{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:GUKXCCU5AINFMI4DQ66MC5BIQQ","short_pith_number":"pith:GUKXCCU5","schema_version":"1.0","canonical_sha256":"3515710a9d021a56238387bcc174288411c5de1a2704e6333a82ca30a2d16991","source":{"kind":"arxiv","id":"1811.00989","version":1},"attestation_state":"computed","paper":{"title":"CMI: An Online Multi-objective Genetic Autoscaler for Scientific and Engineering Workflows in Cloud Infrastructures with Unreliable Virtual Machines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.NE","authors_text":"2), (2) Consejo Nacional de Investigaciones Cient\\'ificas y T\\'ecnicas (CONICET). Argentina., (3) ISISTAN-UNICEN-CONICET. Tandil, (4) Departamento de Lenguajes y Ciencias de la Computaci\\'on, Argentina, Argentina., Buenos Aires, Carlos Garc\\'ia Garino (1) ((1) ITIC, Cristian Mateos (3), David A. Monge (1), Elina Pacini (1, Enrique Alba (4), Universidad de M\\'alaga. Spain.), Universidad Nacional de Cuyo. Mendoza","submitted_at":"2018-11-02T17:11:57Z","abstract_excerpt":"Cloud Computing is becoming the leading paradigm for executing scientific and engineering workflows. The large-scale nature of the experiments they model and their variable workloads make clouds the ideal execution environment due to prompt and elastic access to huge amounts of computing resources. Autoscalers are middleware-level software components that allow scaling up and down the computing platform by acquiring or terminating virtual machines (VM) at the time that workflow's tasks are being scheduled. In this work we propose a novel online multi-objective autoscaler for workflows denomina"},"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":"1811.00989","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-11-02T17:11:57Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"ddcd85b3649c8dcc8016c16139cd846d1ff91aa674d694b6c633290e978aac20","abstract_canon_sha256":"45b913a9a7545f240bf6ca259ea2e0277a4ea4e242648708e6c39d700f18b97a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:39.729465Z","signature_b64":"Qh6r0+Uv4nR94dNqiRpNGnT+7UJbo1JkrHkczTXnmmgccCBQWXijTh3oJhj4gwgYLGcbn+/tNzQTl7+ibWgIDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3515710a9d021a56238387bcc174288411c5de1a2704e6333a82ca30a2d16991","last_reissued_at":"2026-05-18T00:01:39.728985Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:39.728985Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CMI: An Online Multi-objective Genetic Autoscaler for Scientific and Engineering Workflows in Cloud Infrastructures with Unreliable Virtual Machines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.NE","authors_text":"2), (2) Consejo Nacional de Investigaciones Cient\\'ificas y T\\'ecnicas (CONICET). Argentina., (3) ISISTAN-UNICEN-CONICET. Tandil, (4) Departamento de Lenguajes y Ciencias de la Computaci\\'on, Argentina, Argentina., Buenos Aires, Carlos Garc\\'ia Garino (1) ((1) ITIC, Cristian Mateos (3), David A. Monge (1), Elina Pacini (1, Enrique Alba (4), Universidad de M\\'alaga. Spain.), Universidad Nacional de Cuyo. Mendoza","submitted_at":"2018-11-02T17:11:57Z","abstract_excerpt":"Cloud Computing is becoming the leading paradigm for executing scientific and engineering workflows. The large-scale nature of the experiments they model and their variable workloads make clouds the ideal execution environment due to prompt and elastic access to huge amounts of computing resources. Autoscalers are middleware-level software components that allow scaling up and down the computing platform by acquiring or terminating virtual machines (VM) at the time that workflow's tasks are being scheduled. In this work we propose a novel online multi-objective autoscaler for workflows denomina"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.00989","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1811.00989","created_at":"2026-05-18T00:01:39.729056+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.00989v1","created_at":"2026-05-18T00:01:39.729056+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.00989","created_at":"2026-05-18T00:01:39.729056+00:00"},{"alias_kind":"pith_short_12","alias_value":"GUKXCCU5AINF","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_16","alias_value":"GUKXCCU5AINFMI4D","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_8","alias_value":"GUKXCCU5","created_at":"2026-05-18T12:32:25.280505+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/GUKXCCU5AINFMI4DQ66MC5BIQQ","json":"https://pith.science/pith/GUKXCCU5AINFMI4DQ66MC5BIQQ.json","graph_json":"https://pith.science/api/pith-number/GUKXCCU5AINFMI4DQ66MC5BIQQ/graph.json","events_json":"https://pith.science/api/pith-number/GUKXCCU5AINFMI4DQ66MC5BIQQ/events.json","paper":"https://pith.science/paper/GUKXCCU5"},"agent_actions":{"view_html":"https://pith.science/pith/GUKXCCU5AINFMI4DQ66MC5BIQQ","download_json":"https://pith.science/pith/GUKXCCU5AINFMI4DQ66MC5BIQQ.json","view_paper":"https://pith.science/paper/GUKXCCU5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.00989&json=true","fetch_graph":"https://pith.science/api/pith-number/GUKXCCU5AINFMI4DQ66MC5BIQQ/graph.json","fetch_events":"https://pith.science/api/pith-number/GUKXCCU5AINFMI4DQ66MC5BIQQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GUKXCCU5AINFMI4DQ66MC5BIQQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GUKXCCU5AINFMI4DQ66MC5BIQQ/action/storage_attestation","attest_author":"https://pith.science/pith/GUKXCCU5AINFMI4DQ66MC5BIQQ/action/author_attestation","sign_citation":"https://pith.science/pith/GUKXCCU5AINFMI4DQ66MC5BIQQ/action/citation_signature","submit_replication":"https://pith.science/pith/GUKXCCU5AINFMI4DQ66MC5BIQQ/action/replication_record"}},"created_at":"2026-05-18T00:01:39.729056+00:00","updated_at":"2026-05-18T00:01:39.729056+00:00"}