8,737 research outputs found
NeuRoute: Predictive Dynamic Routing for Software-Defined Networks
This paper introduces NeuRoute, a dynamic routing framework for Software
Defined Networks (SDN) entirely based on machine learning, specifically, Neural
Networks. Current SDN/OpenFlow controllers use a default routing based on
Dijkstra algorithm for shortest paths, and provide APIs to develop custom
routing applications. NeuRoute is a controller-agnostic dynamic routing
framework that (i) predicts traffic matrix in real time, (ii) uses a neural
network to learn traffic characteristics and (iii) generates forwarding rules
accordingly to optimize the network throughput. NeuRoute achieves the same
results as the most efficient dynamic routing heuristic but in much less
execution time.Comment: Accepted for CNSM 201
Polycyclic aromatic hydrocarbons biodegradation using isolated strains under indigenous condition
The treatment and disposal of domestic sIudge is an expensive and environmentally sensitive
problem. It is also a growing problem since sludge production will continue to increase as
new wastewzter treatment plants are built due to population increase. The large volume of
domestic sIudge produced had made it difficult for many countries including Malaysia to
assure complete treatment of the sludge before discharging to the receiving environment.
Domestic sludge contains diverse range of pollutants such as pathogen, inorganic and organic
compounds. These pollutants are toxic, mutagenic or carcinogenic and may threaten human
health. Iiilproper disposal and handling of sludge may pose serious impact to the environment
especially on soil and water cycles. Previous studies on Malaysian domestic sludge only
reported on bulk parameters and heavy metals. Thus, no study reported on organic micro
pollutants, namely, polycylic aromatic hydrocarbons (PAHs). Their recalcitrance and
persistence make them problematic environmental contaminants. Microbial degradation is
considered to be the primary mechanism of PAHs removal from the environment. Much has
been reported on biodegradation of PAHs in several countries but there is a lack of
information quantitative on this subject in Malaysia. This study is carried out to understand
the nature of domestic sludge and to provide a better understanding on the biodegradation
processes of PAHs. The methodology of this study comprised field activities, laboratory work
and mathematical modelling. Field activities involved sampling of domestic sludge from
Kolej Mawar, Universiti Teknologi MARA, Shah Alam, Selangor. Laboratory activities
include seven phases of experimental works. First phase is characterization study of domestic
sludge based on bulk parameters, heavy metals and PAHs. Second phase is enrichment and
purification of bacteria isolated from domestic sludge using single PAHs and mixed PAHs as
growth substrate. This was followed by identification of bacteria using BIOLOG system. The
fourth phase focussed on turbidity test to monitor growth rate of the isolated bacteria.
Preliminary degradation study involves optimization of the process at different substrate
concentration, bacteria concentration, pH and temperature. The optimum conditions
established from optimization study were used in degradation study. In biodegradation study,
two experimental conditions were performed. These conditions include using bacteria isolated
from single PAHs as substrate and bacteria isolated from mixed PAHs. Protein and pH tests
were done during degradation study. Final activity is mathematical modelling of the
biodegradation process. In general results on bulk parameters are comparable to previous
studies. Zinc was the main compound with a mean concentration of 11 96.4 mglkg. PAHs
were also detected in all of the samples, with total concentration between 0.72 to 5.36 mglkg
dry weight for six PAHs. In the examined samples, phenanthrene was the main compound
with a mean concentration of 1.0567 mglkg. The results fiom purification studies of bacteria
strains sucessfull isolated 13 bacteria strains fiom single PAH substrate while three bacteria
were isolated from the mixed PAHs substrate. Based on bacteria growth rates, only six strains
grown on single PAHs and three strains grown on mixed PAHs were used for further studies.
Results from the optimization study of biodegradation indicated that maximum rate of PAHs
removal occurred at 100 mg~-' of PAHs, 10% bacteria concentration, pH 7.0 and 30°C. The
results showed that bacteria grown on lower ring of PAHs are not able to grow on higher ring
of PAHs. As for example Micrococcus diversus grown on napthalene as sole carbon source
was unable to degrade other PAHs like acenapthylene, acenapthene, fluorene, phenanthrene
and antlracene. In the case of bacteria isolated from mixed PAHs, the results showed that
most of the napthalene was degraded by isolated strains with the highest average degradation
rate followed by acenapthylene, acenapthene, fluorene, phenanthrene and anthracene. 377.1�781.8�781�+
D4ff + c\,cpda~ition trends were observed in the study could be attributed to the different
subsr , i,lo\~ir 'Led during isolation process. Interaction through cometabolism and synergistic
ocolq bacteria strains isolated from single substrate. Thus, only synergistic interaction
was oL, :a 77ed for bacteria isolated from mixed substrate. Corynebacterium urolyticum
re\e;;ed I,, be the best strain in degrading PAHs. The experimental results have led to a model
conccl~t desclibing I'AHs degradation
Multitask Learning for Network Traffic Classification
Traffic classification has various applications in today's Internet, from
resource allocation, billing and QoS purposes in ISPs to firewall and malware
detection in clients. Classical machine learning algorithms and deep learning
models have been widely used to solve the traffic classification task. However,
training such models requires a large amount of labeled data. Labeling data is
often the most difficult and time-consuming process in building a classifier.
To solve this challenge, we reformulate the traffic classification into a
multi-task learning framework where bandwidth requirement and duration of a
flow are predicted along with the traffic class. The motivation of this
approach is twofold: First, bandwidth requirement and duration are useful in
many applications, including routing, resource allocation, and QoS
provisioning. Second, these two values can be obtained from each flow easily
without the need for human labeling or capturing flows in a controlled and
isolated environment. We show that with a large amount of easily obtainable
data samples for bandwidth and duration prediction tasks, and only a few data
samples for the traffic classification task, one can achieve high accuracy. We
conduct two experiment with ISCX and QUIC public datasets and show the efficacy
of our approach
Trust model for certificate revocation in Ad hoc networks
In this paper we propose a distributed trust model for certificate revocation in Adhoc networks. The proposed model allows trust to be built over time as the number of interactions between nodes increase. Furthermore, trust in a node is defined not only in terms of its potential for maliciousness, but also in terms of the quality of the service it provides. Trust in nodes where there is little or no history of interactions is determined by recommendations from other nodes. If the nodes in the network are selfish, trust is obtained by an exchange of portfolios. Bayesian networks form the underlying basis for this model
Adaptive link-weight routing protocol using cross-layer communication for MANET
Routing efficiency is one of the challenges offered by Mobile Ad-hoc Networks (MANETs). This
paper proposes a novel routing technique called Adaptive Link-Weight (ALW) routing protocol. ALW
adaptively selects an optimum route on the basis of available bandwidth, low delay and long route lifetime. The technique adapts a cross-layer framework where the ALW is integrated with application and physical layer. The proposed design allows applications to convey preferences to the ALW protocol to override the default path
selection mechanism. The results confirm improvement over AODV in terms of network load, route discovery time and link reliability
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