Weight-based strategy for an I/O-intensive application at a cloud data center
- Applications with different characteristics in the cloud may have different resources preferences. However, traditional resource allocation and scheduling strategies rarely take into account the characteristics of applications. Considering that an I/O-intensive application is a typical type of application and that frequent I/O accesses, especially small files randomly accessing the disk, may lead to an inefficient use of resources and reduce the quality of service (QoS) of applications, a weight allocation strategy is proposed based on the available resources that a physical server can provide as well as the characteristics of the applications. Using the weight obtained, a resource allocation and scheduling strategy is presented based on the specific application characteristics in the data center. Extensive experiments show that the strategy is correct and can guarantee a high concurrency of I/O per second (IOPS) in a cloud data center with high QoS. Additionally, the strategy can efficiently improve the utilization of the disk andApplications with different characteristics in the cloud may have different resources preferences. However, traditional resource allocation and scheduling strategies rarely take into account the characteristics of applications. Considering that an I/O-intensive application is a typical type of application and that frequent I/O accesses, especially small files randomly accessing the disk, may lead to an inefficient use of resources and reduce the quality of service (QoS) of applications, a weight allocation strategy is proposed based on the available resources that a physical server can provide as well as the characteristics of the applications. Using the weight obtained, a resource allocation and scheduling strategy is presented based on the specific application characteristics in the data center. Extensive experiments show that the strategy is correct and can guarantee a high concurrency of I/O per second (IOPS) in a cloud data center with high QoS. Additionally, the strategy can efficiently improve the utilization of the disk and resources of the data center without affecting the service quality of applications.…
Author details: | Junjie Peng, Danxu LiuORCiD, Yingtao Wang, Ying Zeng, Feng ChengGND, Wenqiang Zhang |
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DOI: | https://doi.org/10.1002/cpe.4648 |
ISSN: | 1532-0626 |
ISSN: | 1532-0634 |
Title of parent work (English): | Concurrency and computation : practice & experience |
Publisher: | Wiley |
Place of publishing: | Hoboken |
Publication type: | Article |
Language: | English |
Date of first publication: | 2018/05/25 |
Publication year: | 2018 |
Release date: | 2021/09/01 |
Tag: | IOPS; O; process scheduling; random I; small files; weight |
Volume: | 30 |
Issue: | 19 |
Number of pages: | 14 |
Funding institution: | National Natural Science Foundation of ChinaNational Natural Science Foundation of China [61103054, 61572305] |
Organizational units: | An-Institute / Hasso-Plattner-Institut für Digital Engineering gGmbH |
DDC classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
Peer review: | Referiert |