{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:Z7JOBU22Z6662KLMQCMHESCDWO","short_pith_number":"pith:Z7JOBU22","schema_version":"1.0","canonical_sha256":"cfd2e0d35acfbded296c8098724843b3b63feaee819ee8ed8d0ece0047cb6dac","source":{"kind":"arxiv","id":"2409.09063","version":1},"attestation_state":"computed","paper":{"title":"TS-EoH: An Edge Server Task Scheduling Algorithm Based on Evolution of Heuristic","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.DC","authors_text":"Pei Yuchen, Wang Yatong, Zhao Yuqi","submitted_at":"2024-09-04T10:00:32Z","abstract_excerpt":"With the widespread adoption of 5G and Internet of Things (IoT) technologies, the low latency provided by edge computing has great importance for real-time processing. However, managing numerous simultaneous service requests poses a significant challenge to maintaining low latency. Current edge server task scheduling methods often fail to balance multiple optimization goals effectively. This paper introduces a novel task-scheduling approach based on Evolutionary Computing (EC) theory and heuristic algorithms. We model service requests as task sequences and evaluate various scheduling schemes d"},"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":"2409.09063","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2024-09-04T10:00:32Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"86fad745030ccbfce8d0b5ac3cea80b771cfc10a193254872fd4ed9b98c18657","abstract_canon_sha256":"6b383a4894abf7e6de7c98676f92c32be0708e09faf4cf131a2635c7106caa9a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:07:04.386753Z","signature_b64":"V65G7QJEFKT4Wtb/rY++wfLoJE23JxmBV58snY5YbxTa+ZgjqJev307xM5t8dIaa28ygC0fN5cCMwgO0UFs4DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cfd2e0d35acfbded296c8098724843b3b63feaee819ee8ed8d0ece0047cb6dac","last_reissued_at":"2026-07-05T09:07:04.386359Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:07:04.386359Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TS-EoH: An Edge Server Task Scheduling Algorithm Based on Evolution of Heuristic","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.DC","authors_text":"Pei Yuchen, Wang Yatong, Zhao Yuqi","submitted_at":"2024-09-04T10:00:32Z","abstract_excerpt":"With the widespread adoption of 5G and Internet of Things (IoT) technologies, the low latency provided by edge computing has great importance for real-time processing. However, managing numerous simultaneous service requests poses a significant challenge to maintaining low latency. Current edge server task scheduling methods often fail to balance multiple optimization goals effectively. This paper introduces a novel task-scheduling approach based on Evolutionary Computing (EC) theory and heuristic algorithms. We model service requests as task sequences and evaluate various scheduling schemes d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.09063","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2409.09063/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":"2409.09063","created_at":"2026-07-05T09:07:04.386422+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.09063v1","created_at":"2026-07-05T09:07:04.386422+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.09063","created_at":"2026-07-05T09:07:04.386422+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z7JOBU22Z666","created_at":"2026-07-05T09:07:04.386422+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z7JOBU22Z6662KLM","created_at":"2026-07-05T09:07:04.386422+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z7JOBU22","created_at":"2026-07-05T09:07:04.386422+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.08269","citing_title":"A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving","ref_index":162,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Z7JOBU22Z6662KLMQCMHESCDWO","json":"https://pith.science/pith/Z7JOBU22Z6662KLMQCMHESCDWO.json","graph_json":"https://pith.science/api/pith-number/Z7JOBU22Z6662KLMQCMHESCDWO/graph.json","events_json":"https://pith.science/api/pith-number/Z7JOBU22Z6662KLMQCMHESCDWO/events.json","paper":"https://pith.science/paper/Z7JOBU22"},"agent_actions":{"view_html":"https://pith.science/pith/Z7JOBU22Z6662KLMQCMHESCDWO","download_json":"https://pith.science/pith/Z7JOBU22Z6662KLMQCMHESCDWO.json","view_paper":"https://pith.science/paper/Z7JOBU22","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.09063&json=true","fetch_graph":"https://pith.science/api/pith-number/Z7JOBU22Z6662KLMQCMHESCDWO/graph.json","fetch_events":"https://pith.science/api/pith-number/Z7JOBU22Z6662KLMQCMHESCDWO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z7JOBU22Z6662KLMQCMHESCDWO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z7JOBU22Z6662KLMQCMHESCDWO/action/storage_attestation","attest_author":"https://pith.science/pith/Z7JOBU22Z6662KLMQCMHESCDWO/action/author_attestation","sign_citation":"https://pith.science/pith/Z7JOBU22Z6662KLMQCMHESCDWO/action/citation_signature","submit_replication":"https://pith.science/pith/Z7JOBU22Z6662KLMQCMHESCDWO/action/replication_record"}},"created_at":"2026-07-05T09:07:04.386422+00:00","updated_at":"2026-07-05T09:07:04.386422+00:00"}