Evaluations of Network Performance Enhancement on Cloud-native Network Function

2020 ◽  
Author(s):  
Yong-Xuan Huang ◽  
Jerry Chou
Keyword(s):  
Network Performance ◽  
Performance Enhancement ◽  
Network Function

scholarly journals Cross Layer Optimization and Simulation of Smart Grid Home Area Network

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Lipi K. Chhaya ◽  
Paawan Sharma ◽  
Adesh Kumar ◽  
Govind Bhagwatikar
Keyword(s):  
Smart Grid ◽  
Network Performance ◽  
Performance Enhancement ◽  
Hierarchical Architecture ◽  
Buffer Size ◽  
Cross Layer ◽  
Area Network ◽  
Frame Aggregation ◽  
Cross Layer Optimization ◽  
Layered Approach

An electrical “Grid” is a network that carries electricity from power plants to customer premises. Smart Grid is an assimilation of electrical and communication infrastructure. Smart Grid is characterized by bidirectional flow of electricity and information. Smart Grid is a complex network with hierarchical architecture. Realization of complete Smart Grid architecture necessitates diverse set of communication standards and protocols. Communication network protocols are engineered and established on the basis of layered approach. Each layer is designed to produce an explicit functionality in association with other layers. Layered approach can be modified with cross layer approach for performance enhancement. Complex and heterogeneous architecture of Smart Grid demands a deviation from primitive approach and reworking of an innovative approach. This paper describes a joint or cross layer optimization of Smart Grid home/building area network based on IEEE 802.11 standard using RIVERBED OPNET network design and simulation tool. The network performance can be improved by selecting various parameters pertaining to different layers. Simulation results are obtained for various parameters such as WLAN throughput, delay, media access delay, and retransmission attempts. The graphical results show that various parameters have divergent effects on network performance. For example, frame aggregation decreases overall delay but the network throughput is also reduced. To prevail over this effect, frame aggregation is used in combination with RTS and fragmentation mechanisms. The results show that this combination notably improves network performance. Higher value of buffer size considerably increases throughput but the delay is also greater and thus the choice of optimum value of buffer size is inevitable for network performance optimization. Parameter optimization significantly enhances the performance of a designed network. This paper is expected to serve as a comprehensive analysis and performance enhancement of communication standard suitable for Smart Grid HAN applications.

Network Performance Enhancement of Multi-sink Enabled Low Power Lossy Networks in SDN Based Internet of Things

2018 ◽  
Vol 48 (2) ◽  
pp. 367-398 ◽  
Author(s):  
Ghulam Shabbir ◽  
Adeel Akram ◽  
Muhammad Munwar Iqbal ◽  
Sohail Jabbar ◽  
Mai Alfawair ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Low Power ◽  
Network Performance ◽  
Performance Enhancement ◽  
Lossy Networks

scholarly journals Virtual Networking Performance in OpenStack Platform for Network Function Virtualization

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Franco Callegati ◽  
Walter Cerroni ◽  
Chiara Contoli
Keyword(s):  
Cloud Computing ◽  
Network Performance ◽  
Network Virtualization ◽  
Network Function Virtualization ◽  
Network Architectures ◽  
Cloud Data ◽  
Network Function ◽  
Computing Platform ◽  
Central Offices ◽  
The One

The emerging Network Function Virtualization (NFV) paradigm, coupled with the highly flexible and programmatic control of network devices offered by Software Defined Networking solutions, enables unprecedented levels of network virtualization that will definitely change the shape of future network architectures, where legacy telco central offices will be replaced by cloud data centers located at the edge. On the one hand, this software-centric evolution of telecommunications will allow network operators to take advantage of the increased flexibility and reduced deployment costs typical of cloud computing. On the other hand, it will pose a number of challenges in terms of virtual network performance and customer isolation. This paper intends to provide some insights on how an open-source cloud computing platform such as OpenStack implements multitenant network virtualization and how it can be used to deploy NFV, focusing in particular on packet forwarding performance issues. To this purpose, a set of experiments is presented that refer to a number of scenarios inspired by the cloud computing and NFV paradigms, considering both single tenant and multitenant scenarios. From the results of the evaluation it is possible to highlight potentials and limitations of running NFV on OpenStack.

Quality Evaluation of Cloud and Fog Computing Services in 5G Networks

2019 ◽  
pp. 1-36 ◽  
Author(s):  
Stojan Kitanov ◽  
Borislav Popovski ◽  
Toni Janevski
Keyword(s):  
Network Performance ◽  
Fog Computing ◽  
High Mobility ◽  
Network Control ◽  
5G Networks ◽  
New Paradigm ◽  
Network Function ◽  
Computing Services ◽  
5G Network ◽  
Intelligent Networking

Because of the increased computing and intelligent networking demands in 5G network, cloud computing alone encounters too many limitations, such as requirements for reduced latency, high mobility, high scalability, and real-time execution. A new paradigm called fog computing has emerged to resolve these issues. Fog computing distributes computing, data processing, and networking services to the edge of the network, closer to end users. Fog applied in 5G significantly improves network performance in terms of spectral and energy efficiency, enable direct device-to-device wireless communications, and support the growing trend of network function virtualization and separation of network control intelligence from radio network hardware. This chapter evaluates the quality of cloud and fog computing services in 5G network, and proposes five algorithms for an optimal selection of 5G RAN according to the service requirements. The results demonstrate that fog computing is a suitable technology solution for 5G networks.

scholarly journals Overshooting Subcellular Redox-Responses in Rett-Mouse Hippocampus during Neurotransmitter Stimulation

Cells ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2539
Author(s):  
Karina Festerling ◽  
Karolina Can ◽  
Sebastian Kügler ◽  
Michael Müller
Keyword(s):  
Neuronal Network ◽  
Hippocampal Neurons ◽  
Network Performance ◽  
Neurodevelopmental Disorder ◽  
Redox Homeostasis ◽  
Redox Balance ◽  
Mitochondrial Matrix ◽  
Network Function ◽  
Leading Role ◽  
Redox Sensor

Rett syndrome (RTT) is a neurodevelopmental disorder associated with disturbed neuronal responsiveness and impaired neuronal network function. Furthermore, mitochondrial alterations and a weakened cellular redox-homeostasis are considered part of the complex pathogenesis. So far, overshooting redox-responses of MeCP2-deficient neurons were observed during oxidant-mediated stress, hypoxia and mitochondrial inhibition. To further clarify the relevance of the fragile redox-balance for the neuronal (dys)function in RTT, we addressed more physiological stimuli and quantified the subcellular redox responses to neurotransmitter-stimulation. The roGFP redox sensor was expressed in either the cytosol or the mitochondrial matrix of cultured mouse hippocampal neurons, and the responses to transient stimulation by glutamate, serotonin, dopamine and norepinephrine were characterized. Each neurotransmitter evoked more intense oxidizing responses in the cytosol of MeCP2-deficient than in wildtype neurons. In the mitochondrial matrix the neurotransmitter-evoked oxidizing changes were more moderate and more uniform among genotypes. This identifies the cytosol as an important reactive oxygen species (ROS) source and as less stably redox buffered. Fura-2 imaging and extracellular Ca2+ withdrawal confirmed cytosolic Ca2+ transients as a contributing factor of neurotransmitter-induced redox responses and their potentiation in the cytosol of MeCP2-deficient neurons. Chemical uncoupling demonstrated the involvement of mitochondria. Nevertheless, cytosolic NADPH- and xanthine oxidases interact to play the leading role in the neurotransmitter-mediated oxidizing responses. As exaggerated redox-responses were already evident in neonatal MeCP2-deficient neurons, they may contribute remarkably to the altered neuronal network performance and the disturbed neuronal signaling, which are among the hallmarks of RTT.

scholarly journals Wiring variations that enable and constrain neural computation in a sensory microcircuit

2017 ◽  
Author(s):  
William F. Tobin ◽  
Rachel I. Wilson ◽  
Wei-Chung Allen Lee
Keyword(s):  
Large Scale ◽  
Network Performance ◽  
Olfactory Receptor Neuron ◽  
Neural Computation ◽  
Structural Basis ◽  
Projection Neurons ◽  
Synaptic Connections ◽  
Network Function ◽  
Section Electron ◽  
Serial Section Electron Microscopy

ABSTRACTNeural network function can be shaped by varying the strength of synaptic connections. One way to achieve this is to vary connection structure. To investigate how structural variation among synaptic connections might affect neural computation, we examined primary afferent connections in the Drosophila olfactory system. We used large-scale serial section electron microscopy to reconstruct all the olfactory receptor neuron (ORN) axons that target a left-right pair of glomeruli, as well as all the projection neurons (PNs) postsynaptic to these ORNs. We found three variations in ORN→PN connectivity. First, we found a systematic co-variation in synapse number and PN dendrite size, suggesting total synaptic conductance is tuned to postsynaptic excitability. Second, we discovered that PNs receive more synapses from ipsilateral than contralateral ORNs, providing a structural basis for odor lateralization behavior. Finally, we found evidence of imprecision in ORN→PN connections and show how this can diminish network performance.

scholarly journals Wiring variations that enable and constrain neural computation in a sensory microcircuit

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
William F Tobin ◽  
Rachel I Wilson ◽  
Wei-Chung Allen Lee
Keyword(s):  
Large Scale ◽  
Network Performance ◽  
Olfactory Receptor Neuron ◽  
Neural Computation ◽  
Structural Basis ◽  
Projection Neurons ◽  
Synaptic Connections ◽  
Network Function ◽  
Section Electron ◽  
Serial Section Electron Microscopy

Neural network function can be shaped by varying the strength of synaptic connections. One way to achieve this is to vary connection structure. To investigate how structural variation among synaptic connections might affect neural computation, we examined primary afferent connections in the Drosophila olfactory system. We used large-scale serial section electron microscopy to reconstruct all the olfactory receptor neuron (ORN) axons that target a left-right pair of glomeruli, as well as all the projection neurons (PNs) postsynaptic to these ORNs. We found three variations in ORN→PN connectivity. First, we found a systematic co-variation in synapse number and PN dendrite size, suggesting total synaptic conductance is tuned to postsynaptic excitability. Second, we discovered that PNs receive more synapses from ipsilateral than contralateral ORNs, providing a structural basis for odor lateralization behavior. Finally, we found evidence of imprecision in ORN→PN connections that can diminish network performance.

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