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": "c4bfe433-66da-4c0b-9d69-d2f0bf2a794a"}, "_deposit": {"id": "4715", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4715"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4715", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Cloud Infrastructure Resource Demand Prediction Model Using Parameter Optimization and Feature Selection", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Cloud computing offer highly scalable, andeconomical infrastructure for promising heterogeneousplatforms and various applications. According to thegrowing demand nature of cloud infrastructureresources, the cloud providers face the challenge ofperforming the effective resource management. Thispaper presents the development of the CPU resourcedemand prediction model for cloud infrastructure toovercome the critical issue of the cloud providers forworkload forecasting and optimal resourcemanagement. The model is developed based on thepowerful machine learning technique, Random Forests(RF) algorithm via the real data center workload traces.To get the best prediction model by RF, the parameteroptimization is performed. Moreover, some features ofworkload traces cannot influence in prediction and alsogive overheads in model development time. So, thefeature selection is applied to extract the importantfeatures. The performance evaluation of the proposedmodel against four workload traces is also presented."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Cloud Computing"}, {"interim": "Feature Selection"}, {"interim": "Machine Learning"}, {"interim": "Parameter Optimization"}, {"interim": "Random Forests"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value": []}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Tenth International Conference On Computer Applications (ICCA 2012)", "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": "Myo, Myint Myat"}, {"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": "2012-02-28"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/420"}, "item_title": "Cloud Infrastructure Resource Demand Prediction Model Using Parameter Optimization and Feature Selection", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004715", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-04"}, "publish_date": "2019-07-04", "publish_status": "0", "recid": "4715", "relation": {}, "relation_version_is_last": true, "title": ["Cloud Infrastructure Resource Demand Prediction Model Using Parameter Optimization and Feature Selection"], "weko_shared_id": -1}
  1. University of Computer Studies, Yangon
  2. Conferences

Cloud Infrastructure Resource Demand Prediction Model Using Parameter Optimization and Feature Selection

http://hdl.handle.net/20.500.12678/0000004715
http://hdl.handle.net/20.500.12678/0000004715
bae60231-85f0-45be-be96-de8985df1040
c4bfe433-66da-4c0b-9d69-d2f0bf2a794a
Publication type
Article
Upload type
Publication
Title
Title Cloud Infrastructure Resource Demand Prediction Model Using Parameter Optimization and Feature Selection
Language en
Publication date 2012-02-28
Authors
Myo, Myint Myat
Thein, Thandar
Description
Cloud computing offer highly scalable, andeconomical infrastructure for promising heterogeneousplatforms and various applications. According to thegrowing demand nature of cloud infrastructureresources, the cloud providers face the challenge ofperforming the effective resource management. Thispaper presents the development of the CPU resourcedemand prediction model for cloud infrastructure toovercome the critical issue of the cloud providers forworkload forecasting and optimal resourcemanagement. The model is developed based on thepowerful machine learning technique, Random Forests(RF) algorithm via the real data center workload traces.To get the best prediction model by RF, the parameteroptimization is performed. Moreover, some features ofworkload traces cannot influence in prediction and alsogive overheads in model development time. So, thefeature selection is applied to extract the importantfeatures. The performance evaluation of the proposedmodel against four workload traces is also presented.
Keywords
Cloud Computing, Feature Selection, Machine Learning, Parameter Optimization, Random Forests
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/420
Journal articles
Tenth International Conference On Computer Applications (ICCA 2012)
Conference papers
Books/reports/chapters
Thesis/dissertations
Back
0
0
views
downloads
See details
Views Downloads

Versions

Ver.1 2020-09-01 15:17:12.818251
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