Abstract
Information Technology, as an industry, is growing very quickly to keep pace with increased data storage and computing needs. Data growth, if not planned or managed correctly, can have larger efficiency implications on your data center as a whole. The long term reduction in efficiency will increase costs over time and increase operational overhead. Similarly, increases in processor efficiency have led to increased system density in data centers. This can increase cost and operational overhead in your data center infrastructure.
This paper proposes the idea that balanced data center workloads are more efficient in comparison to similar levels of data center workloads that are not balanced across the data center facility. Identifying and documenting this effect would enable system architects to better plan data center system expansions and migrations effectively, while keeping in mind the total cost of the data center facility.
I conducted a scale experiment of data center heat job placement and collected data during multiple data runs. The scale experiment apparatus will allow the researchers to directly control the utilization of servers at all times while collecting data. Data collected will include processor utilization as well as the temperatures of both the hot and cold aisles during the entirety of the experimental procedure.
The experimental research hopes to show that the balanced workload has a positive effect on the temperature difference observed across the hot and cold aisle for the balanced workload tests. A rise in temperature difference would support the conclusion that is proposed by this research.
","abstract_html":"<p>Abstract</p> <p>Information Technology, as an industry, is growing very quickly to keep pace with increased data storage and computing needs. Data growth, if not planned or managed correctly, can have larger efficiency implications on your data center as a whole. The long term reduction in efficiency will increase costs over time and increase operational overhead. Similarly, increases in processor efficiency have led to increased system density in data centers. This can increase cost and operational overhead in your data center infrastructure.</p> <p>This paper proposes the idea that balanced data center workloads are more efficient in comparison to similar levels of data center workloads that are not balanced across the data center facility. Identifying and documenting this effect would enable system architects to better plan data center system expansions and migrations effectively, while keeping in mind the total cost of the data center facility.</p> <p>I conducted a scale experiment of data center heat job placement and collected data during multiple data runs. The scale experiment apparatus will allow the researchers to directly control the utilization of servers at all times while collecting data. Data collected will include processor utilization as well as the temperatures of both the hot and cold aisles during the entirety of the experimental procedure.</p> <p>The experimental research hopes to show that the balanced workload has a positive effect on the temperature difference observed across the hot and cold aisle for the balanced workload tests. A rise in temperature difference would support the conclusion that is proposed by this research.</p>","abstract_has_math":false,"creators":["Kimmons, Brandon"],"institution":null,"degree_name":"Master of Science in Applied Engineering (M.S.A.E.)","degree_level":"Thesis (open access)","degree_discipline":"Department of Electrical Engineering","degree_department":null,"school":null,"contributors":["John O'Malley","Russell Thackston"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-01-01T08:00:00Z","date_published":"2015-01-01T08:00:00Z","updated_at":"2026-07-24T02:28:15Z","subjects":["ETD","Data center","Computer cooling","Power efficiency","Job placement","Virtualization","Hypervisor","Compute load","Clustered systems","Systems automation","Computer and Systems Architecture","Data Storage Systems","Digital Communications and Networking","Hardware Systems","Other Computer Engineering","Power and Energy","Systems and Communications"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.georgiasouthern.edu/etd/1249","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["John O'Malley","Russell Thackston"]},{"key":"dc:creator","label":"Author","values":["Kimmons, Brandon"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2015-04-03T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Department of Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis (open access)"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Applied Engineering (M.S.A.E.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["ETD","Data center","Computer cooling","Power efficiency","Job placement","Virtualization","Hypervisor","Compute load","Clustered systems","Systems automation","Computer and Systems Architecture","Data Storage Systems","Digital Communications and Networking","Hardware Systems","Other Computer Engineering","Power and Energy","Systems and Communications"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.georgiasouthern.edu/etd/1249"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Abstract
Information Technology, as an industry, is growing very quickly to keep pace with increased data storage and computing needs. Data growth, if not planned or managed correctly, can have larger efficiency implications on your data center as a whole. The long term reduction in efficiency will increase costs over time and increase operational overhead. Similarly, increases in processor efficiency have led to increased system density in data centers. This can increase cost and operational overhead in your data center infrastructure.
This paper proposes the idea that balanced data center workloads are more efficient in comparison to similar levels of data center workloads that are not balanced across the data center facility. Identifying and documenting this effect would enable system architects to better plan data center system expansions and migrations effectively, while keeping in mind the total cost of the data center facility.
I conducted a scale experiment of data center heat job placement and collected data during multiple data runs. The scale experiment apparatus will allow the researchers to directly control the utilization of servers at all times while collecting data. Data collected will include processor utilization as well as the temperatures of both the hot and cold aisles during the entirety of the experimental procedure.
The experimental research hopes to show that the balanced workload has a positive effect on the temperature difference observed across the hot and cold aisle for the balanced workload tests. A rise in temperature difference would support the conclusion that is proposed by this research.
"]},{"key":"dc:title","label":"Title","values":["Comparing the Efficiency of Heterogeneous and Homogeneous Data Center Workloads"]}]}],"canonical_facts":{"dc:contributor":["John O'Malley","Russell Thackston"],"dc:creator":["Kimmons, Brandon"],"dc:date.available":["2015-04-03T07:00:00Z"],"dc:description.abstract":["Abstract
Information Technology, as an industry, is growing very quickly to keep pace with increased data storage and computing needs. Data growth, if not planned or managed correctly, can have larger efficiency implications on your data center as a whole. The long term reduction in efficiency will increase costs over time and increase operational overhead. Similarly, increases in processor efficiency have led to increased system density in data centers. This can increase cost and operational overhead in your data center infrastructure.
This paper proposes the idea that balanced data center workloads are more efficient in comparison to similar levels of data center workloads that are not balanced across the data center facility. Identifying and documenting this effect would enable system architects to better plan data center system expansions and migrations effectively, while keeping in mind the total cost of the data center facility.
I conducted a scale experiment of data center heat job placement and collected data during multiple data runs. The scale experiment apparatus will allow the researchers to directly control the utilization of servers at all times while collecting data. Data collected will include processor utilization as well as the temperatures of both the hot and cold aisles during the entirety of the experimental procedure.
The experimental research hopes to show that the balanced workload has a positive effect on the temperature difference observed across the hot and cold aisle for the balanced workload tests. A rise in temperature difference would support the conclusion that is proposed by this research.
"],"dc:identifier":["https://digitalcommons.georgiasouthern.edu/etd/1249"],"dc:subject":["ETD","Data center","Computer cooling","Power efficiency","Job placement","Virtualization","Hypervisor","Compute load","Clustered systems","Systems automation","Computer and Systems Architecture","Data Storage Systems","Digital Communications and Networking","Hardware Systems","Other Computer Engineering","Power and Energy","Systems and Communications"],"dc:title":["Comparing the Efficiency of Heterogeneous and Homogeneous Data Center Workloads"],"thesis:degree_discipline":["Department of Electrical Engineering"],"thesis:degree_level":["Thesis (open access)"],"thesis:degree_name":["Master of Science in Applied Engineering (M.S.A.E.)"]},"updated_at":"2026-07-24T02:28:15Z"}