{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/100882"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/100882","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"GreenMap: MapReduce with ultra-high-efficiency power delivery","abstract":"With the continuous growth of online services, energy consumption has become a significant fraction of the total cost of ownership of large data centers. Though much work in green computing has focused on improving efficiency for computation units such as CPU’s or servers, little attention has been paid to power delivery structures, such as voltage converters, which takes 10-20% of total energy consumption even before any computation takes place. Recently, a new power delivery architecture called series stack has been proposed in the power community, aiming to reduce conversion power loss. In series stack, servers are connected serially, and differential converters are used to regulate server voltage. However, to effectively reduce conversion loss in series stack, computation loads need to be balanced in real time. To balance load for series stack, we implemented GreenMap, a modified MapReduce framework on top of series stacks, that assigns tasks in synchronization. We evaluated the conversion loss of GreenMap on a small data center. At all loads, GreenMap achieves a 81x-138x reduction in conversion loss from commercial-grade high voltage converters used by today’s data centers. The saved power is equivalent to 15% reduction in total energy consumption. GreenMap also achieves 67%-80% reduction in conversion loss compared to Hadoop’s FIFO scheduler under serial stack structure. Based on the observation that the average response time of GreenMap suffers a degradation at low load, we further propose a modification of GreenMap with dynamic scaling to achieve a favorable tradeoff between response time and power efficiency.","abstract_html":"With the continuous growth of online services, energy consumption has become a significant fraction of the total cost of ownership of large data centers. Though much work in green computing has focused on improving efficiency for computation units such as CPU’s or servers, little attention has been paid to power delivery structures, such as voltage converters, which takes 10-20% of total energy consumption even before any computation takes place. Recently, a new power delivery architecture called series stack has been proposed in the power community, aiming to reduce conversion power loss. In series stack, servers are connected serially, and differential converters are used to regulate server voltage. However, to effectively reduce conversion loss in series stack, computation loads need to be balanced in real time. To balance load for series stack, we implemented GreenMap, a modified MapReduce framework on top of series stacks, that assigns tasks in synchronization. We evaluated the conversion loss of GreenMap on a small data center. At all loads, GreenMap achieves a 81x-138x reduction in conversion loss from commercial-grade high voltage converters used by today’s data centers. The saved power is equivalent to 15% reduction in total energy consumption. GreenMap also achieves 67%-80% reduction in conversion loss compared to Hadoop’s FIFO scheduler under serial stack structure. Based on the observation that the average response time of GreenMap suffers a degradation at low load, we further propose a modification of GreenMap with dynamic scaling to achieve a favorable tradeoff between response time and power efficiency.","abstract_has_math":false,"creators":["Su, Du"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Lu, Yi"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-04T20:26:27Z","date_published":"2018-09-04T20:26:27Z","updated_at":"2026-07-22T22:24:38Z","subjects":["Green computing, MapReduce, Load Balance"],"languages":["en"],"rights":["Copyright 2017 Du Su"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/100882","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Lu, Yi"]},{"key":"dc:creator","label":"Author","values":["Su, Du"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-04T20:26:27Z","2017-12-04","2018-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Green computing, MapReduce, Load Balance"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Du Su"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/100882"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["With the continuous growth of online services, energy consumption has become a significant fraction of the total cost of ownership of large data centers. Though much work in green computing has focused on improving efficiency for computation units such as CPU’s or servers, little attention has been paid to power delivery structures, such as voltage converters, which takes 10-20% of total energy consumption even before any computation takes place. Recently, a new power delivery architecture called series stack has been proposed in the power community, aiming to reduce conversion power loss. In series stack, servers are connected serially, and differential converters are used to regulate server voltage. However, to effectively reduce conversion loss in series stack, computation loads need to be balanced in real time. To balance load for series stack, we implemented GreenMap, a modified MapReduce framework on top of series stacks, that assigns tasks in synchronization. We evaluated the conversion loss of GreenMap on a small data center. At all loads, GreenMap achieves a 81x-138x reduction in conversion loss from commercial-grade high voltage converters used by today’s data centers. The saved power is equivalent to 15% reduction in total energy consumption. GreenMap also achieves 67%-80% reduction in conversion loss compared to Hadoop’s FIFO scheduler under serial stack structure. Based on the observation that the average response time of GreenMap suffers a degradation at low load, we further propose a modification of GreenMap with dynamic scaling to achieve a favorable tradeoff between response time and power efficiency.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-08-31 without embargo terms","The student, Du Su, accepted the attached license on 2017-12-04 at 10:25.","The student, Du Su, submitted this Thesis for approval on 2017-12-04 at 10:33.","This Thesis was approved for publication on 2017-12-04 at 13:19.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11824 on 2018-08-31 at 17:07:52","Made available in DSpace on 2018-09-04T20:26:27Z (GMT). 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Recently, a new power delivery architecture called series stack has been proposed in the power community, aiming to reduce conversion power loss. In series stack, servers are connected serially, and differential converters are used to regulate server voltage. However, to effectively reduce conversion loss in series stack, computation loads need to be balanced in real time. To balance load for series stack, we implemented GreenMap, a modified MapReduce framework on top of series stacks, that assigns tasks in synchronization. We evaluated the conversion loss of GreenMap on a small data center. At all loads, GreenMap achieves a 81x-138x reduction in conversion loss from commercial-grade high voltage converters used by today’s data centers. The saved power is equivalent to 15% reduction in total energy consumption. GreenMap also achieves 67%-80% reduction in conversion loss compared to Hadoop’s FIFO scheduler under serial stack structure. Based on the observation that the average response time of GreenMap suffers a degradation at low load, we further propose a modification of GreenMap with dynamic scaling to achieve a favorable tradeoff between response time and power efficiency.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-08-31 without embargo terms","The student, Du Su, accepted the attached license on 2017-12-04 at 10:25.","The student, Du Su, submitted this Thesis for approval on 2017-12-04 at 10:33.","This Thesis was approved for publication on 2017-12-04 at 13:19.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11824 on 2018-08-31 at 17:07:52","Made available in DSpace on 2018-09-04T20:26:27Z (GMT). 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