Journal
IEEE ACCESS
Volume 6, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2017.2773645
Keywords
Performance modeling and analysis; metaheuristic optimization; revenue maximization; task scheduling; delay assurance chaotic search; particle swarm optimization; simulated annealing
Categories
Funding
- China Post-Doctoral Science Foundation [2017T100034, 2016M600912]
- National Natural Science Foundation of China [61703011]
- National Science and Technology Major Project [2018ZX07111005]
- High Dynamic Navigation Technology Beijing Key Laboratory [HDN2017101]
- Beijing Natural Science Foundation [4164090]
- Deanship of Scientific Research at King Abdulaziz University, Jeddah [G-415-135-38]
- China Scholarship Council
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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.
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