{"_buckets": {"deposit": "4c53ce2f-9bbd-407d-a8d6-e05417de6bef"}, "_deposit": {"created_by": 45, "id": "5342", "owner": "45", "owners": [45], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "recid", "value": "5342"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/5342", "sets": ["user-uit"]}, "communities": ["uit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Evaluating Checkpoint Interval for Fault-Tolerance in MapReduce", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "MapReduce is the efficient framework for parallel\nprocessing of distributed big data in cluster environment. In\nsuch a cluster, task failures can impact on performance of\napplications. Although MapReduce automatically reschedules\nthe failed tasks, it takes long completion time because it starts\nfrom scratch. The checkpointing mechanism is the valuable\ntechnique to avoid re-execution of finished tasks in\nMapReduce. However, defining incorrect checkpoint interval\ncan still decrease the performance of MapReduce applications\nand job completion time. So, in this paper, checkpoint interval\nis proposed to avoid re-execution of whole tasks in case of task\nfailures and save job completion time. The proposed\ncheckpoint interval is based on five parameters: expected job\ncompletion time without checkpointing, checkpoint overhead\ntime, rework time, down time and restart time. The\nexperiments show that the proposed checkpoint interval takes\nthe advantage of less checkpoints overhead and reduce\ncompletion time at failure time."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "MapReduce"}, {"interim": "Big data"}, {"interim": "Task failures"}, {"interim": "Completion time"}, {"interim": "Checkpoint interval"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_no", "date": [{"dateType": "Available", "dateValue": "2020-09-14"}], "displaytype": "preview", "filename": "Evaluating Checkpoint Interval for Fault-Tolerance in MapReduce.pdf", "filesize": [{"value": "286 Kb"}], "format": "application/pdf", "licensefree": "©2018 IEEE", "licensetype": "license_free", "url": {"url": "https://meral.edu.mm/record/5342/files/Evaluating Checkpoint Interval for Fault-Tolerance in MapReduce.pdf"}, "version_id": "b5a0a9fd-da1d-4f42-b9a7-1c3a072c7ded"}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "Cyber C", "subitem_c_date": "18 October, 2018", "subitem_conference_title": "2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery", "subitem_place": "Zhengzhou, China, China"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Naychi Nway Nway"}, {"subitem_authors_fullname": "Julia Myint"}, {"subitem_authors_fullname": "Ei Chaw Htoon"}]}]}, "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": "2018-10-18"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "10.1109/CyberC.2018.00046"}, "item_title": "Evaluating Checkpoint Interval for Fault-Tolerance in MapReduce", "item_type_id": "21", "owner": "45", "path": ["1596102391527"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000005342", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-09-14"}, "publish_date": "2020-09-14", "publish_status": "0", "recid": "5342", "relation": {}, "relation_version_is_last": true, "title": ["Evaluating Checkpoint Interval for Fault-Tolerance in MapReduce"], "weko_shared_id": -1}