{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:JWCGOVOGZBL7R77GKFBVTN2IST","short_pith_number":"pith:JWCGOVOG","schema_version":"1.0","canonical_sha256":"4d846755c6c857f8ffe6514359b74894df516a90fe1429be23b9d902c8039562","source":{"kind":"arxiv","id":"2308.05937","version":2},"attestation_state":"computed","paper":{"title":"A Deep Recurrent-Reinforcement Learning Method for Intelligent AutoScaling of Serverless Functions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.SY","eess.SY"],"primary_cat":"cs.DC","authors_text":"Maria A. Rodriguez, Rajkumar Buyya, Siddharth Agarwal","submitted_at":"2023-08-11T04:41:19Z","abstract_excerpt":"FaaS introduces a lightweight, function-based cloud execution model that finds its relevance in a range of applications like IoT-edge data processing and anomaly detection. While cloud service providers offer a near-infinite function elasticity, these applications often experience fluctuating workloads and stricter performance constraints. A typical CSP strategy is to empirically determine and adjust desired function instances or resources, known as autoscaling, based on monitoring-based thresholds such as CPU or memory, to cope with demand and performance. However, threshold configuration eit"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2308.05937","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2023-08-11T04:41:19Z","cross_cats_sorted":["cs.AI","cs.SY","eess.SY"],"title_canon_sha256":"572a7254c3bb167f8e87a9c21f6ed737d9906a766bef0b8aae8f51c009838aed","abstract_canon_sha256":"f682d0068d1607c8bd411fc7d4b06d1f23a5a7554fbbb6c7bf82cfd6b8997b23"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:34:09.542067Z","signature_b64":"oScweABAEMWtuEhxmo216kg1V3kstCSrI0djCaSmiY22hBm7ukXAOJb7JogIClWgxTjD6Ik0+4iaIxPSo8RtAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4d846755c6c857f8ffe6514359b74894df516a90fe1429be23b9d902c8039562","last_reissued_at":"2026-07-05T09:34:09.541562Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:34:09.541562Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Deep Recurrent-Reinforcement Learning Method for Intelligent AutoScaling of Serverless Functions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.SY","eess.SY"],"primary_cat":"cs.DC","authors_text":"Maria A. Rodriguez, Rajkumar Buyya, Siddharth Agarwal","submitted_at":"2023-08-11T04:41:19Z","abstract_excerpt":"FaaS introduces a lightweight, function-based cloud execution model that finds its relevance in a range of applications like IoT-edge data processing and anomaly detection. While cloud service providers offer a near-infinite function elasticity, these applications often experience fluctuating workloads and stricter performance constraints. A typical CSP strategy is to empirically determine and adjust desired function instances or resources, known as autoscaling, based on monitoring-based thresholds such as CPU or memory, to cope with demand and performance. However, threshold configuration eit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.05937","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2308.05937/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2308.05937","created_at":"2026-07-05T09:34:09.541622+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.05937v2","created_at":"2026-07-05T09:34:09.541622+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.05937","created_at":"2026-07-05T09:34:09.541622+00:00"},{"alias_kind":"pith_short_12","alias_value":"JWCGOVOGZBL7","created_at":"2026-07-05T09:34:09.541622+00:00"},{"alias_kind":"pith_short_16","alias_value":"JWCGOVOGZBL7R77G","created_at":"2026-07-05T09:34:09.541622+00:00"},{"alias_kind":"pith_short_8","alias_value":"JWCGOVOG","created_at":"2026-07-05T09:34:09.541622+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JWCGOVOGZBL7R77GKFBVTN2IST","json":"https://pith.science/pith/JWCGOVOGZBL7R77GKFBVTN2IST.json","graph_json":"https://pith.science/api/pith-number/JWCGOVOGZBL7R77GKFBVTN2IST/graph.json","events_json":"https://pith.science/api/pith-number/JWCGOVOGZBL7R77GKFBVTN2IST/events.json","paper":"https://pith.science/paper/JWCGOVOG"},"agent_actions":{"view_html":"https://pith.science/pith/JWCGOVOGZBL7R77GKFBVTN2IST","download_json":"https://pith.science/pith/JWCGOVOGZBL7R77GKFBVTN2IST.json","view_paper":"https://pith.science/paper/JWCGOVOG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.05937&json=true","fetch_graph":"https://pith.science/api/pith-number/JWCGOVOGZBL7R77GKFBVTN2IST/graph.json","fetch_events":"https://pith.science/api/pith-number/JWCGOVOGZBL7R77GKFBVTN2IST/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JWCGOVOGZBL7R77GKFBVTN2IST/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JWCGOVOGZBL7R77GKFBVTN2IST/action/storage_attestation","attest_author":"https://pith.science/pith/JWCGOVOGZBL7R77GKFBVTN2IST/action/author_attestation","sign_citation":"https://pith.science/pith/JWCGOVOGZBL7R77GKFBVTN2IST/action/citation_signature","submit_replication":"https://pith.science/pith/JWCGOVOGZBL7R77GKFBVTN2IST/action/replication_record"}},"created_at":"2026-07-05T09:34:09.541622+00:00","updated_at":"2026-07-05T09:34:09.541622+00:00"}