Resource Management in Multi-Cloud Scenarios via Reinforcement Learning

被引：0

作者：

Pietrabissa, Antonio ^{[1
]}

Battilotti, Stefano ^{[1
]}

Facchinei, Francisco ^{[1
]}

Giuseppi, Alessandro ^{[1
]}

Oddi, Guido ^{[1
]}

Panfili, Martina ^{[1
]}

Suraci, Vincenzo ^{[1
]}

机构：

[1] Univ Roma La Sapienza, Dept Comp Control & Management Engn Antonio Ruber, Rome, Italy

来源：

2015 34TH CHINESE CONTROL CONFERENCE (CCC) | 2015年

关键词：

Cloud networks; Resource Management; Reinforcement Learning; Markov Decision Process;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The concept of Virtualization of Network Resources, such as cloud storage and computing power, has become crucial to any business that needs dynamic IT resources. With virtualization, we refer to the migration of various tasks, usually performed by hardware infrastructures, to virtual IT resources. This approach allows resources to be rapidly deployed, scaled and dynamically reassigned. In the last few years, the demand of cloud resources has grown dramatically, and a new figure plays a key role: the Cloud Management Broker (CMB). The CMB purpose is to manage cloud resources to meet the user's requirements and, at the same time, to optimize their usage. This paper proposes two multi-cloud resource allocation algorithms that manage the resource requests with the aim of maximizing the CMB revenue over time. The algorithms, based on Reinforcement Learning techniques, are evaluated and compared by numerical simulations.

引用

页码：9084 / 9089

页数：6

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