Load variations whether in space or time pose a significant challenge to system designers. These load variations may induce inefficiencies such as load imbalance and over-provisioning, resulting in performance/power/cost overheads. The goal of my research is to mitigate such variation-induced overheads in multicore cloud servers.
First, I focus on power/performance overheads in on-chip networks of a multicore chip. We design an on-chip network that is robust in both performance and energy across applications for time- and space-varying loads. Existing flow control mechanisms that perform well at high (low) loads suffer power and/or energy overheads at low (high) loads. In contrast, our design dynamically adapts flow control to achieve power and performance of the better-suited flow-control mechanism at all loads.
Second, I target cost overheads resulting from time-varying loads for applications hosted in an Infrastructure-as-a-Service (IaaS) cloud. While IaaS clouds may enable significant cost-savings by allowing elastic provisioning, the uncertainty of time-varying loads impose additional cost to maintain quality of service. I demonstrate that, with some knowledge of the statistical properties of time-varying load, one can maximize cost-savings while satisfying response-time targets.
Finally, I propose to mitigate the impact of data popularity variations in cloud servers. Sharding is a common technique to partition data among scale-out servers. Unfortunately, skewed popularity of data-elements can cause significant load imbalance among shard servers, leading to response time degradation. I design an augmented variant of a well-known memory-caching system to identify and replicate popular read-mostly data elements, thus achieving better load balance and higher performance.
","abstract_html":"<p>Load variations whether in space or time pose a significant challenge to system designers. These load variations may induce inefficiencies such as load imbalance and over-provisioning, resulting in performance/power/cost overheads. The goal of my research is to mitigate such variation-induced overheads in multicore cloud servers.</p> <p>First, I focus on power/performance overheads in on-chip networks of a multicore chip. We design an on-chip network that is robust in both performance and energy across applications for time- and space-varying loads. Existing flow control mechanisms that perform well at high (low) loads suffer power and/or energy overheads at low (high) loads. In contrast, our design dynamically adapts flow control to achieve power and performance of the better-suited flow-control mechanism at all loads.</p> <p>Second, I target cost overheads resulting from time-varying loads for applications hosted in an Infrastructure-as-a-Service (IaaS) cloud. While IaaS clouds may enable significant cost-savings by allowing elastic provisioning, the uncertainty of time-varying loads impose additional cost to maintain quality of service. I demonstrate that, with some knowledge of the statistical properties of time-varying load, one can maximize cost-savings while satisfying response-time targets.</p> <p>Finally, I propose to mitigate the impact of data popularity variations in cloud servers. Sharding is a common technique to partition data among scale-out servers. Unfortunately, skewed popularity of data-elements can cause significant load imbalance among shard servers, leading to response time degradation. I design an augmented variant of a well-known memory-caching system to identify and replicate popular read-mostly data elements, thus achieving better load balance and higher performance.</p>","abstract_has_math":false,"creators":["Hong, Yu-Ju"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":["Mithuna S. Thottethodi","Anand Raghunathan","T. N. Vijaykumar","Vijay S. Pai"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-10-01T07:00:00Z","date_published":"2013-10-01T07:00:00Z","updated_at":"2026-07-24T03:53:02Z","subjects":["applied sciences","multicore cloud servers","load variations","on-chip networks","Computer Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://docs.lib.purdue.edu/open_access_dissertations/164","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Mithuna S. Thottethodi","Anand Raghunathan","T. N. Vijaykumar","Vijay S. Pai"]},{"key":"dc:creator","label":"Author","values":["Hong, Yu-Ju"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["applied sciences","multicore cloud servers","load variations","on-chip networks","Computer Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://docs.lib.purdue.edu/open_access_dissertations/164"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Load variations whether in space or time pose a significant challenge to system designers. These load variations may induce inefficiencies such as load imbalance and over-provisioning, resulting in performance/power/cost overheads. The goal of my research is to mitigate such variation-induced overheads in multicore cloud servers.
First, I focus on power/performance overheads in on-chip networks of a multicore chip. We design an on-chip network that is robust in both performance and energy across applications for time- and space-varying loads. Existing flow control mechanisms that perform well at high (low) loads suffer power and/or energy overheads at low (high) loads. In contrast, our design dynamically adapts flow control to achieve power and performance of the better-suited flow-control mechanism at all loads.
Second, I target cost overheads resulting from time-varying loads for applications hosted in an Infrastructure-as-a-Service (IaaS) cloud. While IaaS clouds may enable significant cost-savings by allowing elastic provisioning, the uncertainty of time-varying loads impose additional cost to maintain quality of service. I demonstrate that, with some knowledge of the statistical properties of time-varying load, one can maximize cost-savings while satisfying response-time targets.
Finally, I propose to mitigate the impact of data popularity variations in cloud servers. Sharding is a common technique to partition data among scale-out servers. Unfortunately, skewed popularity of data-elements can cause significant load imbalance among shard servers, leading to response time degradation. I design an augmented variant of a well-known memory-caching system to identify and replicate popular read-mostly data elements, thus achieving better load balance and higher performance.
"]},{"key":"dc:title","label":"Title","values":["Mitigating the Cost, Performance, and Power Overheads Induced by Load Variations in Multicore Cloud Servers"]}]}],"canonical_facts":{"dc:contributor":["Mithuna S. Thottethodi","Anand Raghunathan","T. N. Vijaykumar","Vijay S. Pai"],"dc:creator":["Hong, Yu-Ju"],"dc:description.abstract":["Load variations whether in space or time pose a significant challenge to system designers. These load variations may induce inefficiencies such as load imbalance and over-provisioning, resulting in performance/power/cost overheads. The goal of my research is to mitigate such variation-induced overheads in multicore cloud servers.
First, I focus on power/performance overheads in on-chip networks of a multicore chip. We design an on-chip network that is robust in both performance and energy across applications for time- and space-varying loads. Existing flow control mechanisms that perform well at high (low) loads suffer power and/or energy overheads at low (high) loads. In contrast, our design dynamically adapts flow control to achieve power and performance of the better-suited flow-control mechanism at all loads.
Second, I target cost overheads resulting from time-varying loads for applications hosted in an Infrastructure-as-a-Service (IaaS) cloud. While IaaS clouds may enable significant cost-savings by allowing elastic provisioning, the uncertainty of time-varying loads impose additional cost to maintain quality of service. I demonstrate that, with some knowledge of the statistical properties of time-varying load, one can maximize cost-savings while satisfying response-time targets.
Finally, I propose to mitigate the impact of data popularity variations in cloud servers. Sharding is a common technique to partition data among scale-out servers. Unfortunately, skewed popularity of data-elements can cause significant load imbalance among shard servers, leading to response time degradation. I design an augmented variant of a well-known memory-caching system to identify and replicate popular read-mostly data elements, thus achieving better load balance and higher performance.
"],"dc:identifier":["https://docs.lib.purdue.edu/open_access_dissertations/164"],"dc:subject":["applied sciences","multicore cloud servers","load variations","on-chip networks","Computer Engineering"],"dc:title":["Mitigating the Cost, Performance, and Power Overheads Induced by Load Variations in Multicore Cloud Servers"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T03:53:02Z"}