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Sova: A software-defined autonomic framework for virtual network allocations
Ye,Zhiyong1; Wang,Yang1; He,Shuibing2; Xu,Chengzhong3; Sun,Xian He4
Source PublicationIEEE Transactions on Parallel and Distributed Systems

With the rise of network virtualization, the workloads deployed on data center are dramatically changed to support diverse service-oriented applications, which are in general characterized by the time-bounded service response that in turn puts great burden on the data-center networks. Although there have been numerous techniques proposed to optimize the virtual network allocation in data center, the research on coordinating them in a flexible and effective way to autonomically adapt to the workloads for service time reduction is few and far between. To address these issues, in this article we propose Sova, an autonomic framework that can combine the virtual dynamic SR-IOV (DSR-IOV) and the virtual machine live migration (VLM) for virtual network allocations in data centers. DSR-IOV is a SR-IOV-based virtual network allocation technology, but its operation scope is very limited to a single physical machine, which could lead to the local hotspot issue in the course of computation and communication, likely increasing the service response time. In contrast, VLM is an often-used virtualization technique to optimize global network traffic via VM migration. Sova exploits the software-defined approach to combine these two technologies with reducing the service response time as a goal. To realize the autonomic coordination, the architecture of Sova is designed based on the MAPE-K loop in autonomic computing. With this design, Sova can adaptively optimize the network allocation between different services by coordinating DSR-IOV and VLM in autonomic way, depending on the resource usages of physical servers and the network characteristics of VMs. To this end, Sova needs to monitor the network traffic as well as the workload characteristics in the cluster, whereby the network properties are derived on the fly to direct the coordination between these two technologies. Our experiments show that Sova can exploit the advantages of both techniques to match and even beat the better performance of each individual technology by adapting to the VM workload changes.

KeywordAutonomic Computing Dynamic Sr-iov Mape-k Loop Network Allocation Software-defined Approach Virtual Machine Migration
URLView the original
Indexed BySCIE
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000556765200004
Scopus ID2-s2.0-85090136631
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
Corresponding AuthorWang,Yang
Affiliation1.Chinese Academy of Sciences,Shenzhen Institutes of Advanced Technology,Shenzhen,518055,China
2.College of Computer Science and Technology,Zhejiang University,Hangzhou, Zhejiang,310027,China
3.State Key Laboratory of IoT for Smart City,Faculty of Science and Technology,University of Macau,999078,Macao
4.Department of Computer Science,Illinois Institute of Technology,Chicago,60616,United States
Recommended Citation
GB/T 7714
Ye,Zhiyong,Wang,Yang,He,Shuibing,et al. Sova: A software-defined autonomic framework for virtual network allocations[J]. IEEE Transactions on Parallel and Distributed Systems,2020,32(1):116-130.
APA Ye,Zhiyong,Wang,Yang,He,Shuibing,Xu,Chengzhong,&Sun,Xian He.(2020).Sova: A software-defined autonomic framework for virtual network allocations.IEEE Transactions on Parallel and Distributed Systems,32(1),116-130.
MLA Ye,Zhiyong,et al."Sova: A software-defined autonomic framework for virtual network allocations".IEEE Transactions on Parallel and Distributed Systems 32.1(2020):116-130.
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