Role-based intelligent application state computing for OpenFlow distributed controllers in software-defined networking
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Software-defined networking, in which a network is programmable and controlled with soft computing techniques, is widely used in various network testbeds. To satisfy the demands of large networks, control planes are constructed with distributed controllers, which are a routine aspect of OpenFlow research. Distributed controllers are scalable and fault tolerant; thus, they can replace centralized controllers in large-scale networks. In the foreseeable future, there will emerge more applications based on soft computing techniques. Because there is scant research on application management, a distributed controller with a role-based mechanism for properly managing applications and their states based on their properties has yet to be developed. Thus, in this study, we propose a systematic approach for classifying applications according to their roles to dynamically deploy applications and their states. Both applications and their states are managed based on their properties, including CPU, memory and network bandwidth. This intelligent mechanism, which computes the overhead of applications, provides a compromise between storage and bandwidth usage in OpenFlow distributed controllers. We propose a hierarchical system to differentiate applications and design a controller module for dynamically determining the status of an application.
KeywordsSoftware-defined networking OpenFlow Intelligent application management Distributed controller
This work is funded by the European Framework Program (FP7) under Grant No. FP7-PEOPLE-2011-IRSES, National Natural Science Foundation of China under Grant Nos. 61073009 and 61103197, National High Tech R&D Program 863 of China under Grant No. 2011AA010101, National Sci-Tech Support Plan of China under Grant No. 2014BAH02F03, National Sci-Tech Major Projects of China under Grant Nos. SinoProbe-09-01-03 and 2012ZX01039-004-04-3, Key Sci-Tech Program of Jilin Province of China under Grant Nos. 2011ZDGG007 and 20150204035GX and Fundamental Research Funds for Central Universities of China under Grant Nos. JCKY-QKJC46 and 2412015KJ005.
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Conflicts of interest
All authors declare that we have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors. Our work is original research achieved by all authors. This manuscript has not been submitted to more than one journal for simultaneous consideration. The manuscript has not been published previously (partly or in full). Our study is not split up into several parts to increase the quantity of submissions and submitted to various journals or to one journal over time. No data have been fabricated or manipulated (including images) to support our conclusions. Authors whose names appear on the submission have contributed sufficiently to the scientific work and therefore share collective responsibility and accountability for the results.
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