{"created":"2020-09-14T03:56:57.358936+00:00","id":5341,"links":{},"metadata":{"_buckets":{"deposit":"f2839568-b077-4848-b0b1-17a0b73c04df"},"_deposit":{"created_by":45,"id":"5341","owner":"45","owners":[45],"owners_ext":{"displayname":"","email":"dimennyaung@uit.edu.mm","username":""},"pid":{"revision_id":0,"type":"recid","value":"5341"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/5341","sets":["1582963342780:1596102391527"]},"communities":["uit"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Optimum Checkpoint Interval for MapReduce Fault-Tolerance","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.\nIn such a cluster, task failures can impact on performance\nof applications. Although MapReduce automatically\nreschedules the failed tasks, it takes long completion time\nbecause it starts from scratch. The checkpointing\nmechanism is the valuable technique to avoid reexecution\nof failed tasks in MapReduce. However,\ndefining incorrect checkpoint interval can still decrease\nthe performance of MapReduce applications and job\ncompletion time. In this paper, the optimum checkpoint\ninterval is proposed to reduce MapReduce job completion\ntime when failures occur. The proposed system defines\ncheckpoint interval that is based on five parameters:\nexpected job completion time without checkpointing,\ncheckpoint overhead time, rework time, down time and\nrestart time. Therefore, because of proposed checkpoint\ninterval, MapReduce does not need to re-execute the\nfailed tasks, so it reduces job completion time when\nfailures occur. The proposed system reduces job\ncompletion time even though the number of failures\nincreases and the performance of this system can be\nimproved 4 times better than the original MapReduce."}]},"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_access","date":[{"dateType":"Available","dateValue":"2020-09-14"}],"displaytype":"preview","filename":"Optimum Checkpoint Interval for MapReduce Fault-Tolerance.pdf","filesize":[{"value":"471 Kb"}],"format":"application/pdf","licensetype":"license_0","url":{"url":"https://meral.edu.mm/record/5341/files/Optimum Checkpoint Interval for MapReduce Fault-Tolerance.pdf"},"version_id":"72ca6625-206c-430e-9f24-2098f6fd26f2"}]},"item_1583103147082":{"attribute_name":"Conference papers","attribute_value_mlt":[{"subitem_acronym":"ICAIT","subitem_c_date":"1 November, 2017","subitem_conference_title":"International Conference on Advanced Information Technologies","subitem_place":"Yangon, Myanamar","subitem_website":"https://www.uit.edu.mm/icait-2017/"}]},"item_1583105942107":{"attribute_name":"Authors","attribute_value_mlt":[{"subitem_authors":[{"subitem_authors_fullname":"Naychi Nway Nway"},{"subitem_authors_fullname":"Julia Myint"}]}]},"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":"2017-11-01"},"item_title":"Optimum Checkpoint Interval for MapReduce Fault-Tolerance","item_type_id":"21","owner":"45","path":["1596102391527"],"publish_date":"2020-09-14","publish_status":"0","recid":"5341","relation_version_is_last":true,"title":["Optimum Checkpoint Interval for MapReduce Fault-Tolerance"],"weko_creator_id":"45","weko_shared_id":-1},"updated":"2021-12-13T06:53:02.656723+00:00"}