{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp04_retrieval_first_paired20_20260802","cell_id":"deadweight_loss_reduction__systems_cybernetics","round_index":0,"assessments":[{"hypothesis_id":"H1","search_queries":["compute cluster dynamic pricing internal credits batch jobs peak off peak scheduling research","cloud spot pricing HPC workload scheduling deadline priority reserved capacity research","HPC cluster fairshare priority reservations urgent computing official documentation","dynamic pricing shared computing resources congestion pricing paper"],"sources":[{"source_id":"H1-S1","title":"Dynamic access pricing control for fair and stable resource sharing","publisher":"arXiv","url":"https://arxiv.org/abs/2507.17939","source_class":"PRIMARY_RESEARCH","claims_supported":["Dynamic prices can regulate queues for a capacity-constrained shared resource.","Fairness constraints are needed because ordinary surge pricing can disproportionately exclude price-sensitive users.","The proposed controller combines load-responsive pricing with differentiated treatment of user classes."]},{"source_id":"H1-S2","title":"Advanced Resource Reservation Guide","publisher":"SchedMD","url":"https://slurm.schedmd.com/reservations.html","source_class":"OFFICIAL_PRODUCT_DOCUMENTATION","claims_supported":["Slurm supports time-bounded reservations for selected users, accounts, partitions, and quality-of-service classes.","Reserved jobs receive scheduling preference over non-reserved jobs in the same partition.","Reservations can preserve compute access for designated users while ordinary jobs share remaining capacity."]},{"source_id":"H1-S3","title":"Spot Instances","publisher":"Amazon Web Services","url":"https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-spot-instances.html","source_class":"OFFICIAL_PRODUCT_DOCUMENTATION","claims_supported":["AWS exposes spare compute capacity through a variable price influenced by supply and demand.","The mechanism is explicitly suited to flexible batch jobs and other interruptible work.","Capacity-sensitive prices and protected non-Spot purchasing modes already coexist in a compute service."]}],"closest_analogue":"Dynamic access pricing for a shared queue combined with Slurm reservations for protected compute users.","overlap":"The analogue family already varies an access price with congestion, shifts flexible batch work toward spare capacity, models unequal price responsiveness, and preserves designated capacity through reservations or separate service classes.","remaining_difference":"H1 uses refundable internal credits rather than money and proposes a simulation-cluster pilot with protected-user completion-rate constraints; this is an implementation and evaluation difference, testable by comparing demand shifting, queue time, and protected-user completion against fair-share scheduling.","classification":"OBVIOUS_COLLISION","disposition":"REJECT","rationale":"The causal mechanism is already directly represented by load-responsive pricing of shared compute-like resources plus established reservation safeguards. Refundability and the simulation-cluster setting do not create a sufficiently distinct mechanism for this shallow screen."},{"hypothesis_id":"H2","search_queries":["risk tiered change management low risk standard changes parallel review sandbox deployment controller industrial control systems","NIST configuration change control impact analysis testing rollback low risk changes control systems","IEC 62443 management of change industrial automation control system risk assessment controller update","site reliability engineering canary deployment risk based change approval low risk"],"sources":[{"source_id":"H2-S1","title":"OT Change Management: The Complete 2026 Guide","publisher":"VEM","url":"https://getvem.com/guide/ot-change-management","source_class":"COMMERCIAL_FIRST_PARTY","claims_supported":["The described OT workflow explicitly risk-tiers changes, sending major changes to a change-advisory board while standard changes use a pre-approved template and single approver.","It stages approved changes on a simulated controller before production deployment.","It records controller state before and after deployment."]},{"source_id":"H2-S2","title":"Protecting Controlled Unclassified Information in Nonfederal Systems and Organizations (NIST SP 800-171 Rev. 3)","publisher":"National Institute of Standards and Technology","url":"https://nvlpubs.nist.gov/nistpubs/SpecialPublications/800-171r3/NIST.SP.800-171r3.html","source_class":"GOVERNMENT_OR_REGULATOR","claims_supported":["Configuration changes are reviewed with explicit consideration of security impacts.","Pre-change impact analysis and post-change verification are required.","NIST recognizes that organizations may define different types of configuration-controlled changes and that not every change is configuration controlled."]},{"source_id":"H2-S3","title":"Canarying Releases","publisher":"Google Site Reliability Engineering","url":"https://sre.google/workbook/canarying-releases/","source_class":"OFFICIAL_GUIDANCE","claims_supported":["Canarying is a partial, time-limited deployment evaluated before wider rollout.","Small exposure limits the impact of a defective change.","Monitoring can automatically pause or roll back a deployment when canary metrics diverge from the control."]},{"source_id":"H2-S4","title":"Deployment Risk Assessment Using Diff-Aware Features: A Case Study at Prime Video","publisher":"arXiv / Amazon Prime Video authors","url":"https://arxiv.org/abs/2607.06766","source_class":"PRIMARY_RESEARCH","claims_supported":["Blanket deployment restrictions create delay by treating all changes as equally risky.","Commit-level risk assessment enables selective deployment decisions.","The reported model distinguishes risky changes using structural features and production data."]}],"closest_analogue":"VEM's risk-tiered OT change workflow, supplemented by monitored canary deployment.","overlap":"It already combines controller-specific change requests, risk-tiered approval depth, simulated-controller staging, controlled production deployment, monitoring, and evidence capture; canary practice supplies bounded exposure and rollback.","remaining_difference":"H2 specifically proposes parallel review for reversible low-risk controller-parameter changes and a six-month safety-equivalence test. That workflow timing and outcome comparison is testable, but it does not materially distinguish the mechanism.","classification":"OBVIOUS_COLLISION","disposition":"REJECT","rationale":"A first-party OT workflow closely matches the controller-change domain and nearly the full intervention, while official and primary sources establish risk-based change analysis and monitored bounded deployment."},{"hypothesis_id":"H3","search_queries":["transferable telemetry quota observability storage quota allocation services retention","OpenTelemetry quota allocation retention per service telemetry budget observability official","telemetry budgets per service dynamic allocation observability signals quotas","data storage quota trading transferable quotas cloud resource allocation research"],"sources":[{"source_id":"H3-S1","title":"Dynamic SLA-aware Network Slice Monitoring","publisher":"arXiv","url":"https://arxiv.org/abs/2512.12123","source_class":"PRIMARY_RESEARCH","claims_supported":["SliceScope dynamically reallocates a limited telemetry budget across services according to SLA criticality.","It treats telemetry allocation as a closed-loop control problem.","Evaluation reports substantially better tracking of critical slices than static allocation."]},{"source_id":"H3-S2","title":"How to Use Telemetry Budgets and Quotas per Team","publisher":"OneUptime","url":"https://oneuptime.com/blog/post/2026-02-06-telemetry-budgets-quotas-otel-collector-routing/view","source_class":"COMMERCIAL_FIRST_PARTY","claims_supported":["Per-team telemetry quotas can be enforced at an OpenTelemetry Collector gateway.","Allocations can depend on service tier and use sampling, dropping, or alerting as overage policies.","The design separates teams into independently budgeted telemetry pipelines."]},{"source_id":"H3-S3","title":"Transfer quota within an Azure Quota Group","publisher":"Microsoft","url":"https://learn.microsoft.com/en-us/azure/quotas/transfer-quota-groups","source_class":"OFFICIAL_PRODUCT_DOCUMENTATION","claims_supported":["Unused quota can be returned to a group pool and redistributed to another subscription.","Transfers preserve an aggregate group limit while changing subordinate allocations.","Usage and limits constrain how much quota can be moved."]},{"source_id":"H3-S4","title":"Cloud Monitoring quotas and limits","publisher":"Google Cloud","url":"https://docs.cloud.google.com/monitoring/quotas","source_class":"OFFICIAL_PRODUCT_DOCUMENTATION","claims_supported":["Telemetry quotas restrict consumption and can block operations when exceeded.","Google exposes metric-ingestion usage and mechanisms for quota adjustment or metric exclusion.","Telemetry retention and down-sampling rules vary by signal category."]}],"closest_analogue":"SliceScope's dynamic, SLA-criticality-weighted allocation of a fixed telemetry budget.","overlap":"Both preserve a fixed aggregate telemetry constraint while moving observability resources toward higher-risk or higher-value services. Existing systems also provide per-team telemetry budgets and general mechanisms for transferring unused subordinate quotas through a shared pool.","remaining_difference":"The searched analogues do not combine service-to-service telemetry-retention transfers with risk-weighted bids, nontransferable observability floors, and automatic incident recall. A bounded pilot can test whether that combination reduces unused capacity and quota-caused drops without degrading incident detection.","classification":"POSSIBLE_DISTINCTION","disposition":"ADVANCE","rationale":"Dynamic telemetry allocation and quota transfer each have close analogues, but this shallow search did not locate their specific combination as a transferable retention-allocation mechanism with observability floors and incident recall. The distinction is concrete and experimentally testable rather than inferred from search failure."},{"hypothesis_id":"H4","search_queries":["standards body sunset individual normative requirements obsolete provisions periodic review official","IETF obsolete protocol requirements deprecate normative requirement interoperability sunset RFC process","W3C specification feature at risk removed implementation experience normative requirement","ISO IEC standards systematic review withdraw revise confirm official"],"sources":[{"source_id":"H4-S1","title":"ISO/IEC Directives, Part 1 and Consolidated ISO Supplement","publisher":"International Organization for Standardization","url":"https://www.iso.org/sites/directives/current/consolidated/index.html","source_class":"STANDARD","claims_supported":["Every ISO or joint ISO/IEC publication is subject to systematic review for confirmation, revision, amendment, conversion, or withdrawal.","International Standards normally enter systematic review within five years.","Usage evidence and committee voting inform whether a document is retained or withdrawn."]},{"source_id":"H4-S2","title":"W3C Process Document","publisher":"World Wide Web Consortium","url":"https://www.w3.org/policies/process/","source_class":"STANDARD","claims_supported":["W3C requires implementation experience, including evidence of independent interoperable implementations for specification features.","Candidate Recommendations may identify features as at risk and remove them before advancement.","Published Recommendations may later be revised, declared obsolete, superseded, or rescinded."]},{"source_id":"H4-S3","title":"ISO/IEC 18025:2013 Annex F — Change and deprecation plan","publisher":"ISO/IEC SEDRIS Standards Repository","url":"https://standards.sedris.org/18025/text/annex_f.html","source_class":"STANDARD","claims_supported":["The standard defines deprecation procedures for individual standardized items, not only whole documents.","Deprecated items remain identified for a staged period before deletion.","The process protects user investment and permits later reinstatement."]},{"source_id":"H4-S4","title":"Technical Committee Policies and Procedures","publisher":"Open Geospatial Consortium","url":"https://docs.ogc.org/pol/05-020r29/05-020r29.html","source_class":"STANDARD","claims_supported":["OGC requires evidence-seeking public comment before standards are retired or deprecated.","Individual API elements can be proposed for deprecation with rationale and implementer-impact evidence.","Deprecated API elements remain marked for two years before removal, providing a staged migration period."]}],"closest_analogue":"OGC's evidence-based, staged deprecation and eventual removal of individual API elements in standards.","overlap":"The OGC process operates at the interface-element level, requests evidence about implementation consequences, preserves migration notice, and removes deprecated elements after a defined interval. ISO/IEC and W3C add periodic review, implementation evidence, interoperability tests, and retirement or reinstatement paths.","remaining_difference":"H4 proposes attaching expiry at adoption to every normative interface requirement and scoring dependency, migration cost, security exposure, and asymmetric producer-consumer tolerance. Those measurements could be evaluated, but the lifecycle intervention itself is already closely instantiated.","classification":"OBVIOUS_COLLISION","disposition":"REJECT","rationale":"Existing standards processes already perform evidence-based review and staged retirement of individual interface elements while protecting implementers and interoperability. H4 mainly formalizes additional metrics around an established mechanism."},{"hypothesis_id":"H5","search_queries":["shared research testbed clearinghouse experiment matching facility capabilities reservations official","NSF PAWR testbed experimenter matching capabilities reservation platform official","European research infrastructure testbed catalogue transnational access experiment facility matching","federated testbed clearinghouse standardized resource descriptions experiment scheduling GENI"],"sources":[{"source_id":"H5-S1","title":"GENI Concepts","publisher":"GENI","url":"https://www.geni.net/documentation/geni-concepts/index.html","source_class":"OFFICIAL_GUIDANCE","claims_supported":["GENI is a shared experimental testbed with multiple concurrent experimenters.","Standardized RSpec documents describe requested, advertised, and allocated resources.","GENI clearinghouses provide federated identity while aggregates expose available resources and enforce local policies."]},{"source_id":"H5-S2","title":"Federate Your Testbed","publisher":"GENI","url":"https://www.geni.net/get-involved/federate-your-testbed/index.html","source_class":"OFFICIAL_GUIDANCE","claims_supported":["Independent testbeds can join a federation through a standard aggregate-management API.","Operators document resources, accept federation credentials, and retain testbed-specific usage limits.","Federation is intended to connect experimenters with otherwise separate testbed resources."]},{"source_id":"H5-S3","title":"Federated Cybersecurity Testbed as a Service: A framework to federate cybersecurity testbeds","publisher":"arXiv","url":"https://arxiv.org/abs/2607.10061","source_class":"PRIMARY_RESEARCH","claims_supported":["FCTaaS addresses specialized but underused cyber-physical testbeds through federated discovery and coordinated experimentation.","Testbed Description Files expose capabilities and utilization, while Access Policy Files retain owner control.","The experiment service supports testbed selection, availability checking, reservation, initialization, monitoring, and teardown."]},{"source_id":"H5-S4","title":"IRISCC Catalogue of Services","publisher":"Integrated Research Infrastructure Services for Climate Change Risks","url":"https://www.iriscc.eu/catalogue-of-services","source_class":"OFFICIAL_ORGANIZATION_DATA","claims_supported":["The catalogue is a single entry point for facilities and research-infrastructure services.","Entries expose technical capabilities, access modes, eligibility, providers, and use cases.","Users can filter services and apply for on-site, remote, hybrid, or fast-track access."]}],"closest_analogue":"FCTaaS, with GENI as the mature federated-testbed clearinghouse architecture.","overlap":"The analogues already address underused heterogeneous testbeds through standardized capability descriptions, discovery, constraint and policy screening, reservation, cross-institutional access, coordinated execution, monitoring, and teardown while preserving operator control.","remaining_difference":"H5 narrows the matching objective to rare disturbance tests and adds explicit anti-withholding or ranking-game rules. Those rules could be tested against ordinary repository selection, but they are an incremental governance feature atop an already matching clearinghouse.","classification":"OBVIOUS_COLLISION","disposition":"REJECT","rationale":"FCTaaS and GENI directly instantiate the proposed clearinghouse workflow, including capability descriptors, policy-aware selection, reservations, and safe cross-lab experimentation. The disturbance label and anti-withholding rule do not preserve a distinct core mechanism."}],"nominated_ids":["H3"],"replenishment_recommended":true}