{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:6R37QBCN4PG5OIWVE5Q4KDJ4S7","short_pith_number":"pith:6R37QBCN","schema_version":"1.0","canonical_sha256":"f477f8044de3cdd722d52761c50d3c97c9fd9cf7eb0fe17e01af7b78d4afcaf1","source":{"kind":"arxiv","id":"1507.00567","version":1},"attestation_state":"computed","paper":{"title":"Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.DC","cs.LG","cs.SE"],"primary_cat":"cs.SY","authors_text":"Amir Sharifloo, Andreas Metzger, Claus Pahl, Giovani Estrada, Pooyan Jamshidi","submitted_at":"2015-07-02T13:11:22Z","abstract_excerpt":"Cloud controllers aim at responding to application demands by automatically scaling the compute resources at runtime to meet performance guarantees and minimize resource costs. Existing cloud controllers often resort to scaling strategies that are codified as a set of adaptation rules. However, for a cloud provider, applications running on top of the cloud infrastructure are more or less black-boxes, making it difficult at design time to define optimal or pre-emptive adaptation rules. Thus, the burden of taking adaptation decisions often is delegated to the cloud application. Yet, in most case"},"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":"1507.00567","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2015-07-02T13:11:22Z","cross_cats_sorted":["cs.AI","cs.DC","cs.LG","cs.SE"],"title_canon_sha256":"bcc267495d3c0224aa2c2acb6e53608b2a45f7a479b4a6766214dbe57b125c6b","abstract_canon_sha256":"395ef1bb240dada21e4d81eb9777894722a736f35a1a446e6fd87911d429e6aa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:37:24.264813Z","signature_b64":"DL6JYIB0P1G6Kbi2hPIufcqZhWBwMj0/bp65f1O8vyEzWn6r+vKQXf082wLtKfyh3GSApIYYDuVwUyovzz2+Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f477f8044de3cdd722d52761c50d3c97c9fd9cf7eb0fe17e01af7b78d4afcaf1","last_reissued_at":"2026-05-18T01:37:24.264167Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:37:24.264167Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.DC","cs.LG","cs.SE"],"primary_cat":"cs.SY","authors_text":"Amir Sharifloo, Andreas Metzger, Claus Pahl, Giovani Estrada, Pooyan Jamshidi","submitted_at":"2015-07-02T13:11:22Z","abstract_excerpt":"Cloud controllers aim at responding to application demands by automatically scaling the compute resources at runtime to meet performance guarantees and minimize resource costs. Existing cloud controllers often resort to scaling strategies that are codified as a set of adaptation rules. However, for a cloud provider, applications running on top of the cloud infrastructure are more or less black-boxes, making it difficult at design time to define optimal or pre-emptive adaptation rules. Thus, the burden of taking adaptation decisions often is delegated to the cloud application. Yet, in most case"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.00567","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":"1507.00567","created_at":"2026-05-18T01:37:24.264262+00:00"},{"alias_kind":"arxiv_version","alias_value":"1507.00567v1","created_at":"2026-05-18T01:37:24.264262+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.00567","created_at":"2026-05-18T01:37:24.264262+00:00"},{"alias_kind":"pith_short_12","alias_value":"6R37QBCN4PG5","created_at":"2026-05-18T12:29:07.941421+00:00"},{"alias_kind":"pith_short_16","alias_value":"6R37QBCN4PG5OIWV","created_at":"2026-05-18T12:29:07.941421+00:00"},{"alias_kind":"pith_short_8","alias_value":"6R37QBCN","created_at":"2026-05-18T12:29:07.941421+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/6R37QBCN4PG5OIWVE5Q4KDJ4S7","json":"https://pith.science/pith/6R37QBCN4PG5OIWVE5Q4KDJ4S7.json","graph_json":"https://pith.science/api/pith-number/6R37QBCN4PG5OIWVE5Q4KDJ4S7/graph.json","events_json":"https://pith.science/api/pith-number/6R37QBCN4PG5OIWVE5Q4KDJ4S7/events.json","paper":"https://pith.science/paper/6R37QBCN"},"agent_actions":{"view_html":"https://pith.science/pith/6R37QBCN4PG5OIWVE5Q4KDJ4S7","download_json":"https://pith.science/pith/6R37QBCN4PG5OIWVE5Q4KDJ4S7.json","view_paper":"https://pith.science/paper/6R37QBCN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1507.00567&json=true","fetch_graph":"https://pith.science/api/pith-number/6R37QBCN4PG5OIWVE5Q4KDJ4S7/graph.json","fetch_events":"https://pith.science/api/pith-number/6R37QBCN4PG5OIWVE5Q4KDJ4S7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6R37QBCN4PG5OIWVE5Q4KDJ4S7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6R37QBCN4PG5OIWVE5Q4KDJ4S7/action/storage_attestation","attest_author":"https://pith.science/pith/6R37QBCN4PG5OIWVE5Q4KDJ4S7/action/author_attestation","sign_citation":"https://pith.science/pith/6R37QBCN4PG5OIWVE5Q4KDJ4S7/action/citation_signature","submit_replication":"https://pith.science/pith/6R37QBCN4PG5OIWVE5Q4KDJ4S7/action/replication_record"}},"created_at":"2026-05-18T01:37:24.264262+00:00","updated_at":"2026-05-18T01:37:24.264262+00:00"}