자료유형 | 학위논문 |
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서명/저자사항 | Elastic Cloud Computing for QOS-aware Data Processing. |
개인저자 | Imai, Shigeru. |
단체저자명 | Rensselaer Polytechnic Institute. Computer Science. |
발행사항 | [S.l.]: Rensselaer Polytechnic Institute., 2018. |
발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
형태사항 | 143 p. |
기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
ISBN | 9780438206861 |
학위논문주기 | Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2018. |
일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Carlos A. Varela. |
요약 | Infrastructure-as-a-Service (IaaS) clouds such as Amazon EC2 offer various types of virtual machines (VMs) through pay-per-use pricing. Elastic resource allocation allows us to allocate and release VMs as computing demand changes while satisfyin |
요약 | First, we present two frameworks for elastic batch data processing. The first elastic batch data processing framework supports autonomous VM scaling using application-level migration. It does not require any prior knowledge about the target appl |
요약 | Next, we propose an elastic micro-batch data processing framework for continuous air traffic optimization. Air traffic optimization is commonly formulated as an integer linear programming (ILP) problem. For continuous optimization, we periodical |
요약 | Finally, we propose a framework for sustainable elastic stream processing based on the concept of Maximum Sustainable Throughput ( MST). It is the maximum processing throughput a streaming application can process indefinitely for a number of VMs |
요약 | Our studies show that QoS-aware elastic data processing is effective for these processing models in both performance scalability and cost savings. For batch processing, elastic resource scheduling helps achieve the target QoS metrics such as CPU |
일반주제명 | Computer science. |
언어 | 영어 |
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