TY - JOUR
T1 - WARM
T2 - Workload-Aware Multi-Application Task Scheduling for Revenue Maximization in SDN-Based Cloud Data Center
AU - Yuan, Haitao
AU - Bi, Jing
AU - Zhou, Mengchu
AU - Sedraoui, Khaled
N1 - Funding Information:
This work was supported in part by the Special Financial Grant from the China Post-Doctoral Science Foundation under Grant 2017T100034, in part by the China Post-Doctoral Science Foundation under Grant 2016M600912, in part by the National Natural Science Foundation of China under Grant 61703011, in part by the National Science and Technology Major Project under Grant 2018ZX07111005, in part by the High Dynamic Navigation Technology Beijing Key Laboratory under Grant HDN2017101, in part by the Beijing Natural Science Foundation under Grant 4164090 and in part by the Deanship of Scientific Research at King Abdulaziz University, Jeddah, under Grant G-415-135-38. The work of H. Yuan was supported by the China Scholarship Council.
Publisher Copyright:
© 2013 IEEE.
PY - 2018
Y1 - 2018
N2 - Nowadays an increasing number of companies and organizations choose to deploy their applications in data centers to leverage resource sharing. The increase in tasks of multiple applications, however, makes it challenging for a provider to maximize its revenue by intelligently scheduling tasks in its software-defined networking (SDN)-enabled data centers. Existing SDN controllers only reduce network latency while ignoring virtual machine (VM) latency, which may lead to revenue loss. In the context of SDN-enabled data centers, this paper presents a workload-Aware revenue maximization (WARM) approach to maximize the revenue from a data center provider's perspective. Its core idea is to jointly consider the optimal combination of VMs and routing paths for tasks of each application. This work compares it with state-of-The-Art methods, experimentally. The results show that WARM yields the best schedules that not only increase the revenue but also reduce the round-Trip time of tasks for all applications.
AB - Nowadays an increasing number of companies and organizations choose to deploy their applications in data centers to leverage resource sharing. The increase in tasks of multiple applications, however, makes it challenging for a provider to maximize its revenue by intelligently scheduling tasks in its software-defined networking (SDN)-enabled data centers. Existing SDN controllers only reduce network latency while ignoring virtual machine (VM) latency, which may lead to revenue loss. In the context of SDN-enabled data centers, this paper presents a workload-Aware revenue maximization (WARM) approach to maximize the revenue from a data center provider's perspective. Its core idea is to jointly consider the optimal combination of VMs and routing paths for tasks of each application. This work compares it with state-of-The-Art methods, experimentally. The results show that WARM yields the best schedules that not only increase the revenue but also reduce the round-Trip time of tasks for all applications.
KW - Performance modeling and analysis
KW - delay assurance chaotic search
KW - metaheuristic optimization
KW - particle swarm optimization
KW - revenue maximization
KW - simulated annealing
KW - task scheduling
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U2 - 10.1109/ACCESS.2017.2773645
DO - 10.1109/ACCESS.2017.2773645
M3 - Article
AN - SCOPUS:85042214807
SN - 2169-3536
VL - 6
SP - 645
EP - 657
JO - IEEE Access
JF - IEEE Access
ER -