DocumentCode :
2181533
Title :
Application-Aware Resource Allocation for SDN-based Cloud Datacenters
Author :
Weifan Hong ; Kuochen Wang ; Yi-Huai Hsu
Author_Institution :
Dept. of Comput. Sci., Nat. Chiao Tung Univ., Hsinchu, Taiwan
fYear :
2013
fDate :
16-19 Dec. 2013
Firstpage :
106
Lastpage :
110
Abstract :
In cloud datacenters, since resource requirements change frequently, how to assign and manage resources efficiently while meeting service level agreements (SLAs) of different types of applications is an important research issue. In this paper, we propose an Application-aware Resource Allocation (App-RA) scheme to predict resource requirements and allocate an appropriate number of virtual machines (VMs) for each application in SDN-based cloud datacenters. To the best of our knowledge, the proposed App-RA is the first application-aware resource allocation scheme that adapts to all types of applications. The App-RA can meet SLAs, allocate resources efficiently, and reduce power consumption for each application in cloud datacenters. The proposed App-RA adopts the neural network based predictor to forecast the requirements of resources (CPU, Memory, GPU, Disk I/O and bandwidth) for an application. In the proposed App-RA, we have designed two algorithms which allocate appropriate numbers of virtual machines and use the VM allocation threshold to avoid SLA violations for five different types of applications. In addition, we adopt an SDN-based OpenFlow network with CICQ switches to appropriately schedule packets for different types of application in the network layer. Finally, simulation results show that the power consumption of the proposed App-RA is only 9.21% higher than that of the best case (oracle) and the power consumption of EAACVA, which is a representative resource allocation method for non-graphic applications, is 104.58% worse than that of App-RA. Furthermore, the SLA violation rate of the proposed App-RA is less than 4% for all applications.
Keywords :
cloud computing; contracts; resource allocation; virtual machines; App-RA scheme; CICQ switches; SDN-based OpenFlow network; SDN-based cloud data centers; SLA; VM; application-aware resource allocation scheme; nongraphic applications; power consumption; resource management; service level agreements; virtual machines; Bandwidth; Neural networks; Power demand; Resource management; Schedules; Scheduling; Time factors; Service level agreement; application-aware; cloud datacenter; resource allocation; software define network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cloud Computing and Big Data (CloudCom-Asia), 2013 International Conference on
Conference_Location :
Fuzhou
Print_ISBN :
978-1-4799-2829-3
Type :
conf
DOI :
10.1109/CLOUDCOM-ASIA.2013.44
Filename :
6820980
Link To Document :
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