Smartly Handling Renewable Energy Instability in Supporting A Cloud Datacenter
The size and energy consumption of datacenters have been increasing significantly over the past years. As a result, datacenters' increasing electricity monetary cost, energy consumption and energy harmful gas emissions have become a severe problem. Renewable energy supply is widely seen as a pr...
Uloženo v:
| Vydáno v: | Proceedings - IEEE International Parallel and Distributed Processing Symposium s. 769 - 778 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
01.05.2020
|
| Témata: | |
| ISSN: | 1530-2075 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | The size and energy consumption of datacenters have been increasing significantly over the past years. As a result, datacenters' increasing electricity monetary cost, energy consumption and energy harmful gas emissions have become a severe problem. Renewable energy supply is widely seen as a promising solution. However, the instability of renewable energy brings about a new challenge since insufficient energy supply may lead to job running interruptions or failures. Though previous works attempt to more accurately predict the amount of produced renewable energy, due to the instability of its influencing factors (e.g., wind, temperature), sufficient renewable energy supply cannot be always guaranteed. To handle this problem, in this paper, we propose allocating jobs with the same service-level-objective (SLO) level to the same physical machine (PM) group, and power each PM group with renewable energy generators that have probability no less than its SLO to produce the amount no less than its energy demand. It ensures that insufficient renewable energy supply will not lead to SLO violations. We use a deep learning technique to predict the probability of producing amount no less than each value of each renewable energy source and predict the energy demands of each PM area. We formulate an optimization problem: how to match renewable energy resources with different instabilities to different PM groups as energy supply in order to minimize the number of SLO violations (due to interruption from insufficient renewable energy supply), total energy monetary cost and total carbon emission. We then use reinforcement learning method and linear programming method to solve the optimization problem. The real trace driven experiments show that our method can achieve much lower SLO violations, total energy monetary cost and total carbon emission compared to other methods. |
|---|---|
| AbstractList | The size and energy consumption of datacenters have been increasing significantly over the past years. As a result, datacenters' increasing electricity monetary cost, energy consumption and energy harmful gas emissions have become a severe problem. Renewable energy supply is widely seen as a promising solution. However, the instability of renewable energy brings about a new challenge since insufficient energy supply may lead to job running interruptions or failures. Though previous works attempt to more accurately predict the amount of produced renewable energy, due to the instability of its influencing factors (e.g., wind, temperature), sufficient renewable energy supply cannot be always guaranteed. To handle this problem, in this paper, we propose allocating jobs with the same service-level-objective (SLO) level to the same physical machine (PM) group, and power each PM group with renewable energy generators that have probability no less than its SLO to produce the amount no less than its energy demand. It ensures that insufficient renewable energy supply will not lead to SLO violations. We use a deep learning technique to predict the probability of producing amount no less than each value of each renewable energy source and predict the energy demands of each PM area. We formulate an optimization problem: how to match renewable energy resources with different instabilities to different PM groups as energy supply in order to minimize the number of SLO violations (due to interruption from insufficient renewable energy supply), total energy monetary cost and total carbon emission. We then use reinforcement learning method and linear programming method to solve the optimization problem. The real trace driven experiments show that our method can achieve much lower SLO violations, total energy monetary cost and total carbon emission compared to other methods. |
| Author | Wang, Haoyu Shen, Haiying Gao, Jiechao |
| Author_xml | – sequence: 1 givenname: Jiechao surname: Gao fullname: Gao, Jiechao organization: University of Virginia,Department of Computer Science,Charlottesville,VA,USA – sequence: 2 givenname: Haoyu surname: Wang fullname: Wang, Haoyu organization: University of Virginia,Department of Computer Science,Charlottesville,VA,USA – sequence: 3 givenname: Haiying surname: Shen fullname: Shen, Haiying organization: University of Virginia,Department of Computer Science,Charlottesville,VA,USA |
| BookMark | eNotj99KwzAcRqMouM09gQh5gdb8a5Ncjm5zhaHD6vVI019GpKajzZC-vRW9OjeH8_HN0U3oAiD0SElKKdFP5WF9qITUTKSMMJISQpS4QkstFZVM0VzJnFyjGc04SRiR2R2aD8MnmVwu9Ay9VF-mj-2IdyY0rQ8n_AYBvk3dAt4E6E8jLsMQTe1bH0fsA64u53PXx191hYu2uzR4baKxECL09-jWmXaA5T8X6GO7eS92yf71uSxW-8RPuzGRSnEjRcOymkpLJYimZnXmMgvWMMqEo9xCw2XuOFUEtGDO1dzxjCkAq_kCPfx1PQAcz72fXoxHTblWkvEfuitRjg |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/IPDPS47924.2020.00084 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE/IET Electronic Library (IEL) (UW System Shared) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISBN | 9781728168760 1728168767 |
| EISSN | 1530-2075 |
| EndPage | 778 |
| ExternalDocumentID | 9139872 |
| Genre | orig-research |
| GroupedDBID | 29O 6IE 6IF 6IH 6IK 6IL 6IN AAJGR AAWTH ABLEC ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK IPLJI OCL RIE RIL |
| ID | FETCH-LOGICAL-i203t-7883a74d25b17c17e4db2b5f5ceca2124f13ced376f3180e942ffb3f3528eec93 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 237 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000643734800074&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Wed Aug 27 02:33:56 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i203t-7883a74d25b17c17e4db2b5f5ceca2124f13ced376f3180e942ffb3f3528eec93 |
| PageCount | 10 |
| ParticipantIDs | ieee_primary_9139872 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-May |
| PublicationDateYYYYMMDD | 2020-05-01 |
| PublicationDate_xml | – month: 05 year: 2020 text: 2020-May |
| PublicationDecade | 2020 |
| PublicationTitle | Proceedings - IEEE International Parallel and Distributed Processing Symposium |
| PublicationTitleAbbrev | IPDPS |
| PublicationYear | 2020 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0020349 |
| Score | 2.265093 |
| Snippet | The size and energy consumption of datacenters have been increasing significantly over the past years. As a result, datacenters' increasing electricity... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 769 |
| SubjectTerms | Carbon dioxide Clouds Energy consumption Generators Renewable energy sources Schedules Servers |
| Title | Smartly Handling Renewable Energy Instability in Supporting A Cloud Datacenter |
| URI | https://ieeexplore.ieee.org/document/9139872 |
| WOSCitedRecordID | wos000643734800074&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07T8MwELZKxcBUoEW85YGR0Npx4mREfagsVURB6lbZzhlVKgkKKaj_Hl8S2oWFLbIiRbnYdz7nexByZ0XErBbSMyoIPcFU4OnISE-HxmVDxU0Q2spsQs5m0WIRJy1yv-PCAEAFPoMHvKz-5ae52eBRWR8lLCPpEu6BlGHN1do1V6iz0jB02CDuPyWjZC6k6y5cD8gRvlULmO4dVKoCMun879HHpLdn4tFkV2NOSAuyU9L5tWKgzcrsktn83c2B9ZZOUTXB3UqfXRL7Rl4UHVf0PoqwgFqUe0tXGUU7zxwlBN7oIx2u801KR6pUCNaEokdeJ-OX4dRrrBK8lXvzEjGBvpIi5YFm0jAJItVcBzYwYJSrTsIy30Dqsol1i3gAseDWat-itguAif0z0s7yDM4JZVxzBlzFbjMkpFRRGIlIp6nb-emBtcEF6WJ4lh-1Gsayiczl38NX5AjjX0MEr0m7LDZwQw7NV7n6LG6rT_gDp4Cd5g |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3LS8MwGA9jCnqauolvc_BoXZMmTXuUPdhwluIm7DaSNJHBbKV2yv57k7ZuFy_eQgmE5vF9-drfA4A7TQKkBWGO5NR3COLUEYFkjvCliYYcS-rr0myCRVEwn4dxA9xvuTBKqRJ8ph5ss_yXn2RybT-Vda2EZcBMwN2jhGC3YmttyyurtFJzdJAbdsdxP54SZuoLUwViC-CqJEx3HiplChm2_jf4EejsuHgw3maZY9BQ6Qlo_ZoxwPpstkE0fTe7YLWBI6ubYLrCFxPGvi0zCg5Kgh-0wIBKlnsDlym0hp6ZFRF4g4-wt8rWCezzglu4pso74HU4mPVGTm2W4CzNmxcWFehxRhJMBWISMUUSgQXVVCrJTX4iGnlSJSaeaHOMXRUSrLXwtFV3UUqG3iloplmqzgBEWGCkMA_NdYgwxgM_IIFIEnP3E67W9By07fQsPio9jEU9Mxd_P74FB6PZ82QxGUdPl-DQrkUFGLwCzSJfq2uwL7-K5Wd-Uy7nDw14oS0 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=Proceedings+-+IEEE+International+Parallel+and+Distributed+Processing+Symposium&rft.atitle=Smartly+Handling+Renewable+Energy+Instability+in+Supporting+A+Cloud+Datacenter&rft.au=Gao%2C+Jiechao&rft.au=Wang%2C+Haoyu&rft.au=Shen%2C+Haiying&rft.date=2020-05-01&rft.pub=IEEE&rft.eissn=1530-2075&rft.spage=769&rft.epage=778&rft_id=info:doi/10.1109%2FIPDPS47924.2020.00084&rft.externalDocID=9139872 |