Cost-Aware Cloud Profiling, Prediction, and Provisioning as a Service

被引：18

作者：

Chard, Ryan ^{[1
]}

Chard, Kyle ^{[1
,2
]}

Wolski, Rich ^{[3
]}

Madduri, Ravi ^{[4
,5
]}

Ng, Bryan ^{[6
]}

Bubendorfer, Kris ^{[7
]}

Foster, Ian ^{[1
,2
]}

机构：

[1] Argonne Natl Lab, Argonne, IL 60439 USA

[2] Univ Chicago, Chicago, IL 60637 USA

[3] Univ Calif Santa Barbara, Santa Barbara, CA 93106 USA

[4] Univ Chicago, Computat Inst, Chicago, IL 60637 USA

[5] Argonne Natl Lab, Math & Comp Sci Div, Argonne, IL 60439 USA

[6] Victoria Univ Wellington, Wellington, New Zealand

[7] Victoria Univ Wellington, Network Engn, Wellington, New Zealand

来源：

IEEE CLOUD COMPUTING | 2017年 / 4卷 / 04期

关键词：

application profiling; cloud market prediction; cloud provisioning; multicloud;

D O I：

10.1109/MCC.2017.3791025

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Here we present Scalable Cost-Aware Cloud Infrastructure Management and Provisioning (SCRIMP)a service-based system that enables application developers and users to reliably outsource the task of provisioning cloud infrastructure. We show that by understanding application requirements, predicting dynamic market conditions, and automatically provisioning infrastructure according to user-defined policies and real-time conditions that our approaches can reduce costs by an order of magnitude when using commercial clouds while also improving execution performance and efficiency.

引用

页码：48 / 59

页数：12

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