{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:LNBI43H4IGMNR2AEQA5GJYGOQZ","short_pith_number":"pith:LNBI43H4","schema_version":"1.0","canonical_sha256":"5b428e6cfc4198d8e804803a64e0ce867edc70b0e31ba828bb2674671132c347","source":{"kind":"arxiv","id":"1506.05443","version":1},"attestation_state":"computed","paper":{"title":"Using Application Data for SLA-aware Auto-scaling in Cloud Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Andre Abrantes D. P. Souza, Marco A. S. Netto","submitted_at":"2015-06-17T19:52:04Z","abstract_excerpt":"With the establishment of cloud computing as the environment of choice for most modern applications, auto-scaling is an economic matter of great importance. For applications like stream computing that process ever changing amounts of data, modifying the number and configuration of resources to meet performance requirements becomes essential. Current solutions on auto-scaling are mostly rule-based using infrastructure level metrics such as CPU/memory/network utilization, and system level metrics such as throughput and response time. In this paper, we introduce a study on how effective auto-scal"},"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":"1506.05443","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2015-06-17T19:52:04Z","cross_cats_sorted":[],"title_canon_sha256":"5b36c696fa658f80edbdf9a6381d586b0eca60a63818d33290c4b8de0a4e6240","abstract_canon_sha256":"2701e6726f7e5d380da3305fd5e61c7305de667b41682968f402013c6c608203"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:45:45.214018Z","signature_b64":"59iaE55APQW62qQ31KZcV2JyQC01sfdrn7KKif5yqZrv//xWI6nPZAREnNJm6fOQcybmeaoJDJLnTzs8jsU3Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5b428e6cfc4198d8e804803a64e0ce867edc70b0e31ba828bb2674671132c347","last_reissued_at":"2026-05-18T01:45:45.213027Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:45:45.213027Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Using Application Data for SLA-aware Auto-scaling in Cloud Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Andre Abrantes D. P. Souza, Marco A. S. Netto","submitted_at":"2015-06-17T19:52:04Z","abstract_excerpt":"With the establishment of cloud computing as the environment of choice for most modern applications, auto-scaling is an economic matter of great importance. For applications like stream computing that process ever changing amounts of data, modifying the number and configuration of resources to meet performance requirements becomes essential. Current solutions on auto-scaling are mostly rule-based using infrastructure level metrics such as CPU/memory/network utilization, and system level metrics such as throughput and response time. In this paper, we introduce a study on how effective auto-scal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.05443","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":"1506.05443","created_at":"2026-05-18T01:45:45.213153+00:00"},{"alias_kind":"arxiv_version","alias_value":"1506.05443v1","created_at":"2026-05-18T01:45:45.213153+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.05443","created_at":"2026-05-18T01:45:45.213153+00:00"},{"alias_kind":"pith_short_12","alias_value":"LNBI43H4IGMN","created_at":"2026-05-18T12:29:29.992203+00:00"},{"alias_kind":"pith_short_16","alias_value":"LNBI43H4IGMNR2AE","created_at":"2026-05-18T12:29:29.992203+00:00"},{"alias_kind":"pith_short_8","alias_value":"LNBI43H4","created_at":"2026-05-18T12:29:29.992203+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/LNBI43H4IGMNR2AEQA5GJYGOQZ","json":"https://pith.science/pith/LNBI43H4IGMNR2AEQA5GJYGOQZ.json","graph_json":"https://pith.science/api/pith-number/LNBI43H4IGMNR2AEQA5GJYGOQZ/graph.json","events_json":"https://pith.science/api/pith-number/LNBI43H4IGMNR2AEQA5GJYGOQZ/events.json","paper":"https://pith.science/paper/LNBI43H4"},"agent_actions":{"view_html":"https://pith.science/pith/LNBI43H4IGMNR2AEQA5GJYGOQZ","download_json":"https://pith.science/pith/LNBI43H4IGMNR2AEQA5GJYGOQZ.json","view_paper":"https://pith.science/paper/LNBI43H4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1506.05443&json=true","fetch_graph":"https://pith.science/api/pith-number/LNBI43H4IGMNR2AEQA5GJYGOQZ/graph.json","fetch_events":"https://pith.science/api/pith-number/LNBI43H4IGMNR2AEQA5GJYGOQZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LNBI43H4IGMNR2AEQA5GJYGOQZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LNBI43H4IGMNR2AEQA5GJYGOQZ/action/storage_attestation","attest_author":"https://pith.science/pith/LNBI43H4IGMNR2AEQA5GJYGOQZ/action/author_attestation","sign_citation":"https://pith.science/pith/LNBI43H4IGMNR2AEQA5GJYGOQZ/action/citation_signature","submit_replication":"https://pith.science/pith/LNBI43H4IGMNR2AEQA5GJYGOQZ/action/replication_record"}},"created_at":"2026-05-18T01:45:45.213153+00:00","updated_at":"2026-05-18T01:45:45.213153+00:00"}