{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:BAT3FBMFLGDYUEXCA5IHTTZIEA","short_pith_number":"pith:BAT3FBMF","schema_version":"1.0","canonical_sha256":"0827b2858559878a12e2075079cf2820337d56f25f24b4f69107fc55d042fade","source":{"kind":"arxiv","id":"1804.08403","version":1},"attestation_state":"computed","paper":{"title":"Machine Learning-Assisted Least Loaded Routing to Improve Performance of Circuit-Switched Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Gangxiang Shen, Longfei Li, Moshe Zukerman, Sanjay K. Bose, Wei Chen, Ya Zhang","submitted_at":"2018-04-03T04:08:44Z","abstract_excerpt":"The Least Loaded (LL) routing algorithm has been in recent decades the routing method of choice in circuit switched networks and therefore it provides a benchmark against which new methods can be compared. This paper improves the performance of the LL algorithm by additionally incorporating a machine learning approach, using a conceptually simple supervised na\\\"ive Bayes (NB) classifier. Based on a sequence of historical network snapshots, this predicts the potential future circuit blocking probability between each node pair. These snapshots are taken for each service request arriving to the n"},"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":"1804.08403","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2018-04-03T04:08:44Z","cross_cats_sorted":[],"title_canon_sha256":"60ade70cf0b77122c3021c28aa5a10934610856d9b3b0dad3d2cba68c65dc872","abstract_canon_sha256":"44a7c926fe7fefb9e75066347327b65199a9a73a3b6e0155eb6866bb5a9c4384"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:17:48.488031Z","signature_b64":"d8G5ZuBynLVBJo6SisW5jQTfSe4jIOH5EurqHDGn1SVyVn9cfEdM7UsjOOE8kVQ1ObpkAjIrbqOQTH6tYQsmDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0827b2858559878a12e2075079cf2820337d56f25f24b4f69107fc55d042fade","last_reissued_at":"2026-05-18T00:17:48.487323Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:17:48.487323Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Machine Learning-Assisted Least Loaded Routing to Improve Performance of Circuit-Switched Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Gangxiang Shen, Longfei Li, Moshe Zukerman, Sanjay K. Bose, Wei Chen, Ya Zhang","submitted_at":"2018-04-03T04:08:44Z","abstract_excerpt":"The Least Loaded (LL) routing algorithm has been in recent decades the routing method of choice in circuit switched networks and therefore it provides a benchmark against which new methods can be compared. This paper improves the performance of the LL algorithm by additionally incorporating a machine learning approach, using a conceptually simple supervised na\\\"ive Bayes (NB) classifier. Based on a sequence of historical network snapshots, this predicts the potential future circuit blocking probability between each node pair. These snapshots are taken for each service request arriving to the n"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.08403","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":"1804.08403","created_at":"2026-05-18T00:17:48.487424+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.08403v1","created_at":"2026-05-18T00:17:48.487424+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.08403","created_at":"2026-05-18T00:17:48.487424+00:00"},{"alias_kind":"pith_short_12","alias_value":"BAT3FBMFLGDY","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_16","alias_value":"BAT3FBMFLGDYUEXC","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_8","alias_value":"BAT3FBMF","created_at":"2026-05-18T12:32:13.499390+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/BAT3FBMFLGDYUEXCA5IHTTZIEA","json":"https://pith.science/pith/BAT3FBMFLGDYUEXCA5IHTTZIEA.json","graph_json":"https://pith.science/api/pith-number/BAT3FBMFLGDYUEXCA5IHTTZIEA/graph.json","events_json":"https://pith.science/api/pith-number/BAT3FBMFLGDYUEXCA5IHTTZIEA/events.json","paper":"https://pith.science/paper/BAT3FBMF"},"agent_actions":{"view_html":"https://pith.science/pith/BAT3FBMFLGDYUEXCA5IHTTZIEA","download_json":"https://pith.science/pith/BAT3FBMFLGDYUEXCA5IHTTZIEA.json","view_paper":"https://pith.science/paper/BAT3FBMF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.08403&json=true","fetch_graph":"https://pith.science/api/pith-number/BAT3FBMFLGDYUEXCA5IHTTZIEA/graph.json","fetch_events":"https://pith.science/api/pith-number/BAT3FBMFLGDYUEXCA5IHTTZIEA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BAT3FBMFLGDYUEXCA5IHTTZIEA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BAT3FBMFLGDYUEXCA5IHTTZIEA/action/storage_attestation","attest_author":"https://pith.science/pith/BAT3FBMFLGDYUEXCA5IHTTZIEA/action/author_attestation","sign_citation":"https://pith.science/pith/BAT3FBMFLGDYUEXCA5IHTTZIEA/action/citation_signature","submit_replication":"https://pith.science/pith/BAT3FBMFLGDYUEXCA5IHTTZIEA/action/replication_record"}},"created_at":"2026-05-18T00:17:48.487424+00:00","updated_at":"2026-05-18T00:17:48.487424+00:00"}