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PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers

Title
PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers
Type
Article in International Scientific Journal
Year
2015
Authors
Altino M. Sampaio
(Author)
Other
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Jorge G. Barbosa
(Author)
FEUP
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Radu Prodan
(Author)
Other
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Journal
Vol. 57 No. 9
Pages: 142-160
ISSN: 1569-190X
Publisher: Elsevier
Indexing
Publicação em ISI Web of Knowledge ISI Web of Knowledge
Publicação em ISI Web of Science ISI Web of Science
Publicação em Scopus Scopus
Scientific classification
FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering
CORDIS: Physical sciences > Computer science > Computer systems
Other information
Resumo (PT):
Abstract (EN): Cloud data centers have been progressively adopted in different scenarios, as reflected in the execution of heterogeneous applications with diverse workloads and diverse quality of service (QoS) requirements. Virtual machine (VM) technology eases resource manage- ment in physical servers and helps cloud providers achieve goals such as optimization of energy consumption. However, the performance of an application running inside a VM is not guaranteed due to the interference among co-hosted workloads sharing the same physical resources. Moreover, the different types of co-hosted applications with diverse QoS requirements as well as the dynamic behavior of the cloud makes efficient provision- ing of resources even more difficult and a challenging problem in cloud data centers. In this paper, we address the problem of resource allocation within a data center that runs differ- ent types of application workloads, particularly CPU- and network-intensive applications. To address these challenges, we propose an interference- and power-aware management mechanism that combines a performance deviation estimator and a scheduling algorithm to guide the resource allocation in virtualized environments. We conduct simulations by injecting synthetic workloads whose characteristics follow the last version of the Google Cloud tracelogs. The results indicate that our performance-enforcing strategy is able to fulfill contracted SLAs of real-world environments while reducing energy costs by as much as 21%.
Language: English
Type (Professor's evaluation): Scientific
No. of pages: 19
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