Log in
Language:

MERAL Myanmar Education Research and Learning Portal

  • Top
  • Universities
  • Ranking
To
lat lon distance
To

Field does not validate



Index Link

Index Tree

Please input email address.

WEKO

One fine body…

WEKO

One fine body…

Item

{"_buckets": {"deposit": "19eaef79-b829-43e5-85fd-c1a68637720a"}, "_deposit": {"id": "3865", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "3865"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/3865", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Elastic Resource Prediction for Cloud Data Center", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Cloud data centers offer utility-oriented ITservices to users worldwide. The nature ofresource demand of cloud data centers is elasticas the overall workloads are always changing.For handling dynamic workload nature ahead ofthe needs, elastic resource demand prediction isthe key issue in cloud data centers. If the cloudprovider does not ensure they have enoughresources to meet demand which will lead tounder or over provisioning of resources. In thispaper, integrated elastic resource predictionsystem is proposed by combining signaturebasedprediction and state-based predictionapproahces. The workload nature of the clouddata centers are both repeating pattern and nonrepeatingpattern workload. Signature-basedprediction is used to predict the repeatingpattern workload and state-based prediction isused to predict the non-repeating patternworkload. Integrated Elastic ResourcePrediction (IERP) system is used to predict themixed workload pattern. Feature selection isconducted first to reduce processing overheadswhile achieving high prediction accuracy. Theproposed predictors are implemented andevaluated with real world workload traces whichshow that they achieve high resource prediction accuracy with above 95%."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Cloud Data Center"}, {"interim": "Integrated Elastic Resource Prediction"}, {"interim": "Signature-based Prediction"}, {"interim": "State-based Prediction"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-07-03"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "12059.pdf", "filesize": [{"value": "1076 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 1076000.0, "url": {"url": "https://meral.edu.mm/record/3865/files/12059.pdf"}, "version_id": "22b067ce-1c78-4eb5-924e-6fcd76dbb74b"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Twelfth International Conference On Computer Applications (ICCA 2014)", "subitem_pages": "", "subitem_volume": ""}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "", "subitem_c_date": "", "subitem_conference_title": "", "subitem_part": "", "subitem_place": "", "subitem_session": "", "subitem_website": ""}]}, "item_1583103211336": {"attribute_name": "Books/reports/chapters", "attribute_value_mlt": [{"subitem_book_title": "", "subitem_isbn": "", "subitem_pages": "", "subitem_place": "", "subitem_publisher": ""}]}, "item_1583103233624": {"attribute_name": "Thesis/dissertations", "attribute_value_mlt": [{"subitem_awarding_university": "", "subitem_supervisor(s)": [{"subitem_supervisor": ""}]}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Tun, Thant Zin"}, {"subitem_authors_fullname": "Thein, Thandar"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Article"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2014-02-17"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/159"}, "item_title": "Elastic Resource Prediction for Cloud Data Center", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000003865", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-03"}, "publish_date": "2019-07-03", "publish_status": "0", "recid": "3865", "relation": {}, "relation_version_is_last": true, "title": ["Elastic Resource Prediction for Cloud Data Center"], "weko_shared_id": -1}
  1. University of Computer Studies, Yangon
  2. Conferences

Elastic Resource Prediction for Cloud Data Center

http://hdl.handle.net/20.500.12678/0000003865
http://hdl.handle.net/20.500.12678/0000003865
ee8206e5-ddce-48ad-a9e6-f956c5e3c8ba
19eaef79-b829-43e5-85fd-c1a68637720a
None
Preview
Name / File License Actions
12059.pdf 12059.pdf (1076 Kb)
Publication type
Article
Upload type
Publication
Title
Title Elastic Resource Prediction for Cloud Data Center
Language en
Publication date 2014-02-17
Authors
Tun, Thant Zin
Thein, Thandar
Description
Cloud data centers offer utility-oriented ITservices to users worldwide. The nature ofresource demand of cloud data centers is elasticas the overall workloads are always changing.For handling dynamic workload nature ahead ofthe needs, elastic resource demand prediction isthe key issue in cloud data centers. If the cloudprovider does not ensure they have enoughresources to meet demand which will lead tounder or over provisioning of resources. In thispaper, integrated elastic resource predictionsystem is proposed by combining signaturebasedprediction and state-based predictionapproahces. The workload nature of the clouddata centers are both repeating pattern and nonrepeatingpattern workload. Signature-basedprediction is used to predict the repeatingpattern workload and state-based prediction isused to predict the non-repeating patternworkload. Integrated Elastic ResourcePrediction (IERP) system is used to predict themixed workload pattern. Feature selection isconducted first to reduce processing overheadswhile achieving high prediction accuracy. Theproposed predictors are implemented andevaluated with real world workload traces whichshow that they achieve high resource prediction accuracy with above 95%.
Keywords
Cloud Data Center, Integrated Elastic Resource Prediction, Signature-based Prediction, State-based Prediction
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/159
Journal articles
Twelfth International Conference On Computer Applications (ICCA 2014)
Conference papers
Books/reports/chapters
Thesis/dissertations
Back
0
0
views
downloads
See details
Views Downloads

Versions

Ver.1 2020-09-01 13:42:36.599157
Show All versions

Share

Mendeley Twitter Facebook Print Addthis

Export

OAI-PMH
  • OAI-PMH DublinCore
Other Formats
  • JSON

Confirm


Back to MERAL


Back to MERAL