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Quality of Service for Performance-Critical Cloud Applications
Quality of Service for Performance-Critical Cloud Applications
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152951
- ISBN
- 9798384041825
- DDC
- 004
- 저자명
- Zhang, Yiwen.
- 서명/저자
- Quality of Service for Performance-Critical Cloud Applications
- 발행사항
- [Sl] : University of Michigan, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 182 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
- 주기사항
- Advisor: Chowdhury, Mosharaf.
- 학위논문주기
- Thesis (Ph.D.)--University of Michigan, 2024.
- 초록/해제
- 요약Cloud infrastructure continues to scale due to rapid evolvement of both hardware and software technologies in recent years. On the one hand, recent hardware advancement such as accelerators, kernel-bypass networks, and high-speed interconnect brings more powerful computing devices, faster networking equipment, and larger data storage. On the other hand, new software technologies such as computer vision and natural language processing introduce more workloads across datacenters and the edge. As a result, more and more applications from many tenants with different performance requirements must share the compute and network resources to improve resource utilization. Therefore, it is more important than ever to ensure performance-critical applications receive the appropriate level of priority and service quality. This dissertation aims to build system support for better quality of service (QoS) for performance-critical applications in the cloud. Specifically, we aim to provide guaranteed performance specified by service level objectives (SLOs) for multiple coexisting applications while maximizing system resource utilization. Unfortunately, we observe that existing cloud infrastructure lacks QoS support in multiple critical places including network interface cards (NICs), datacenter fabrics, edge devices and tiered memory systems, each of which requires unique QoS-aware system design to ensure predictable application performance. To this end, we have built software solutions to provide better QoS in each of the aforementioned areas. First, we built Justitia to provide performance isolation and fairness in the NIC for kernel-bypass networks (KBNs). Justitia overcomes the unique challenges in KBN with several innovations, including split connections with message-level shaping, sender-based resource mediation with receiver-side updates, and passive latency monitoring. Second, we built Aequitas to provide QoS for latency-critical remote procedure calls (RPCs) inside datacenter networks. Aequitas is a distributed sender-driven admission control scheme that uses commodity Weighted-Fair Queuing (WFQ) to guarantee RPC-level SLOs. It enforces cluster-wide RPC latency SLOs via probabilistic downgrading in order to limit the amount of traffic admitted into different QoS levels. Third, we built Vulcan to automatically generate query plans for live ML queries based on their accuracy and end-to-end latency requirements, while minimizing resource consumption across the edge. Vulcan determines the best pipeline, placement, and query configuration by combining several techniques including Bayesian Optimization and memorizing intermediate results of pipeline operators. Finally, we built Mercury, a QoS-aware tiered memory system to provide predictable performance for memory-intensive applications. Mercury proposes a new resource management scheme inside the kernel tailored for tiered memory systems. It leverages a novel admission control and a real-time adaptation algorithm to ensure QoS guarantees for both latency-sensitive and bandwidth-intensive applications. Together, these solutions provide the missing pieces from the edge to the cloud to enable QoS for performance-critical cloud applications.
- 일반주제명
- Computer science
- 일반주제명
- Computer engineering
- 일반주제명
- Systems science
- 키워드
- Computer memory
- 키워드
- Machine learning
- 키워드
- Cloud
- 기타저자
- University of Michigan Computer Science & Engineering
- 기본자료저록
- Dissertations Abstracts International. 86-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211152951
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■007cr#unu||||||||
■020 ▼a9798384041825
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■035 ▼a(MiAaPQ)umichrackham005859
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aZhang, Yiwen.
■24510▼aQuality of Service for Performance-Critical Cloud Applications
■260 ▼a[Sl]▼bUniversity of Michigan▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a182 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: B.
■500 ▼aAdvisor: Chowdhury, Mosharaf.
■5021 ▼aThesis (Ph.D.)--University of Michigan, 2024.
■520 ▼aCloud infrastructure continues to scale due to rapid evolvement of both hardware and software technologies in recent years. On the one hand, recent hardware advancement such as accelerators, kernel-bypass networks, and high-speed interconnect brings more powerful computing devices, faster networking equipment, and larger data storage. On the other hand, new software technologies such as computer vision and natural language processing introduce more workloads across datacenters and the edge. As a result, more and more applications from many tenants with different performance requirements must share the compute and network resources to improve resource utilization. Therefore, it is more important than ever to ensure performance-critical applications receive the appropriate level of priority and service quality. This dissertation aims to build system support for better quality of service (QoS) for performance-critical applications in the cloud. Specifically, we aim to provide guaranteed performance specified by service level objectives (SLOs) for multiple coexisting applications while maximizing system resource utilization. Unfortunately, we observe that existing cloud infrastructure lacks QoS support in multiple critical places including network interface cards (NICs), datacenter fabrics, edge devices and tiered memory systems, each of which requires unique QoS-aware system design to ensure predictable application performance. To this end, we have built software solutions to provide better QoS in each of the aforementioned areas. First, we built Justitia to provide performance isolation and fairness in the NIC for kernel-bypass networks (KBNs). Justitia overcomes the unique challenges in KBN with several innovations, including split connections with message-level shaping, sender-based resource mediation with receiver-side updates, and passive latency monitoring. Second, we built Aequitas to provide QoS for latency-critical remote procedure calls (RPCs) inside datacenter networks. Aequitas is a distributed sender-driven admission control scheme that uses commodity Weighted-Fair Queuing (WFQ) to guarantee RPC-level SLOs. It enforces cluster-wide RPC latency SLOs via probabilistic downgrading in order to limit the amount of traffic admitted into different QoS levels. Third, we built Vulcan to automatically generate query plans for live ML queries based on their accuracy and end-to-end latency requirements, while minimizing resource consumption across the edge. Vulcan determines the best pipeline, placement, and query configuration by combining several techniques including Bayesian Optimization and memorizing intermediate results of pipeline operators. Finally, we built Mercury, a QoS-aware tiered memory system to provide predictable performance for memory-intensive applications. Mercury proposes a new resource management scheme inside the kernel tailored for tiered memory systems. It leverages a novel admission control and a real-time adaptation algorithm to ensure QoS guarantees for both latency-sensitive and bandwidth-intensive applications. Together, these solutions provide the missing pieces from the edge to the cloud to enable QoS for performance-critical cloud applications.
■590 ▼aSchool code: 0127.
■650 4▼aComputer science
■650 4▼aComputer engineering
■650 4▼aSystems science
■653 ▼aQuality of service
■653 ▼aComputer networks
■653 ▼aComputer memory
■653 ▼aMachine learning
■653 ▼aCloud
■690 ▼a0984
■690 ▼a0464
■690 ▼a0790
■71020▼aUniversity of Michigan▼bComputer Science & Engineering.
■7730 ▼tDissertations Abstracts International▼g86-03B.
■790 ▼a0127
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164350▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
