TY - GEN
T1 - Revenue-sensitive scheduling of multi-application tasks in software-defined cloud
AU - Yuan, Haitao
AU - Bi, Jing
AU - Zhou, Mengchu
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - The development of cloud computing attracts a growing number of corporations to implement their applications in data centers. The increase in variety and amount of applications in data centers that support software-defined networking (SDN) protocols makes it a big challenge to maximize revenue for data center providers. However, current SDN controllers just consider latency optimization in network and do not consider latency in virtual machines (VMs), and therefore revenue loss may occur. Different from current studies, this work aims to maximize revenue of a software-defined cloud provider. A Revenue-sensitive Scheduling of Multi-application Tasks (RSMT) method is then proposed to increase the revenue of a cloud provider. It is realized by jointly determining optimal routing paths and VMs for multi-application tasks. Simulation based on real-life task data demonstrates that compared with several current algorithms, RSMT can produce the efficient schedules that increase the cloud provider's revenue and decrease round trip time of multi-application tasks.
AB - The development of cloud computing attracts a growing number of corporations to implement their applications in data centers. The increase in variety and amount of applications in data centers that support software-defined networking (SDN) protocols makes it a big challenge to maximize revenue for data center providers. However, current SDN controllers just consider latency optimization in network and do not consider latency in virtual machines (VMs), and therefore revenue loss may occur. Different from current studies, this work aims to maximize revenue of a software-defined cloud provider. A Revenue-sensitive Scheduling of Multi-application Tasks (RSMT) method is then proposed to increase the revenue of a cloud provider. It is realized by jointly determining optimal routing paths and VMs for multi-application tasks. Simulation based on real-life task data demonstrates that compared with several current algorithms, RSMT can produce the efficient schedules that increase the cloud provider's revenue and decrease round trip time of multi-application tasks.
UR - http://www.scopus.com/inward/record.url?scp=85044954968&partnerID=8YFLogxK
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U2 - 10.1109/COASE.2017.8256326
DO - 10.1109/COASE.2017.8256326
M3 - Conference contribution
AN - SCOPUS:85044954968
T3 - IEEE International Conference on Automation Science and Engineering
SP - 1566
EP - 1571
BT - 2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017
PB - IEEE Computer Society
T2 - 13th IEEE Conference on Automation Science and Engineering, CASE 2017
Y2 - 20 August 2017 through 23 August 2017
ER -