Performance Evaluation of Centralized In-Network Caching and Content Visibility in Information Centric Network over SDN/OpenFlow

Performance is the main issue that we must consider as the key point in order to design Information Centric Network architecture (ICN). Cooperative in-network caching and ability of network’s nodes to see the contents in network will improve performance of ICN. In centralized network, the controller plays very important role in order to enable ICN nodes to do cooperative caching. Also, it has ability to see contents partially or globally according to our configuration. This paper aims to observe and compare the performance of ICN when we apply different levels of cooperative in-network caching and content visibility based on centralized control by using SDN/OpenFlow concept. We will evaluate performance of ICN by using three mechanisms; firstly, non-cooperative in-network caching with individual visibility; secondly, path cooperative in-network caching with path content visibility; and finally, global cooperative in-network caching with global content visibility. Our emulation result shows that global cooperative in-network caching with global content visibility mechanism gives better performance for ICN in terms of average number of hops to reach the content and number of requests hit server

Performance Evaluation of Centralized In-Network Caching and Content Visibility in Information Centric Network Over SDN/OpenFlow

Performance is the main issue that we must consider as the key point in order to design Information Centric Network architecture (ICN). Cooperative in-network caching and ability of network's nodes to see the contents in network will improve performance of ICN. In centralized network, the controller plays very important role in order to enable ICN nodes to do cooperative caching. Also, it has ability to see contents partially or globally according to our configuration. This paper aims to observe and compare the performance of ICN when we apply different levels of cooperative in-network caching and content visibility based on centralized control by using SDN/OpenFlow concept. We will evaluate performance of ICN by using three mechanisms; firstly, non-cooperative in-network caching with individual visibility; secondly, path cooperative in-network caching with path content visibility; and finally, global cooperative in-network caching with global content visibility. Our emulation result shows that global cooperative in-network caching with global content visibility mechanism gives better performance for ICN in terms of average number of hops to reach the content and number of requests hit server

Performance Improvement of Vehicular Ad Hoc Network Environment by Cooperation between SDN/OpenFlow Controller and IEEE 802.11p

Vehicular communication has recently become an active research issue in both academic and industry. Vehicular Network, by nature, could possess potential problems in connectivity, intelligence, scalability and flexibility. Networking technology nowadays is moving toward to Software-Defined Networking (SDN) concept where the network is mainly separated into two planes; control plane and data plane. OpenFlow is the most popular open interface for SDN southbound API. In this paper, we proposed the SDN application over Vehicular ad hoc Network (VANET) environment. We believe that the emerging SDN technology and IEEE 802.11p can be used to increase the efficiency and to bridge the gaps in VANET application. We hope to exploit the benefit of SDN by adopting POX/OpenFlow controller to process and perform message routing. A centralized controller is the key player to enable communication between vehicles and roadside unit (RSU). We evaluated the proposed work based on three simulation indicators, such as packet delivery ratio, throughput and packet delay time

Bottleneck Based Gridlock Prediction in an Urban Road Network Using Long Short-Term Memory

The traffic bottlenecks in urban road networks are more challenging to investigate and discover than in freeways or simple arterial networks. A bottleneck indicates the congestion evolution and queue formation, which consequently disturb travel delay and degrade the urban traffic environment and safety. For urban road networks, sensors are needed to cover a wide range of areas, especially for bottleneck and gridlock analysis, requiring high installation and maintenance costs. The emerging widespread availability of GPS vehicles significantly helps to overcome the geographic coverage and spacing limitations of traditional fixed-location detector data. Therefore, this study investigated GPS vehicles that have passed through the links in the simulated gridlock-looped intersection area. The sample size estimation is fundamental to any traffic engineering analysis. Therefore, this study tried a different number of sample sizes to analyze the severe congestion state of gridlock. Traffic condition prediction is one of the primary components of intelligent transportation systems. In this study, the Long Short-Term Memory (LSTM) neural network was applied to predict gridlock based on bottleneck states of intersections in the simulated urban road network. This study chose to work on the Chula-Sathorn SUMO Simulator (Chula-SSS) dataset. It was calibrated with the past actual traffic data collection by using the Simulation of Urban MObility (SUMO) software. The experiments show that LSTM provides satisfactory results for gridlock prediction with temporal dependencies. The reported prediction error is based on long-range time dependencies on the respective sample sizes using the calibrated Chula-SSS dataset. On the other hand, the low sampling rate of GPS trajectories gives high RMSE and MAE error, but with reduced computation time. Analyzing the percentage of simulated GPS data with different random seed numbers suggests the possibility of gridlock identification and reports satisfying prediction errors. Document type: Articl

Performance Analysis for Total Delay and Total Packet Loss Rate in 802.11 MAC Layer

In this paper, we introduce the analytical model of 802.11 MAC layer to assess system performance under the effect of external elements such as load and the number of stations. Concurrently, we analyze the effect of these elements in term of total delay time and packet loss rate in network with saturated condition, i.e. when every station always has packet to send. In the scope of this paper, we consider the case of unicast only and assume having no hidden terminal phenomenon.APSIPA ASC 2009: Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference. 4-7 October 2009. Sapporo, Japan. Oral session: Wireless Communications (7 October 2009)

Bottleneck Based Gridlock Prediction in an Urban Road Network Using Long Short-Term Memory

The traffic bottlenecks in urban road networks are more challenging to investigate and discover than in freeways or simple arterial networks. A bottleneck indicates the congestion evolution and queue formation, which consequently disturb travel delay and degrade the urban traffic environment and safety. For urban road networks, sensors are needed to cover a wide range of areas, especially for bottleneck and gridlock analysis, requiring high installation and maintenance costs. The emerging widespread availability of GPS vehicles significantly helps to overcome the geographic coverage and spacing limitations of traditional fixed-location detector data. Therefore, this study investigated GPS vehicles that have passed through the links in the simulated gridlock-looped intersection area. The sample size estimation is fundamental to any traffic engineering analysis. Therefore, this study tried a different number of sample sizes to analyze the severe congestion state of gridlock. Traffic condition prediction is one of the primary components of intelligent transportation systems. In this study, the Long Short-Term Memory (LSTM) neural network was applied to predict gridlock based on bottleneck states of intersections in the simulated urban road network. This study chose to work on the Chula-Sathorn SUMO Simulator (Chula-SSS) dataset. It was calibrated with the past actual traffic data collection by using the Simulation of Urban MObility (SUMO) software. The experiments show that LSTM provides satisfactory results for gridlock prediction with temporal dependencies. The reported prediction error is based on long-range time dependencies on the respective sample sizes using the calibrated Chula-SSS dataset. On the other hand, the low sampling rate of GPS trajectories gives high RMSE and MAE error, but with reduced computation time. Analyzing the percentage of simulated GPS data with different random seed numbers suggests the possibility of gridlock identification and reports satisfying prediction errors

FTIR Microspectroscopy for the Assessment of Mycoplasmas in HepG2 Cell Culture

To assess the presence and absence of mycoplasma contamination in cell culture, Fourier transform infrared (FTIR) microspectroscopy, coupled with multivariate analysis, was deployed to determine the biomolecular changes in hepatocellular carcinoma cells, HepG2, before and after mycoplasma contamination. The contaminated HepG2 cells were treated with antibiotic BM-Cyclin to decontaminate the mycoplasma, and polymerase chain reaction (PCR) was then performed to confirm the presence or the absence of mycoplasma contamination. The contaminated and decontaminated HepG2 cells were analyzed by FTIR microspectroscopy with principal component analysis (PCA) and peak integral area analysis. The results showed that the FTIR spectra of contaminated HepG2 cells demonstrated the alteration in the IR spectra corresponding to the lipid, protein, and nucleic acid regions. PCA analysis distinguished the spectral differences between the groups of mycoplasma-contaminated and -decontaminated cells. The PCA loading plots suggest that lipid and protein are the main contributed molecules for the difference between these two cell groups. Peak integral area analysis illustrated the increase of lipid and nucleic acid and the decrease of protein contents in the contaminated HepG2 cells. FTIR microspectroscopy is, therefore, proven to be a potential tool for assessing mycoplasma removal by monitoring biomolecular alterations in cell culture