{"_buckets": {"deposit": "e202cd21-e608-49a8-b1c7-aa0f0ebf547d"}, "_deposit": {"created_by": 155, "id": "7324", "owner": "155", "owners": [155], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "depid", "value": "7324"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/00007324", "sets": ["1608151108672", "1608152413915", "user-mmu"]}, "communities": ["mmu"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Electricity Price Prediction for Geographically Distributed Data Centers in Multi-Region Electricity Markets", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Geographically distributed data centers (GDCs)\nserving as infrastructures for cloud services, are growing in\nboth number and scale. They usually consume enormous\namount of electric power, which lead to high operational costs\nand this has been recognized as a main challenge in cloud\ncomputing. Energy cost can be reduced by directing the\nrequests to the favor of data center with lower electricity price\nby incorporating spatially and temporally price diversity,\nespecially in the multi-region electricity markets. If the\nelectricity prices of data centers are predicted in advance, the\ncloud provider can reduce energy cost. An efficient electricity\nprice prediction is needed for minimizing electricity bill of\nGDCs. This paper proposes electricity price prediction for\nGDCs in multi-region electricity markets. Experiment is\nconducted on real-life electricity price data sets with machine\nlearning algorithms. By comparatively assessing the prediction\naccuracy of the models, the most accurate one is selected.\nExperiment results show that the prediction model can provide\npromising accuracy."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "electricity price prediction, geographically distributed data centers, machine learning, multi-region electricity markets"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_login", "date": [{"dateType": "Available", "dateValue": "2021-01-08"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "MohMohThan(CS).pdf", "filesize": [{"value": "2.7 MB"}], "format": "application/pdf", "future_date_message": "Restricted Access", "is_thumbnail": false, "licensetype": "license_0", "mimetype": "application/pdf", "size": 2700000.0, "url": {"url": "https://meral.edu.mm/record/7324/files/MohMohThan(CS).pdf"}, "version_id": "c5f9477b-f718-4a1c-9865-e7302bc1461c"}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "Prediction", "subitem_c_date": "27-30 April 2018", "subitem_conference_title": "2018 3rd International Conference on Computer and Communication Systems (ICCCS)", "subitem_place": "Nagoya, Japan", "subitem_session": "Data Theory and Engineering", "subitem_website": "http://www.icccs.org"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Moh Moh Than, Thandar Thein"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Conference paper"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2018-09-13"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "10.1109/CCOMS.2018.8463272"}, "item_title": "Electricity Price Prediction for Geographically Distributed Data Centers in Multi-Region Electricity Markets", "item_type_id": "21", "owner": "155", "path": ["1608151108672", "1608152413915"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000007324", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2021-01-08"}, "publish_date": "2021-01-08", "publish_status": "0", "recid": "7324", "relation": {}, "relation_version_is_last": true, "title": ["Electricity Price Prediction for Geographically Distributed Data Centers in Multi-Region Electricity Markets"], "weko_shared_id": -1}