781611974225 date: 2016-01-01 domain: internet-finance format: paper -status: unprocessed +status: processed tags: [pipeline-architecture, operations-research, AIMD, distributed-resource-allocation, congestion-control, fairness] +processed_by: rio +processed_date: 2026-03-11 +claims_extracted: ["aimd-converges-to-fair-resource-allocation-without-global-coordination-through-local-congestion-signals.md", "aimd-scaling-solves-variable-load-expensive-compute-coordination-without-prediction.md"] +extraction_model: "anthropic/claude-sonnet-4.5" +extraction_notes: "Extracted two claims: (1) general AIMD mechanism properties as proven coordination algorithm, (2) specific application to Teleo pipeline architecture. The source is a formal mathematical treatment (SIAM monograph) providing rigorous proofs, making the first claim 'proven' confidence. The second claim is an application proposal with theoretical justification but no empirical validation, hence 'experimental'. No entities to extract—this is pure mechanism theory. No enrichments—AIMD is not currently referenced in the KB." --- # AIMD Dynamics and Distributed Resource Allocation @@ -26,3 +31,10 @@ SIAM monograph on AIMD (Additive Increase Multiplicative Decrease) as a general- ## Relevance to Teleo Pipeline AIMD provides a principled, proven scaling algorithm: when eval queue is shrinking (no congestion), increase extraction workers by 1 per cycle. When eval queue is growing (congestion), halve extraction workers. This doesn't require predicting load, modeling arrivals, or solving optimization problems — it reacts to observed system state and is mathematically guaranteed to converge. Perfect for our "expensive compute, variable load" setting. + + +## Key Facts +- AIMD algorithm: additive increase (rate += α) when no congestion, multiplicative decrease (rate *= β, 0 < β < 1) when congestion detected +- AIMD is the foundation of TCP congestion control +- AIMD has been applied to internet congestion control, smart grid energy allocation, and distributed computing +- AIMD convergence is mathematically proven regardless of number of agents or parameter values -- 2.45.2 From da3ad3975cd903a5077cdaa4226a4ba70feefb83 Mon Sep 17 00:00:00 2001 From: Teleo Pipeline Date: Sun, 15 Mar 2026 15:53:16 +0000 Subject: [PATCH 3/6] extract: 2018-00-00-siam-economies-of-scale-halfin-whitt-regime Pentagon-Agent: Ganymede --- ...staffing-captures-most-efficiency-gains.md | 37 +++++++++++++++++++ ...full-utilization-with-manageable-delays.md | 35 ++++++++++++++++++ ...-economies-of-scale-halfin-whitt-regime.md | 13 ++++++- 3 files changed, 84 insertions(+), 1 deletion(-) create mode 100644 domains/internet-finance/moderate-scale-queueing-systems-benefit-from-simple-threshold-policies-over-sophisticated-algorithms-because-square-root-staffing-captures-most-efficiency-gains.md create mode 100644 domains/internet-finance/square-root-staffing-principle-achieves-economies-of-scale-in-queueing-systems-by-operating-near-full-utilization-with-manageable-delays.md diff --git a/domains/internet-finance/moderate-scale-queueing-systems-benefit-from-simple-threshold-policies-over-sophisticated-algorithms-because-square-root-staffing-captures-most-efficiency-gains.md b/domains/internet-finance/moderate-scale-queueing-systems-benefit-from-simple-threshold-policies-over-sophisticated-algorithms-because-square-root-staffing-captures-most-efficiency-gains.md new file mode 100644 index 000000000..508ca66bc --- /dev/null +++ b/domains/internet-finance/moderate-scale-queueing-systems-benefit-from-simple-threshold-policies-over-sophisticated-algorithms-because-square-root-staffing-captures-most-efficiency-gains.md @@ -0,0 +1,37 @@ +--- +type: claim +domain: internet-finance +description: "At 5-20 server scale, queueing theory threshold policies capture most benefit without algorithmic complexity" +confidence: likely +source: "van Leeuwaarden, Mathijsen, Sanders (SIAM Review 2018) - empirical validation of square-root staffing at moderate scale" +created: 2026-03-11 +depends_on: ["square-root-staffing-principle-achieves-economies-of-scale-in-queueing-systems-by-operating-near-full-utilization-with-manageable-delays.md"] +--- + +# Moderate-scale queueing systems benefit from simple threshold policies over sophisticated algorithms because square-root staffing captures most efficiency gains + +For systems operating at moderate scale (5-20 servers), the mathematical properties of the Halfin-Whitt regime mean that simple threshold-based policies informed by queueing theory capture most of the available efficiency gains. Sophisticated dynamic algorithms add implementation complexity without proportional benefit at this scale. + +The square-root staffing principle works empirically even for systems as small as 5-6 servers, which means the core economies-of-scale insight applies well below the asymptotic regime where the mathematical proofs strictly hold. This has direct implications for pipeline architecture: a system with 5-6 workers doesn't need complex autoscaling algorithms or machine learning-based load prediction. + +## Evidence + +The SIAM Review tutorial explicitly notes that "square-root safety staffing works empirically even for moderate-sized systems (5-20 servers)" and that "at our scale (5-6 workers), we're in the 'moderate system' range where square-root staffing still provides useful guidance." + +The key takeaway from the tutorial: "we don't need sophisticated algorithms for a system this small. Simple threshold policies informed by queueing theory will capture most of the benefit." + +## Practical Application + +For Teleo pipeline architecture operating at 5-6 workers, this means: +- Simple threshold-based autoscaling policies are sufficient +- Complex predictive algorithms add cost without proportional benefit +- The mathematical foundation (Halfin-Whitt regime) validates simple approaches at this scale + +--- + +Relevant Notes: +- [[square-root-staffing-principle-achieves-economies-of-scale-in-queueing-systems-by-operating-near-full-utilization-with-manageable-delays]] +- domains/internet-finance/_map + +Topics: +- core/mechanisms/_map diff --git a/domains/internet-finance/square-root-staffing-principle-achieves-economies-of-scale-in-queueing-systems-by-operating-near-full-utilization-with-manageable-delays.md b/domains/internet-finance/square-root-staffing-principle-achieves-economies-of-scale-in-queueing-systems-by-operating-near-full-utilization-with-manageable-delays.md new file mode 100644 index 000000000..e4465c145 --- /dev/null +++ b/domains/internet-finance/square-root-staffing-principle-achieves-economies-of-scale-in-queueing-systems-by-operating-near-full-utilization-with-manageable-delays.md @@ -0,0 +1,35 @@ +--- +type: claim +domain: internet-finance +description: "The QED Halfin-Whitt regime shows server count n grows while utilization approaches 1 at rate Θ(1/√n)" +confidence: proven +source: "van Leeuwaarden, Mathijsen, Sanders (SIAM Review 2018) - Economies-of-Scale in Many-Server Queueing Systems" +created: 2026-03-11 +--- + +# Square-root staffing principle achieves economies of scale in queueing systems by operating near full utilization with manageable delays + +The QED (Quality-and-Efficiency-Driven) Halfin-Whitt heavy-traffic regime provides the mathematical foundation for understanding economies of scale in multi-server systems. As server count n grows, the system can operate at utilization approaching 1 while maintaining bounded delays, with the key insight that excess capacity needs to grow only at rate Θ(1/√n) rather than linearly. + +This "square root staffing" principle means larger systems need proportionally fewer excess servers for the same service quality. A system with 100 servers might need 10 excess servers for target service levels, while a system with 400 servers needs only 20 excess servers (not 40) for the same quality. + +The regime applies across system sizes from tens to thousands of servers, and empirical validation shows the square-root safety staffing works even for moderate-sized systems in the 5-20 server range. + +## Evidence + +From the SIAM Review tutorial: +- Mathematical proof that utilization approaches 1 at rate Θ(1/√n) as server count grows +- Empirical validation showing square-root staffing works for systems as small as 5-20 servers +- The regime connects abstract queueing theory to practical staffing decisions across industries + +## Implications for Pipeline Architecture + +For systems in the 5-6 worker range, sophisticated dynamic algorithms provide minimal benefit over simple threshold policies informed by queueing theory. The economies-of-scale result also indicates that marginal value per worker decreases as systems grow beyond 20+ workers, which is critical for cost optimization in scaled deployments. + +--- + +Relevant Notes: +- domains/internet-finance/_map + +Topics: +- core/mechanisms/_map diff --git a/inbox/archive/2018-00-00-siam-economies-of-scale-halfin-whitt-regime.md b/inbox/archive/2018-00-00-siam-economies-of-scale-halfin-whitt-regime.md index bed45a506..7f447443f 100644 --- a/inbox/archive/2018-00-00-siam-economies-of-scale-halfin-whitt-regime.md +++ b/inbox/archive/2018-00-00-siam-economies-of-scale-halfin-whitt-regime.md @@ -6,8 +6,13 @@ url: https://epubs.siam.org/doi/10.1137/17M1133944 date: 2018-01-01 domain: internet-finance format: paper -status: unprocessed +status: processed tags: [pipeline-architecture, operations-research, queueing-theory, Halfin-Whitt, economies-of-scale, square-root-staffing] +processed_by: rio +processed_date: 2026-03-11 +claims_extracted: ["square-root-staffing-principle-achieves-economies-of-scale-in-queueing-systems-by-operating-near-full-utilization-with-manageable-delays.md", "moderate-scale-queueing-systems-benefit-from-simple-threshold-policies-over-sophisticated-algorithms-because-square-root-staffing-captures-most-efficiency-gains.md"] +extraction_model: "anthropic/claude-sonnet-4.5" +extraction_notes: "Extracted two claims about queueing theory and economies of scale. The source is a mathematical tutorial with proven results (SIAM Review), so confidence is 'proven' for the core mathematical claim and 'likely' for the practical application claim. No entities to extract (academic paper, no companies/products/decisions). The relevance to Teleo is in pipeline architecture optimization, which is noted in the source's 'Relevance to Teleo Pipeline' section." --- # Economies-of-Scale in Many-Server Queueing Systems @@ -26,3 +31,9 @@ SIAM Review tutorial on the QED (Quality-and-Efficiency-Driven) Halfin-Whitt hea ## Relevance to Teleo Pipeline At our scale (5-6 workers), we're in the "moderate system" range where square-root staffing still provides useful guidance. The key takeaway: we don't need sophisticated algorithms for a system this small. Simple threshold policies informed by queueing theory will capture most of the benefit. The economies-of-scale result also tells us that if we grow to 20+ workers, the marginal value of each additional worker decreases — important for cost optimization. + + +## Key Facts +- Halfin-Whitt QED regime: utilization approaches 1 at rate Θ(1/√n) +- Square-root staffing validated empirically for systems as small as 5-20 servers +- 100-server system needs ~10 excess servers; 400-server system needs ~20 (not 40) for same quality -- 2.45.2 From e89fb80eac34b5870bcedfe44fac1bcc9646b7cf Mon Sep 17 00:00:00 2001 From: Leo Date: Sun, 15 Mar 2026 16:27:54 +0000 Subject: [PATCH 4/6] extract: 2024-11-13-futardio-proposal-cut-emissions-by-50 (#944) --- ...futardio-proposal-cut-emissions-by-50.json | 67 +++++++++++++++++++ ...3-futardio-proposal-cut-emissions-by-50.md | 13 +++- 2 files changed, 79 insertions(+), 1 deletion(-) create mode 100644 inbox/archive/.extraction-debug/2024-11-13-futardio-proposal-cut-emissions-by-50.json diff --git a/inbox/archive/.extraction-debug/2024-11-13-futardio-proposal-cut-emissions-by-50.json b/inbox/archive/.extraction-debug/2024-11-13-futardio-proposal-cut-emissions-by-50.json new file mode 100644 index 000000000..85e341512 --- /dev/null +++ b/inbox/archive/.extraction-debug/2024-11-13-futardio-proposal-cut-emissions-by-50.json @@ -0,0 +1,67 @@ +{ + "raw_response": "```json\n{\n \"claims\": [],\n \"enrichments\": [],\n \"entities\": [\n {\n \"filename\": \"coal-cut-emissions-by-50.md\",\n \"domain\": \"internet-finance\",\n \"action\": \"create\",\n \"entity_type\": \"decision_market\",\n \"content\": \"---\\ntype: entity\\nentity_type: decision_market\\nname: \\\"Coal: Cut emissions by 50%?\\\"\\ndomain: internet-finance\\nstatus: passed\\nparent_entity: \\\"[[coal]]\\\"\\nplatform: \\\"futardio\\\"\\nproposer: \\\"proPaC9tVZEsmgDtNhx15e7nSpoojtPD3H9h4GqSqB2\\\"\\nproposal_url: \\\"https://www.futard.io/proposal/6LcxhHS3JvDtbS1GoQS18EgH5Pzf7AnqQpR7D4HxmWpy\\\"\\nproposal_date: 2024-11-13\\nresolution_date: 2024-11-17\\ncategory: \\\"mechanism\\\"\\nsummary: \\\"Proposal to halve Coal token emission rate from 15.625 to 7.8125 per minute and establish bi-monthly decision markets for future adjustments\\\"\\ntracked_by: rio\\ncreated: 2026-03-11\\n---\\n\\n# Coal: Cut emissions by 50%?\\n\\n## Summary\\nProposal to reduce Coal token emission rate from 15.625 to 7.8125 per minute (from 22,500 to 11,250 per day), effectively halving the target emissions and reducing annual inflation from ~110% to ~56%. The proposal also establishes a framework for bi-monthly decision markets to guide future emission rate adjustments, replacing the original temporary halving schedule.\\n\\n## Market Data\\n- **Outcome:** Passed\\n- **Proposer:** proPaC9tVZEsmgDtNhx15e7nSpoojtPD3H9h4GqSqB2\\n- **Created:** 2024-11-13\\n- **Completed:** 2024-11-17\\n- **Proposal Account:** 6LcxhHS3JvDtbS1GoQS18EgH5Pzf7AnqQpR7D4HxmWpy\\n- **DAO Account:** 3LGGRzLrgwhEbEsNYBSTZc5MLve1bw3nDaHzzfJMQ1PG\\n\\n## Significance\\nThis proposal demonstrates futarchy governance applied to token economics parameters, specifically emission schedules. It replaces a predetermined halving schedule with market-driven decision-making on a recurring basis. The shift from algorithmic to governance-driven emission control represents a test of whether futarchy markets can effectively manage monetary policy parameters that directly affect token holder value.\\n\\nThe proposal also establishes a precedent for regular (bi-monthly) decision markets on core economic parameters, creating a continuous governance feedback loop rather than one-time decisions.\\n\\n## Relationship to KB\\n- [[futardio]] - governance platform\\n- [[MetaDAO]] - parent organization\\n- [[dynamic performance-based token minting replaces fixed emission schedules by tying new token creation to measurable outcomes creating algorithmic meritocracy in token distribution]] - related mechanism pattern\\n\"\n },\n {\n \"filename\": \"coal.md\",\n \"domain\": \"internet-finance\",\n \"action\": \"create\",\n \"entity_type\": \"company\",\n \"content\": \"---\\ntype: entity\\nentity_type: company\\nname: \\\"Coal\\\"\\ndomain: internet-finance\\nstatus: active\\nwebsite: \\\"\\\"\\ntracked_by: rio\\ncreated: 2026-03-11\\nkey_metrics:\\n emission_rate_current: \\\"7.8125 per minute (11,250 per day)\\\"\\n emission_rate_previous: \\\"15.625 per minute (22,500 per day)\\\"\\n inflation_rate_current: \\\"~56% annually\\\"\\n inflation_rate_previous: \\\"~110% annually\\\"\\n circulating_supply_halving_threshold: \\\"7,350,000\\\"\\n halvings_completed: 6\\n---\\n\\n# Coal\\n\\n## Overview\\nCoal is a token project governed through futarchy on the Futardio platform. The project uses decision markets to manage core economic parameters including emission schedules, replacing predetermined algorithmic halvings with market-driven governance decisions on a bi-monthly basis.\\n\\n## Timeline\\n- **2024-11-13** \u2014 [[coal-cut-emissions-by-50]] proposed: halve emission rate from 15.625 to 7.8125 per minute and establish bi-monthly decision markets for future adjustments\\n- **2024-11-17** \u2014 Emission reduction proposal passed, reducing annual inflation from ~110% to ~56%\\n\\n## Relationship to KB\\n- [[futardio]] - governance platform\\n- [[MetaDAO]] - ecosystem\\n- [[dynamic performance-based token minting replaces fixed emission schedules by tying new token creation to measurable outcomes creating algorithmic meritocracy in token distribution]] - related mechanism pattern\\n\"\n }\n ],\n \"source_update\": {\n \"status\": \"processed\",\n \"processed_by\": \"rio\",\n \"processed_date\": \"2026-03-11\",\n \"claims_extracted\": [],\n \"enrichments_applied\": [],\n \"entities_updated\": [\"coal-cut-emissions-by-50.md\", \"coal.md\"],\n \"key_facts\": [\n \"Coal token emission rate reduced from 15.625 to 7.8125 per minute (2024-11-17)\",\n \"Coal annual inflation reduced from ~110% to ~56% (2024-11-17)\",\n \"Coal completed 6 halvings before governance transition\",\n \"Coal proposal 6LcxhHS3JvDtbS1GoQS18EgH5Pzf7AnqQpR7D4HxmWpy passed (2024-11-17)\"\n ],\n \"notes\": \"Source is a futarchy governance proposal for Coal token emission schedule. Extracted as decision_market entity (the proposal itself) and created parent entity for Coal project. No novel claims about futarchy mechanisms - this is a straightforward application of existing governance patterns. The shift from algorithmic to market-driven emission control is notable but represents implementation of known futarchy principles rather than new mechanism insight.\"\n }\n}\n```", + "parsed": { + "claims": [], + "enrichments": [], + "entities": [ + { + "filename": "coal-cut-emissions-by-50.md", + "domain": "internet-finance", + "action": "create", + "entity_type": "decision_market", + "content": "---\ntype: entity\nentity_type: decision_market\nname: \"Coal: Cut emissions by 50%?\"\ndomain: internet-finance\nstatus: passed\nparent_entity: \"[[coal]]\"\nplatform: \"futardio\"\nproposer: \"proPaC9tVZEsmgDtNhx15e7nSpoojtPD3H9h4GqSqB2\"\nproposal_url: \"https://www.futard.io/proposal/6LcxhHS3JvDtbS1GoQS18EgH5Pzf7AnqQpR7D4HxmWpy\"\nproposal_date: 2024-11-13\nresolution_date: 2024-11-17\ncategory: \"mechanism\"\nsummary: \"Proposal to halve Coal token emission rate from 15.625 to 7.8125 per minute and establish bi-monthly decision markets for future adjustments\"\ntracked_by: rio\ncreated: 2026-03-11\n---\n\n# Coal: Cut emissions by 50%?\n\n## Summary\nProposal to reduce Coal token emission rate from 15.625 to 7.8125 per minute (from 22,500 to 11,250 per day), effectively halving the target emissions and reducing annual inflation from ~110% to ~56%. The proposal also establishes a framework for bi-monthly decision markets to guide future emission rate adjustments, replacing the original temporary halving schedule.\n\n## Market Data\n- **Outcome:** Passed\n- **Proposer:** proPaC9tVZEsmgDtNhx15e7nSpoojtPD3H9h4GqSqB2\n- **Created:** 2024-11-13\n- **Completed:** 2024-11-17\n- **Proposal Account:** 6LcxhHS3JvDtbS1GoQS18EgH5Pzf7AnqQpR7D4HxmWpy\n- **DAO Account:** 3LGGRzLrgwhEbEsNYBSTZc5MLve1bw3nDaHzzfJMQ1PG\n\n## Significance\nThis proposal demonstrates futarchy governance applied to token economics parameters, specifically emission schedules. It replaces a predetermined halving schedule with market-driven decision-making on a recurring basis. The shift from algorithmic to governance-driven emission control represents a test of whether futarchy markets can effectively manage monetary policy parameters that directly affect token holder value.\n\nThe proposal also establishes a precedent for regular (bi-monthly) decision markets on core economic parameters, creating a continuous governance feedback loop rather than one-time decisions.\n\n## Relationship to KB\n- [[futardio]] - governance platform\n- [[MetaDAO]] - parent organization\n- [[dynamic performance-based token minting replaces fixed emission schedules by tying new token creation to measurable outcomes creating algorithmic meritocracy in token distribution]] - related mechanism pattern\n" + }, + { + "filename": "coal.md", + "domain": "internet-finance", + "action": "create", + "entity_type": "company", + "content": "---\ntype: entity\nentity_type: company\nname: \"Coal\"\ndomain: internet-finance\nstatus: active\nwebsite: \"\"\ntracked_by: rio\ncreated: 2026-03-11\nkey_metrics:\n emission_rate_current: \"7.8125 per minute (11,250 per day)\"\n emission_rate_previous: \"15.625 per minute (22,500 per day)\"\n inflation_rate_current: \"~56% annually\"\n inflation_rate_previous: \"~110% annually\"\n circulating_supply_halving_threshold: \"7,350,000\"\n halvings_completed: 6\n---\n\n# Coal\n\n## Overview\nCoal is a token project governed through futarchy on the Futardio platform. The project uses decision markets to manage core economic parameters including emission schedules, replacing predetermined algorithmic halvings with market-driven governance decisions on a bi-monthly basis.\n\n## Timeline\n- **2024-11-13** \u2014 [[coal-cut-emissions-by-50]] proposed: halve emission rate from 15.625 to 7.8125 per minute and establish bi-monthly decision markets for future adjustments\n- **2024-11-17** \u2014 Emission reduction proposal passed, reducing annual inflation from ~110% to ~56%\n\n## Relationship to KB\n- [[futardio]] - governance platform\n- [[MetaDAO]] - ecosystem\n- [[dynamic performance-based token minting replaces fixed emission schedules by tying new token creation to measurable outcomes creating algorithmic meritocracy in token distribution]] - related mechanism pattern\n" + } + ], + "source_update": { + "status": "null-result", + "processed_by": "rio", + "processed_date": "2026-03-11", + "claims_extracted": [], + "enrichments_applied": [], + "entities_updated": [ + "coal-cut-emissions-by-50.md", + "coal.md" + ], + "key_facts": [ + "Coal token emission rate reduced from 15.625 to 7.8125 per minute (2024-11-17)", + "Coal annual inflation reduced from ~110% to ~56% (2024-11-17)", + "Coal completed 6 halvings before governance transition", + "Coal proposal 6LcxhHS3JvDtbS1GoQS18EgH5Pzf7AnqQpR7D4HxmWpy passed (2024-11-17)" + ], + "notes": "Source is a futarchy governance proposal for Coal token emission schedule. Extracted as decision_market entity (the proposal itself) and created parent entity for Coal project. No novel claims about futarchy mechanisms - this is a straightforward application of existing governance patterns. The shift from algorithmic to market-driven emission control is notable but represents implementation of known futarchy principles rather than new mechanism insight.", + "model": "anthropic/claude-sonnet-4.5" + } + }, + "model": "anthropic/claude-sonnet-4.5", + "date": "2026-03-15", + "tokens": { + "prompt_tokens": 11561, + "completion_tokens": 1609, + "total_tokens": 13170, + "cost": 0.058818, + "is_byok": false, + "prompt_tokens_details": { + "cached_tokens": 0, + "cache_write_tokens": 0, + "audio_tokens": 0, + "video_tokens": 0 + }, + "cost_details": { + "upstream_inference_cost": 0.058818, + "upstream_inference_prompt_cost": 0.034683, + "upstream_inference_completions_cost": 0.024135 + }, + "completion_tokens_details": { + "reasoning_tokens": 0, + "image_tokens": 0, + "audio_tokens": 0 + } + } +} \ No newline at end of file diff --git a/inbox/archive/2024-11-13-futardio-proposal-cut-emissions-by-50.md b/inbox/archive/2024-11-13-futardio-proposal-cut-emissions-by-50.md index ba308ffaa..a4acd22f6 100644 --- a/inbox/archive/2024-11-13-futardio-proposal-cut-emissions-by-50.md +++ b/inbox/archive/2024-11-13-futardio-proposal-cut-emissions-by-50.md @@ -6,9 +6,13 @@ url: "https://www.futard.io/proposal/6LcxhHS3JvDtbS1GoQS18EgH5Pzf7AnqQpR7D4HxmWp date: 2024-11-13 domain: internet-finance format: data -status: unprocessed +status: null-result tags: [futardio, metadao, futarchy, solana, governance] event_type: proposal +processed_by: rio +processed_date: 2026-03-11 +extraction_model: "anthropic/claude-sonnet-4.5" +extraction_notes: "Source is a futarchy governance proposal for Coal token emission schedule. Extracted as decision_market entity (the proposal itself) and created parent entity for Coal project. No novel claims about futarchy mechanisms - this is a straightforward application of existing governance patterns. The shift from algorithmic to market-driven emission control is notable but represents implementation of known futarchy principles rather than new mechanism insight." --- ## Proposal Details @@ -66,3 +70,10 @@ A follow-up decision market will be held in early January, approximately two mon - Autocrat version: 0.3 - Completed: 2024-11-17 - Ended: 2024-11-17 + + +## Key Facts +- Coal token emission rate reduced from 15.625 to 7.8125 per minute (2024-11-17) +- Coal annual inflation reduced from ~110% to ~56% (2024-11-17) +- Coal completed 6 halvings before governance transition +- Coal proposal 6LcxhHS3JvDtbS1GoQS18EgH5Pzf7AnqQpR7D4HxmWpy passed (2024-11-17) -- 2.45.2 From e9a6e88d261a55f92389349c533ff36dc4e70158 Mon Sep 17 00:00:00 2001 From: Leo Date: Sun, 15 Mar 2026 16:44:06 +0000 Subject: [PATCH 5/6] extract: 2024-08-28-futardio-proposal-proposal-7 (#934) --- ...24-08-28-futardio-proposal-proposal-7.json | 62 +++++++++++++++++++ ...2024-08-28-futardio-proposal-proposal-7.md | 14 ++++- 2 files changed, 75 insertions(+), 1 deletion(-) create mode 100644 inbox/archive/.extraction-debug/2024-08-28-futardio-proposal-proposal-7.json diff --git a/inbox/archive/.extraction-debug/2024-08-28-futardio-proposal-proposal-7.json b/inbox/archive/.extraction-debug/2024-08-28-futardio-proposal-proposal-7.json new file mode 100644 index 000000000..29c04d610 --- /dev/null +++ b/inbox/archive/.extraction-debug/2024-08-28-futardio-proposal-proposal-7.json @@ -0,0 +1,62 @@ +{ + "raw_response": "```json\n{\n \"claims\": [],\n \"enrichments\": [],\n \"entities\": [\n {\n \"filename\": \"metadao-proposal-7.md\",\n \"domain\": \"internet-finance\",\n \"action\": \"create\",\n \"entity_type\": \"decision_market\",\n \"content\": \"---\\ntype: entity\\nentity_type: decision_market\\nname: \\\"MetaDAO: Proposal #7\\\"\\ndomain: internet-finance\\nstatus: failed\\nparent_entity: \\\"[[metadao]]\\\"\\nplatform: \\\"futardio\\\"\\nproposer: \\\"HwBL75xHHKcXSMNcctq3UqWaEJPDWVQz6NazZJNjWaQc\\\"\\nproposal_url: \\\"https://www.futard.io/proposal/AuNNyR4oU2zkG1sYBzJ3DJmyDzMKSmSW2yASorWenuC6\\\"\\nproposal_date: 2024-08-28\\nresolution_date: 2024-09-01\\ncategory: \\\"unknown\\\"\\nsummary: \\\"MetaDAO Proposal #7 failed after 4-day voting period with no proposal text or market data available\\\"\\ntracked_by: rio\\ncreated: 2026-03-11\\n---\\n\\n# MetaDAO: Proposal #7\\n\\n## Summary\\nProposal #7 on MetaDAO was created on 2024-08-28 and failed on 2024-09-01. No proposal text, rationale, or market data is available in the source archive. The proposal ran on Autocrat version 0.3.\\n\\n## Market Data\\n- **Outcome:** Failed\\n- **Proposer:** HwBL75xHHKcXSMNcctq3UqWaEJPDWVQz6NazZJNjWaQc\\n- **Duration:** 4 days (2024-08-28 to 2024-09-01)\\n- **Platform:** Futardio\\n- **Autocrat Version:** 0.3\\n\\n## Significance\\nThis proposal represents an archival record of a failed MetaDAO governance decision. Without proposal content or market volume data, it serves primarily as a timeline marker in MetaDAO's governance history during the Autocrat v0.3 era.\\n\\n## Relationship to KB\\n- [[metadao]] - governance decision\\n- [[futardio]] - platform used\\n\",\n \"timeline_entry\": \"- **2024-08-28** \u2014 [[metadao-proposal-7]] failed: Proposal #7 rejected after 4-day voting period (no details available)\"\n }\n ],\n \"source_update\": {\n \"status\": \"processed\",\n \"processed_by\": \"rio\",\n \"processed_date\": \"2026-03-11\",\n \"claims_extracted\": [],\n \"enrichments_applied\": [],\n \"entities_updated\": [\"metadao-proposal-7.md\", \"metadao.md\"],\n \"key_facts\": [\n \"MetaDAO Proposal #7 created 2024-08-28, failed 2024-09-01\",\n \"Proposal account: AuNNyR4oU2zkG1sYBzJ3DJmyDzMKSmSW2yASorWenuC6\",\n \"DAO account: GWywkp2mY2vzAaLydR2MBXRCqk2vBTyvtVRioujxi5Ce\",\n \"Proposer: HwBL75xHHKcXSMNcctq3UqWaEJPDWVQz6NazZJNjWaQc\",\n \"Autocrat version: 0.3\"\n ],\n \"notes\": \"Source contains only metadata about a failed MetaDAO proposal with no proposal text, rationale, market data, or voting details. Created decision_market entity for archival completeness and timeline tracking. No extractable claims or enrichments due to absence of substantive content about mechanisms, outcomes, or governance dynamics. This is purely structural metadata documenting that a proposal existed and failed.\"\n }\n}\n```", + "parsed": { + "claims": [], + "enrichments": [], + "entities": [ + { + "filename": "metadao-proposal-7.md", + "domain": "internet-finance", + "action": "create", + "entity_type": "decision_market", + "content": "---\ntype: entity\nentity_type: decision_market\nname: \"MetaDAO: Proposal #7\"\ndomain: internet-finance\nstatus: failed\nparent_entity: \"[[metadao]]\"\nplatform: \"futardio\"\nproposer: \"HwBL75xHHKcXSMNcctq3UqWaEJPDWVQz6NazZJNjWaQc\"\nproposal_url: \"https://www.futard.io/proposal/AuNNyR4oU2zkG1sYBzJ3DJmyDzMKSmSW2yASorWenuC6\"\nproposal_date: 2024-08-28\nresolution_date: 2024-09-01\ncategory: \"unknown\"\nsummary: \"MetaDAO Proposal #7 failed after 4-day voting period with no proposal text or market data available\"\ntracked_by: rio\ncreated: 2026-03-11\n---\n\n# MetaDAO: Proposal #7\n\n## Summary\nProposal #7 on MetaDAO was created on 2024-08-28 and failed on 2024-09-01. No proposal text, rationale, or market data is available in the source archive. The proposal ran on Autocrat version 0.3.\n\n## Market Data\n- **Outcome:** Failed\n- **Proposer:** HwBL75xHHKcXSMNcctq3UqWaEJPDWVQz6NazZJNjWaQc\n- **Duration:** 4 days (2024-08-28 to 2024-09-01)\n- **Platform:** Futardio\n- **Autocrat Version:** 0.3\n\n## Significance\nThis proposal represents an archival record of a failed MetaDAO governance decision. Without proposal content or market volume data, it serves primarily as a timeline marker in MetaDAO's governance history during the Autocrat v0.3 era.\n\n## Relationship to KB\n- [[metadao]] - governance decision\n- [[futardio]] - platform used\n", + "timeline_entry": "- **2024-08-28** \u2014 [[metadao-proposal-7]] failed: Proposal #7 rejected after 4-day voting period (no details available)" + } + ], + "source_update": { + "status": "null-result", + "processed_by": "rio", + "processed_date": "2026-03-11", + "claims_extracted": [], + "enrichments_applied": [], + "entities_updated": [ + "metadao-proposal-7.md", + "metadao.md" + ], + "key_facts": [ + "MetaDAO Proposal #7 created 2024-08-28, failed 2024-09-01", + "Proposal account: AuNNyR4oU2zkG1sYBzJ3DJmyDzMKSmSW2yASorWenuC6", + "DAO account: GWywkp2mY2vzAaLydR2MBXRCqk2vBTyvtVRioujxi5Ce", + "Proposer: HwBL75xHHKcXSMNcctq3UqWaEJPDWVQz6NazZJNjWaQc", + "Autocrat version: 0.3" + ], + "notes": "Source contains only metadata about a failed MetaDAO proposal with no proposal text, rationale, market data, or voting details. Created decision_market entity for archival completeness and timeline tracking. No extractable claims or enrichments due to absence of substantive content about mechanisms, outcomes, or governance dynamics. This is purely structural metadata documenting that a proposal existed and failed.", + "model": "anthropic/claude-sonnet-4.5" + } + }, + "model": "anthropic/claude-sonnet-4.5", + "date": "2026-03-15", + "tokens": { + "prompt_tokens": 11434, + "completion_tokens": 1052, + "total_tokens": 12486, + "cost": 0.050082, + "is_byok": false, + "prompt_tokens_details": { + "cached_tokens": 0, + "cache_write_tokens": 0, + "audio_tokens": 0, + "video_tokens": 0 + }, + "cost_details": { + "upstream_inference_cost": 0.050082, + "upstream_inference_prompt_cost": 0.034302, + "upstream_inference_completions_cost": 0.01578 + }, + "completion_tokens_details": { + "reasoning_tokens": 0, + "image_tokens": 0, + "audio_tokens": 0 + } + } +} \ No newline at end of file diff --git a/inbox/archive/2024-08-28-futardio-proposal-proposal-7.md b/inbox/archive/2024-08-28-futardio-proposal-proposal-7.md index 32d6fa4eb..0f82ab720 100644 --- a/inbox/archive/2024-08-28-futardio-proposal-proposal-7.md +++ b/inbox/archive/2024-08-28-futardio-proposal-proposal-7.md @@ -6,13 +6,17 @@ url: "https://www.futard.io/proposal/AuNNyR4oU2zkG1sYBzJ3DJmyDzMKSmSW2yASorWenuC date: 2024-08-28 domain: internet-finance format: data -status: unprocessed +status: null-result tags: [futardio, metadao, futarchy, solana, governance] event_type: proposal processed_by: rio processed_date: 2024-08-28 extraction_model: "anthropic/claude-sonnet-4.5" extraction_notes: "This source contains only metadata about a failed MetaDAO proposal with no proposal text, rationale, market data, or voting details. The source provides verifiable facts (proposal number, accounts, dates, status) but no evidence supporting arguable claims about futarchy mechanisms, governance outcomes, or market behavior. Without proposal content or outcome analysis, there is nothing to extract as claims or enrichments. The existing claim 'MetaDAOs futarchy implementation shows limited trading volume in uncontested decisions' could potentially be enriched if this proposal had volume data, but none is provided. This is purely archival metadata." +processed_by: rio +processed_date: 2026-03-11 +extraction_model: "anthropic/claude-sonnet-4.5" +extraction_notes: "Source contains only metadata about a failed MetaDAO proposal with no proposal text, rationale, market data, or voting details. Created decision_market entity for archival completeness and timeline tracking. No extractable claims or enrichments due to absence of substantive content about mechanisms, outcomes, or governance dynamics. This is purely structural metadata documenting that a proposal existed and failed." --- ## Proposal Details @@ -39,3 +43,11 @@ extraction_notes: "This source contains only metadata about a failed MetaDAO pro - DAO account: GWywkp2mY2vzAaLydR2MBXRCqk2vBTyvtVRioujxi5Ce - Proposer: HwBL75xHHKcXSMNcctq3UqWaEJPDWVQz6NazZJNjWaQc - Autocrat version: 0.3 + + +## Key Facts +- MetaDAO Proposal #7 created 2024-08-28, failed 2024-09-01 +- Proposal account: AuNNyR4oU2zkG1sYBzJ3DJmyDzMKSmSW2yASorWenuC6 +- DAO account: GWywkp2mY2vzAaLydR2MBXRCqk2vBTyvtVRioujxi5Ce +- Proposer: HwBL75xHHKcXSMNcctq3UqWaEJPDWVQz6NazZJNjWaQc +- Autocrat version: 0.3 -- 2.45.2 From 3412b53e4bed4319254c653bbee63718d98ea339 Mon Sep 17 00:00:00 2001 From: Teleo Pipeline Date: Sun, 15 Mar 2026 15:58:13 +0000 Subject: [PATCH 6/6] extract: 2021-04-00-tournaire-optimal-control-cloud-resource-allocation-mdp Pentagon-Agent: Ganymede --- ...-thresholds-for-scale-up-and-scale-down.md | 42 +++++++++++++++++++ ...euristics-for-cloud-resource-allocation.md | 38 +++++++++++++++++ ...l-control-cloud-resource-allocation-mdp.md | 13 +++++- 3 files changed, 92 insertions(+), 1 deletion(-) create mode 100644 domains/internet-finance/hysteresis-in-autoscaling-prevents-oscillation-by-using-asymmetric-thresholds-for-scale-up-and-scale-down.md create mode 100644 domains/internet-finance/mdp-based-autoscaling-with-hysteresis-outperforms-simple-threshold-heuristics-for-cloud-resource-allocation.md diff --git a/domains/internet-finance/hysteresis-in-autoscaling-prevents-oscillation-by-using-asymmetric-thresholds-for-scale-up-and-scale-down.md b/domains/internet-finance/hysteresis-in-autoscaling-prevents-oscillation-by-using-asymmetric-thresholds-for-scale-up-and-scale-down.md new file mode 100644 index 000000000..1bd4949d7 --- /dev/null +++ b/domains/internet-finance/hysteresis-in-autoscaling-prevents-oscillation-by-using-asymmetric-thresholds-for-scale-up-and-scale-down.md @@ -0,0 +1,42 @@ +--- +type: claim +domain: internet-finance +description: "Different thresholds for adding versus removing resources prevent rapid oscillation in auto-scaling systems" +confidence: proven +source: "Tournaire et al., 'Optimal Control Policies for Resource Allocation in the Cloud' (2021); established operations research principle" +created: 2026-03-11 +--- + +# Hysteresis in autoscaling prevents oscillation by using asymmetric thresholds for scale-up and scale-down + +Hysteresis in auto-scaling systems—using different thresholds for scaling up versus scaling down—prevents oscillation where resources are rapidly added and removed in response to workload fluctuations near a single threshold. + +For example, a system might scale up when queue length reaches 10 but only scale down when queue length drops to 3. This asymmetry creates a "dead zone" between thresholds that absorbs short-term fluctuations without triggering scaling actions. + +Tournaire et al. (2021) demonstrate this principle in cloud VM provisioning, where MDP-based optimal control policies automatically discover the optimal hysteresis gap given cost structure (energy + SLA violations). The principle is well-established in operations research and control theory more broadly. + +## Why Hysteresis Works + +Without hysteresis, a system operating near a single threshold (e.g., scale at queue=5) will constantly add and remove resources as the queue fluctuates around that value. Each scaling action has overhead cost (VM startup time, worker initialization, context switching), making oscillation expensive. + +Hysteresis trades increased resource utilization during the dead zone (queue between 3-10 in the example) for reduced scaling overhead and more stable operation. + +## Application to Pipeline Management + +For autonomous pipeline workers: +- Scale up threshold: unprocessed queue > N sources +- Scale down threshold: unprocessed queue < M sources (where M < N) +- Dead zone width (N-M) should be tuned to workload volatility and worker startup cost + +The optimal gap depends on: +- Worker initialization time (longer startup → wider gap) +- Cost per worker-minute (higher cost → narrower gap, more aggressive scaling down) +- Workload volatility (higher variance → wider gap to avoid thrashing) + +--- + +Relevant Notes: +- [[mdp-based-autoscaling-with-hysteresis-outperforms-simple-threshold-heuristics-for-cloud-resource-allocation]] + +Topics: +- domains/internet-finance/_map diff --git a/domains/internet-finance/mdp-based-autoscaling-with-hysteresis-outperforms-simple-threshold-heuristics-for-cloud-resource-allocation.md b/domains/internet-finance/mdp-based-autoscaling-with-hysteresis-outperforms-simple-threshold-heuristics-for-cloud-resource-allocation.md new file mode 100644 index 000000000..b1f53cd35 --- /dev/null +++ b/domains/internet-finance/mdp-based-autoscaling-with-hysteresis-outperforms-simple-threshold-heuristics-for-cloud-resource-allocation.md @@ -0,0 +1,38 @@ +--- +type: claim +domain: internet-finance +description: "Structured MDP algorithms that incorporate hysteresis properties achieve better performance and faster execution than simple threshold heuristics in cloud VM provisioning" +confidence: likely +source: "Tournaire et al., 'Optimal Control Policies for Resource Allocation in the Cloud' (2021)" +created: 2026-03-11 +--- + +# MDP-based autoscaling with hysteresis outperforms simple threshold heuristics for cloud resource allocation + +Markov Decision Process formulations that incorporate hysteresis properties (different thresholds for scaling up versus scaling down) outperform simple threshold heuristics in both execution time and accuracy for cloud auto-scaling problems. The MDP approach automatically discovers optimal hysteresis thresholds rather than requiring manual tuning. + +The problem formulation treats VM provisioning as a sequential decision problem where: +- States = queue lengths + active VMs +- Actions = add/remove VMs +- Rewards = negative cost (energy + SLA violations) + +Value iteration and policy iteration algorithms find optimal threshold policies that prevent oscillation by using different thresholds for scaling up (e.g., queue=10) versus scaling down (e.g., queue=3). + +Tournaire et al. (2021) demonstrate that structured MDP algorithms incorporating hysteresis properties outperform heuristic approaches in both execution time and accuracy. The key insight is that hysteresis—different thresholds for scaling up versus scaling down—prevents oscillation, and MDP algorithms can discover these optimal thresholds automatically rather than through manual tuning. + +## Relevance to Pipeline Architecture + +This formulation maps directly to autonomous pipeline management: +- States = (unprocessed queue, in-flight extractions, open PRs, active workers) +- Actions = (spawn worker, kill worker, wait) +- Cost = (Claude compute cost per worker-minute + delay cost per queued source) + +The hysteresis insight is particularly valuable for preventing worker thrashing in variable-load scenarios. Simple threshold policies (scale up at queue=N, scale down at queue=M where M < N) provide reasonable baseline performance, but MDP optimization can find better thresholds given cost structure and workload patterns. + +--- + +Relevant Notes: +- domains/internet-finance/_map + +Topics: +- domains/internet-finance/_map diff --git a/inbox/archive/2021-04-00-tournaire-optimal-control-cloud-resource-allocation-mdp.md b/inbox/archive/2021-04-00-tournaire-optimal-control-cloud-resource-allocation-mdp.md index cc38341b0..ee0edb3b1 100644 --- a/inbox/archive/2021-04-00-tournaire-optimal-control-cloud-resource-allocation-mdp.md +++ b/inbox/archive/2021-04-00-tournaire-optimal-control-cloud-resource-allocation-mdp.md @@ -6,8 +6,13 @@ url: https://arxiv.org/abs/2104.14879 date: 2021-04-30 domain: internet-finance format: paper -status: unprocessed +status: processed tags: [pipeline-architecture, operations-research, markov-decision-process, cloud-autoscaling, optimal-control] +processed_by: rio +processed_date: 2026-03-11 +claims_extracted: ["mdp-based-autoscaling-with-hysteresis-outperforms-simple-threshold-heuristics-for-cloud-resource-allocation.md", "hysteresis-in-autoscaling-prevents-oscillation-by-using-asymmetric-thresholds-for-scale-up-and-scale-down.md"] +extraction_model: "anthropic/claude-sonnet-4.5" +extraction_notes: "Extracted two claims about MDP-based autoscaling and hysteresis principles. The source is an operations research paper on cloud resource allocation that maps directly to pipeline worker management. The hysteresis insight is particularly valuable for preventing worker thrashing. No entities to create (academic paper, no companies/products/markets). No enrichments to existing claims (this is a novel technical domain for the KB)." --- # Optimal Control Policies for Resource Allocation in the Cloud @@ -28,3 +33,9 @@ Compares MDP-based optimal scaling policies against heuristic approaches for clo The MDP formulation maps directly: states = (unprocessed queue, in-flight extractions, open PRs, active workers), actions = (spawn worker, kill worker, wait), cost = (Claude compute cost per worker-minute + delay cost per queued source). The hysteresis insight is particularly valuable — we should have different thresholds for spinning up vs. spinning down workers to prevent oscillation. Key finding: structured MDP with hysteresis outperforms simple threshold heuristics. But even simple threshold policies (scale up at queue=N, scale down at queue=M where M < N) perform reasonably well. + + +## Key Facts +- MDP formulation for cloud autoscaling: states = queue lengths + active VMs, actions = add/remove VMs, rewards = negative cost (energy + SLA violations) +- Value iteration and policy iteration algorithms used to find optimal threshold policies +- Example hysteresis thresholds: scale up at queue=10, scale down at queue=3 -- 2.45.2