On the use of the genetic programming for balanced load distribution in software-defined networks
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @Article{JAMALI:2019:DCN,
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author = "Shahram Jamali and Amin Badirzadeh and
Mina Soltani Siapoush",
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title = "On the use of the genetic programming for balanced
load distribution in software-defined networks",
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journal = "Digital Communications and Networks",
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volume = "5",
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number = "4",
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pages = "288--296",
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year = "2019",
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ISSN = "2352-8648",
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DOI = "doi:10.1016/j.dcan.2019.10.002",
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URL = "http://www.sciencedirect.com/science/article/pii/S235286481830261X",
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keywords = "genetic algorithms, genetic programming,
Software-defined networking, OpenFlow, Mininet,
OpenDaylight, Load balancing",
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abstract = "As a new networking paradigm, Software-Defined
Networking (SDN)enables us to cope with the limitations
of traditional networks. SDN uses a controller that has
a global view of the network and switch devices which
act as packet forwarding hardware, known as {"}OpenFlow
switches{"}. Since load balancing service is essential
to distribute workload across servers in data centers,
we propose an effective load balancing scheme in SDN,
using a genetic programming approach, called Genetic
Programming based Load Balancing (GPLB). We formulate
the problem to find a path: 1) with the best bottleneck
switch which has the lowest capacity within bottleneck
switches of each path, 2) with the shortest path, and
3) requiring the less possible operations. For the
purpose of choosing the real-time least loaded path,
GPLB immediately calculates the integrated load of
paths based on the information that receives from the
SDN controller. Hence, in this design, the controller
sends the load information of each path to the load
balancing algorithm periodically and then the load
balancing algorithm returns a least loaded path to the
controller. In this paper, we use the Mininet emulator
and the OpenDaylight controller to evaluate the
effectiveness of the GPLB. The simulative study of the
GPLB shows that there is a big improvement in
performance metrics and the latency and the jitter are
minimized. The GPLB also has the maximum throughput in
comparison with related works and has performed better
in the heavy traffic situation. The results show that
our model stands smartly while not increasing further
overhead",
- }
Genetic Programming entries for
Shahram Jamali
Amin Badirzadeh
Mina Soltani Siapoush
Citations