{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124165"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124165","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"CATCloud: closing semantic gaps of CPU interfaces for precise autoscaling in the cloud","abstract":"Precise CPU allocation for a multi-programmed computer is crucial to application performance and resource efficiency, but is notoriously difficult under dynamic cloud workloads, where multiple users executing diverse applications often share the CPUs. We argue that the fundamental problem is rooted in the mismatch of the existing CPU allocation interface between the cloud and the OS---while the cloud represents CPU resources as a percentage quota of the host CPU (i.e., millicpu), the OS interprets CPU resources as time-shared quota slices allowed to run within a defined period. The cloud interface's disregard for periodicity stems from the fundamental difficulty of capturing fine-grained application runtime behavior in userspace. Consequently, existing solutions rely on coarse-grained, surrogate metrics such as CPU utilization, throttle, and queue lengths, leading to slow and imprecise allocation. We present CATCloud, an OS extension that closes the semantic gap of cloud CPU allocation. CATCloud views CPU resources as a shared bandwidth interface and implements a millisecond-scale CPU bandwidth autotuner for quota and periodicity. Implemented in the OS scheduler, CATCloud realizes observability of fine-grained run time and yield time behavior of target applications; which was previously opaque to the userspace autoscalers. By continuously capturing historical data, it accurately estimates the short-term CPU period and quota requirements. With an execution latency of only a few milliseconds, CATCloud can quickly and effectively react to bursty, dynamic workloads with simple statistical algorithms. We show that CATCloud significantly outperforms state-of-the-art techniques in terms of responsiveness, precision, and efficiency. Our evaluation on various cloud workloads shows that CATCloud can improve CPU efficiency by on an average of 27.8%, up to 81.3% and performance improvements on average of 27.91%, up to 152.5% with negligible memory and compute overheads, over existing autoscaling solutions","abstract_html":"Precise CPU allocation for a multi-programmed computer is crucial to application performance and resource efficiency, but is notoriously difficult under dynamic cloud workloads, where multiple users executing diverse applications often share the CPUs. We argue that the fundamental problem is rooted in the mismatch of the existing CPU allocation interface between the cloud and the OS---while the cloud represents CPU resources as a percentage quota of the host CPU (i.e., millicpu), the OS interprets CPU resources as time-shared quota slices allowed to run within a defined period. The cloud interface's disregard for periodicity stems from the fundamental difficulty of capturing fine-grained application runtime behavior in userspace. Consequently, existing solutions rely on coarse-grained, surrogate metrics such as CPU utilization, throttle, and queue lengths, leading to slow and imprecise allocation. We present CATCloud, an OS extension that closes the semantic gap of cloud CPU allocation. CATCloud views CPU resources as a shared bandwidth interface and implements a millisecond-scale CPU bandwidth autotuner for quota and periodicity. Implemented in the OS scheduler, CATCloud realizes observability of fine-grained run time and yield time behavior of target applications; which was previously opaque to the userspace autoscalers. By continuously capturing historical data, it accurately estimates the short-term CPU period and quota requirements. With an execution latency of only a few milliseconds, CATCloud can quickly and effectively react to bursty, dynamic workloads with simple statistical algorithms. We show that CATCloud significantly outperforms state-of-the-art techniques in terms of responsiveness, precision, and efficiency. Our evaluation on various cloud workloads shows that CATCloud can improve CPU efficiency by on an average of 27.8%, up to 81.3% and performance improvements on average of 27.91%, up to 152.5% with negligible memory and compute overheads, over existing autoscaling solutions","abstract_has_math":false,"creators":["Sampat, Pratik Rajesh"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Ghose, Saugata","Xu, Tianyin"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:00Z","subjects":["Operating System","Cpu Allocation","Autoscaling","Cloud Computing","Resource Management"],"languages":["eng","en"],"rights":["Copyright 2024 Pratik Rajesh Sampat"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124165","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ghose, Saugata","Xu, Tianyin"]},{"key":"dc:creator","label":"Author","values":["Sampat, Pratik Rajesh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-04-25"]},{"key":"dc:type","label":"Dc Type","values":["Text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"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":["Operating System","Cpu Allocation","Autoscaling","Cloud Computing","Resource Management"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng","en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Pratik Rajesh Sampat"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124165"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Precise CPU allocation for a multi-programmed computer is crucial to application performance and resource efficiency, but is notoriously difficult under dynamic cloud workloads, where multiple users executing diverse applications often share the CPUs. We argue that the fundamental problem is rooted in the mismatch of the existing CPU allocation interface between the cloud and the OS---while the cloud represents CPU resources as a percentage quota of the host CPU (i.e., millicpu), the OS interprets CPU resources as time-shared quota slices allowed to run within a defined period. The cloud interface's disregard for periodicity stems from the fundamental difficulty of capturing fine-grained application runtime behavior in userspace. Consequently, existing solutions rely on coarse-grained, surrogate metrics such as CPU utilization, throttle, and queue lengths, leading to slow and imprecise allocation. We present CATCloud, an OS extension that closes the semantic gap of cloud CPU allocation. CATCloud views CPU resources as a shared bandwidth interface and implements a millisecond-scale CPU bandwidth autotuner for quota and periodicity. Implemented in the OS scheduler, CATCloud realizes observability of fine-grained run time and yield time behavior of target applications; which was previously opaque to the userspace autoscalers. By continuously capturing historical data, it accurately estimates the short-term CPU period and quota requirements. With an execution latency of only a few milliseconds, CATCloud can quickly and effectively react to bursty, dynamic workloads with simple statistical algorithms. We show that CATCloud significantly outperforms state-of-the-art techniques in terms of responsiveness, precision, and efficiency. Our evaluation on various cloud workloads shows that CATCloud can improve CPU efficiency by on an average of 27.8%, up to 81.3% and performance improvements on average of 27.91%, up to 152.5% with negligible memory and compute overheads, over existing autoscaling solutions","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Pratik Rajesh Sampat, accepted the attached license on 2024-04-16 at 22:16.","The student, Pratik Rajesh Sampat, submitted this Thesis for approval on 2024-04-16 at 22:30.","This Thesis was approved for publication on 2024-04-25 at 13:53.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20278 on 2024-09-16 at 00:33:38"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["CATCloud: closing semantic gaps of CPU interfaces for precise autoscaling in the cloud"]}]}],"canonical_facts":{"dc:contributor":["Ghose, Saugata","Xu, Tianyin"],"dc:creator":["Sampat, Pratik Rajesh"],"dc:date":["2024-05","2024-04-25"],"dc:description":["Precise CPU allocation for a multi-programmed computer is crucial to application performance and resource efficiency, but is notoriously difficult under dynamic cloud workloads, where multiple users executing diverse applications often share the CPUs. We argue that the fundamental problem is rooted in the mismatch of the existing CPU allocation interface between the cloud and the OS---while the cloud represents CPU resources as a percentage quota of the host CPU (i.e., millicpu), the OS interprets CPU resources as time-shared quota slices allowed to run within a defined period. The cloud interface's disregard for periodicity stems from the fundamental difficulty of capturing fine-grained application runtime behavior in userspace. Consequently, existing solutions rely on coarse-grained, surrogate metrics such as CPU utilization, throttle, and queue lengths, leading to slow and imprecise allocation. We present CATCloud, an OS extension that closes the semantic gap of cloud CPU allocation. CATCloud views CPU resources as a shared bandwidth interface and implements a millisecond-scale CPU bandwidth autotuner for quota and periodicity. Implemented in the OS scheduler, CATCloud realizes observability of fine-grained run time and yield time behavior of target applications; which was previously opaque to the userspace autoscalers. By continuously capturing historical data, it accurately estimates the short-term CPU period and quota requirements. With an execution latency of only a few milliseconds, CATCloud can quickly and effectively react to bursty, dynamic workloads with simple statistical algorithms. We show that CATCloud significantly outperforms state-of-the-art techniques in terms of responsiveness, precision, and efficiency. Our evaluation on various cloud workloads shows that CATCloud can improve CPU efficiency by on an average of 27.8%, up to 81.3% and performance improvements on average of 27.91%, up to 152.5% with negligible memory and compute overheads, over existing autoscaling solutions","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Pratik Rajesh Sampat, accepted the attached license on 2024-04-16 at 22:16.","The student, Pratik Rajesh Sampat, submitted this Thesis for approval on 2024-04-16 at 22:30.","This Thesis was approved for publication on 2024-04-25 at 13:53.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20278 on 2024-09-16 at 00:33:38"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124165"],"dc:language":["eng","en"],"dc:rights":["Copyright 2024 Pratik Rajesh Sampat"],"dc:subject":["Operating System","Cpu Allocation","Autoscaling","Cloud Computing","Resource Management"],"dc:title":["CATCloud: closing semantic gaps of CPU interfaces for precise autoscaling in the cloud"],"dc:type":["Text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}