Virtual cluster, as a fundamental service of cloud computing, is an important delivery model of cloud services. Allocating physical resources for a virtual cluster is known as virtual cluster embedding (VCE), which has a significant impact on the performance. VCE includes the components of virtual machine, virtual switch, virtual link, virtual software-defined network (SDN) controller and cluster system controller. VCE needs to be considered comprehensively to adapt the influencing factor weight proportion and to be flexible for different types of embedding. This paper, based on the topology of the cloud data center network, develops a coordinated VCE approach, called CoVCE. The approach combines virtual network with SDN to form virtual SDN and weakens slightly the constraints of VM placement. The network centrality, correlation property and resource fragmentation are optimized with multiple objectives to receive more requests, increase the throughput and decrease network delay and runtime. The CoVCE method integrates not only the logical topology of virtual machines in the cluster but also the relationship with virtual switch, virtual link and control services. This does not only optimize the placement location of virtual components, but also allows the virtual resources to migrate upon virtual cluster requests. The method further improves the utilization of physical resources and reduces resource fragmentation. According to extensive simulation and emulation experiments and comparison with correlative algorithms, CoVCE effectively reduces network delays, offers a higher embedding efficiency, improves user experiences and, to some extent, also improves the revenue/cost ratio and throughput.
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This work is supported by National Core Electronic Devices, High-end Generic Chips and Basic Software Project under Grant No. 2016ZX01040101; Mobile Terminal Operating System under Grant No. 2016ZX01040101; the National Natural Science Foundation of China under Grant No. 61602492, 61502511, 61772070; Beijing Municipal Education Commission under Grant No.KM201510016009, Beijing Higher Education Young Elite Teacher Project (Grant No. YETP1169), Excellent Teachers Development Foundation of BUCEA (No.21082717046), and National Key R&D Program of China (No.2016YFC060090).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
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