國立虎尾科技大學 |

Elastic Cloud Computing for QOS-aware Data Processing.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Elastic Cloud Computing for QOS-aware Data Processing./
作者:	Imai, Shigeru.
面頁冊數:	1 online resource (143 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Contained By:	Dissertation Abstracts International79-12B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780438206861

Elastic Cloud Computing for QOS-aware Data Processing.
Imai, Shigeru.

Elastic Cloud Computing for QOS-aware Data Processing. - 1 online resource (143 pages)

Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.

Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2018.

Includes bibliographical references

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 satisfying Quality-of-Service (QoS) requirements. In this thesis, we explore QoS-aware elastic resource allocation for three different data processing models: batch, micro-batch, and streaming.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438206861Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Elastic Cloud Computing for QOS-aware Data Processing.
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500 $a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 $a Adviser: Carlos A. Varela.
502 $a Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2018.
504 $a Includes bibliographical references
520 $a 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 satisfying Quality-of-Service (QoS) requirements. In this thesis, we explore QoS-aware elastic resource allocation for three different data processing models: batch, micro-batch, and streaming.
520 $a 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 application, but dynamically reconfigures the application to keep the CPU utilization within a certain range. The second framework uses Workload-tailored Elastic Compute Units as a measure of computing resources analogous to Amazon EC2's ECUs. Given a deadline, our framework finds the cost-optimal resource configuration of heterogeneous VMs to satisfy the required throughput.
520 $a 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 periodically solve ILP problems with regular intervals, where each problem is a micro-batch data processing job. Since the fluctuating number of flights creates dynamically changing computational demand, our framework predicts future workload and proactively schedules VMs to solve the ILP problems in a timely manner.
520 $a 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. Stream processing is sustainable if the system's MST is always greater than the input data rates of incoming workload. Using MST and future workload prediction models, our framework proactively schedules VMs to keep the stream processing sustainable. It explicitly incorporates uncertainties in both MST and workload prediction models, and estimates the number of VMs to satisfy a certain probability criteria.
520 $a 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 utilization and job completion time. For both micro-batch and stream processing with fluctuating workloads, QoS-aware elastic scheduling saves up to 49% cost compared to a static scheduling that covers the peak workload to achieve a similar level of QoS. These results show potential for future fully automated cloud computing resource management systems that efficiently enable truly elastic and scalable general-purpose workload.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Rensselaer Polytechnic Institute. $b Computer Science. $3 1190468
773 0 $t Dissertation Abstracts International $g 79-12B(E).
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