Cost-based virtual machine scheduling resilient to network traffic anomalies for Data as a Service

被引：0

作者：

Alves de Oliveira, Ana Cristina ^{[1
]}

Spohn, Marco Aurelio ^{[2
]}

机构：

[1] Inst Fed Paraiba IFPB, Lab Tecnol Informacao LATIN, Campus Campina Grande, Campina Grande, Paraiba, Brazil

[2] Univ Fed Fronteira Sul UFFS, Chapeco, SC, Brazil

来源：

REVISTA BRASILEIRA DE COMPUTACAO APLICADA | 2020年 / 12卷 / 03期

关键词：

Cloud computing; data as a service; anomaly detection; cost based scheduling; network traffic analysis; ALLOCATION;

D O I：

10.5335/rbca.v12i3.11220

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Cloud computing services run on top of different abstraction levels, involving many actors playing alternate roles as customers or providers. Financial losses caused by faults are propagated throughout all the systems' levels. Mechanisms to reduce costs and losses are important in face of the large scale of cloud computing systems. In this paper, we propose a cost-based scheduling model integrated into the network traffic anomaly detection engine. The Data-as-a-Service (DaaS) business model was taken as a case study to evaluate the impact resulting from anomalous traffic. The results showed a significant reduction in costs and financial losses, with gains ranging from 15% to 26% of the conventional method's financial losses.

引用

页码：85 / 96

页数：12

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