{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:EBHXIYHOX325PT55TBQIO2NC7Z","short_pith_number":"pith:EBHXIYHO","canonical_record":{"source":{"id":"2404.01459","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.DC","submitted_at":"2024-04-01T20:13:28Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"c51372cab8c23c8960759714c38e26272abb773a767a155ef4ebd48024e65ad2","abstract_canon_sha256":"f0413d42e906a45430863e72d8490d85633d119c02dc5b76399683fa89eca5b4"},"schema_version":"1.0"},"canonical_sha256":"204f7460eebef5d7cfbd98608769a2fe61c345a5d0ebb10de42489aada40b73b","source":{"kind":"arxiv","id":"2404.01459","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.01459","created_at":"2026-07-05T08:03:14Z"},{"alias_kind":"arxiv_version","alias_value":"2404.01459v1","created_at":"2026-07-05T08:03:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.01459","created_at":"2026-07-05T08:03:14Z"},{"alias_kind":"pith_short_12","alias_value":"EBHXIYHOX325","created_at":"2026-07-05T08:03:14Z"},{"alias_kind":"pith_short_16","alias_value":"EBHXIYHOX325PT55","created_at":"2026-07-05T08:03:14Z"},{"alias_kind":"pith_short_8","alias_value":"EBHXIYHO","created_at":"2026-07-05T08:03:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:EBHXIYHOX325PT55TBQIO2NC7Z","target":"record","payload":{"canonical_record":{"source":{"id":"2404.01459","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.DC","submitted_at":"2024-04-01T20:13:28Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"c51372cab8c23c8960759714c38e26272abb773a767a155ef4ebd48024e65ad2","abstract_canon_sha256":"f0413d42e906a45430863e72d8490d85633d119c02dc5b76399683fa89eca5b4"},"schema_version":"1.0"},"canonical_sha256":"204f7460eebef5d7cfbd98608769a2fe61c345a5d0ebb10de42489aada40b73b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:03:14.494956Z","signature_b64":"QnyL9bI40cvZPP7YXOJzF0HmsN9QpkCq+lkvODw0CxMpSWiHfRPhJjedT29ReiZmaxry5B7MfyASARZbvlczCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"204f7460eebef5d7cfbd98608769a2fe61c345a5d0ebb10de42489aada40b73b","last_reissued_at":"2026-07-05T08:03:14.494519Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:03:14.494519Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2404.01459","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:03:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ImGI7GSdRD7WHZBDM4c5bOW/YciiPHOrh8BOvRnExziOi42lju4eeSOhfXxqcosiHsNBo39ee3YGuQGgI3SICA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T15:42:03.685481Z"},"content_sha256":"fbffcada3da423d6a1c45941a5dbb436fc7220f2ba9a434b11f9d0d9a0c907c3","schema_version":"1.0","event_id":"sha256:fbffcada3da423d6a1c45941a5dbb436fc7220f2ba9a434b11f9d0d9a0c907c3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:EBHXIYHOX325PT55TBQIO2NC7Z","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Game-Theoretic Deep Reinforcement Learning to Minimize Carbon Emissions and Energy Costs for AI Inference Workloads in Geo-Distributed Data Centers","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.DC","authors_text":"Ninad Hogade, Sudeep Pasricha","submitted_at":"2024-04-01T20:13:28Z","abstract_excerpt":"Data centers are increasingly using more energy due to the rise in Artificial Intelligence (AI) workloads, which negatively impacts the environment and raises operational costs. Reducing operating expenses and carbon emissions while maintaining performance in data centers is a challenging problem. This work introduces a unique approach combining Game Theory (GT) and Deep Reinforcement Learning (DRL) for optimizing the distribution of AI inference workloads in geo-distributed data centers to reduce carbon emissions and cloud operating (energy + data transfer) costs. The proposed technique integ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.01459","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/2404.01459/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:03:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tCdvoPNomr1t4jTxaam4psanyNiPQJQkvQMWJ42b6pnm5R6qe5YGEe4HJfBaHehCC+31auBqMygb+MmNagDQBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T15:42:03.686030Z"},"content_sha256":"012ddfce3a6d678730e765c7fba8601ea93887c4b3721ad7eeff1a225e3775f6","schema_version":"1.0","event_id":"sha256:012ddfce3a6d678730e765c7fba8601ea93887c4b3721ad7eeff1a225e3775f6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EBHXIYHOX325PT55TBQIO2NC7Z/bundle.json","state_url":"https://pith.science/pith/EBHXIYHOX325PT55TBQIO2NC7Z/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EBHXIYHOX325PT55TBQIO2NC7Z/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-21T15:42:03Z","links":{"resolver":"https://pith.science/pith/EBHXIYHOX325PT55TBQIO2NC7Z","bundle":"https://pith.science/pith/EBHXIYHOX325PT55TBQIO2NC7Z/bundle.json","state":"https://pith.science/pith/EBHXIYHOX325PT55TBQIO2NC7Z/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EBHXIYHOX325PT55TBQIO2NC7Z/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:EBHXIYHOX325PT55TBQIO2NC7Z","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f0413d42e906a45430863e72d8490d85633d119c02dc5b76399683fa89eca5b4","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.DC","submitted_at":"2024-04-01T20:13:28Z","title_canon_sha256":"c51372cab8c23c8960759714c38e26272abb773a767a155ef4ebd48024e65ad2"},"schema_version":"1.0","source":{"id":"2404.01459","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.01459","created_at":"2026-07-05T08:03:14Z"},{"alias_kind":"arxiv_version","alias_value":"2404.01459v1","created_at":"2026-07-05T08:03:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.01459","created_at":"2026-07-05T08:03:14Z"},{"alias_kind":"pith_short_12","alias_value":"EBHXIYHOX325","created_at":"2026-07-05T08:03:14Z"},{"alias_kind":"pith_short_16","alias_value":"EBHXIYHOX325PT55","created_at":"2026-07-05T08:03:14Z"},{"alias_kind":"pith_short_8","alias_value":"EBHXIYHO","created_at":"2026-07-05T08:03:14Z"}],"graph_snapshots":[{"event_id":"sha256:012ddfce3a6d678730e765c7fba8601ea93887c4b3721ad7eeff1a225e3775f6","target":"graph","created_at":"2026-07-05T08:03:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2404.01459/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Data centers are increasingly using more energy due to the rise in Artificial Intelligence (AI) workloads, which negatively impacts the environment and raises operational costs. Reducing operating expenses and carbon emissions while maintaining performance in data centers is a challenging problem. This work introduces a unique approach combining Game Theory (GT) and Deep Reinforcement Learning (DRL) for optimizing the distribution of AI inference workloads in geo-distributed data centers to reduce carbon emissions and cloud operating (energy + data transfer) costs. The proposed technique integ","authors_text":"Ninad Hogade, Sudeep Pasricha","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.DC","submitted_at":"2024-04-01T20:13:28Z","title":"Game-Theoretic Deep Reinforcement Learning to Minimize Carbon Emissions and Energy Costs for AI Inference Workloads in Geo-Distributed Data Centers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.01459","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:fbffcada3da423d6a1c45941a5dbb436fc7220f2ba9a434b11f9d0d9a0c907c3","target":"record","created_at":"2026-07-05T08:03:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f0413d42e906a45430863e72d8490d85633d119c02dc5b76399683fa89eca5b4","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.DC","submitted_at":"2024-04-01T20:13:28Z","title_canon_sha256":"c51372cab8c23c8960759714c38e26272abb773a767a155ef4ebd48024e65ad2"},"schema_version":"1.0","source":{"id":"2404.01459","kind":"arxiv","version":1}},"canonical_sha256":"204f7460eebef5d7cfbd98608769a2fe61c345a5d0ebb10de42489aada40b73b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"204f7460eebef5d7cfbd98608769a2fe61c345a5d0ebb10de42489aada40b73b","first_computed_at":"2026-07-05T08:03:14.494519Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:03:14.494519Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QnyL9bI40cvZPP7YXOJzF0HmsN9QpkCq+lkvODw0CxMpSWiHfRPhJjedT29ReiZmaxry5B7MfyASARZbvlczCg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:03:14.494956Z","signed_message":"canonical_sha256_bytes"},"source_id":"2404.01459","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fbffcada3da423d6a1c45941a5dbb436fc7220f2ba9a434b11f9d0d9a0c907c3","sha256:012ddfce3a6d678730e765c7fba8601ea93887c4b3721ad7eeff1a225e3775f6"],"state_sha256":"7cd65e888f387b87633d1c6d3db12c7532a2e0716ca1e898dea6d4e5b2c5cfa2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VKsxjpFwGIoSQLxcEVIhjU9fIZ95Q9yOX2aYU3njZlq6X26EhG2QAro/us8agN6yb12vmdUJnMBVPWVAYTSsBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T15:42:03.691358Z","bundle_sha256":"687e2c42dd584d4a07bffeb81ee52e6d60cfc7d0cd86bb855e14fe103b0a6c57"}}