This summarizes a research paper on developing an intelligent system that leverages big data telemetry analysis to enable trend-based networking decisions. The system collects data from traditional and SDN networks using SNMP and OpenFlow. Logstash filters the data and sends it to PNDA's Kafka interface. A Jupyter notebook streams the data to OpenTSDB. Benchmarking identifies trends, and the system takes automated action like load balancing across the networks when trends are detected. A GUI provides centralized management. The system demonstrates using big data analytics to monitor networks and make proactive, automated decisions based on observed trends.
Enhancing Traffic Routing Inside a Network through IoT Technology & Network C...IJCNCJournal
IoT networking uses real items as stationary or mobile nodes. Mobile nodes complicate networking. Internet of Things (IoT) networks have a lot of control overhead messages because devices are mobile. These signals are generated by the constant flow of control data as such device identity, geographical positioning, node mobility, device configuration, and others. Network clustering is a popular overhead communication management method. Many cluster-based routing methods have been developed to address system restrictions. Node clustering based on the Internet of Things (IoT) protocol, may be used to cluster all network nodes according to predefined criteria. Each cluster will have a Smart Designated Node. SDN cluster management is efficient. Many intelligent nodes remain in the network. The network design spreads these signals. This paper presents an intelligent and responsive routing approach for clustered nodes in IoT networks. An existing method builds a new sub-area clustered topology. The Nodes Clustering Based on the Internet of Things (NCIoT) method improves message transmission between any two nodes. This will facilitate the secure and reliable interchange of healthcare data between professionals and patients. NCIoT is a system that organizes nodes in the Internet of Things (IoT) by grouping them together based on their proximity. It also picks SDN routes for these nodes. This approach involves selecting one option from a range of choices and preparing for likely outcomes problem addressing limitations on activities is a primary focus during the review process. Predictive inquiry employs the process of analyzing data to forecast and anticipate future events. This document provides an explanation of compact units. The Predictive Inquiry Small Packets (PISP) improved its backup system and partnered with SDN to establish a routing information table for each intelligent node, resulting in higher routing performance. Both principal and secondary roads are available for use. The simulation findings indicate that NCIoT algorithms outperform CBR protocols. Enhancements lead to a substantial 78% boost in network performance. In addition, the end-to-end latency dropped by 12.5%. The PISP methodology produces 5.9% more inquiry packets compared to alternative approaches. The algorithms are constructed and evaluated against academic ones.
Enhancing Traffic Routing Inside a Network through IoT Technology & Network C...IJCNCJournal
IoT networking uses real items as stationary or mobile nodes. Mobile nodes complicate networking. Internet of Things (IoT) networks have a lot of control overhead messages because devices are mobile. These signals are generated by the constant flow of control data as such device identity, geographical positioning, node mobility, device configuration, and others. Network clustering is a popular overhead communication management method. Many cluster-based routing methods have been developed to address system restrictions. Node clustering based on the Internet of Things (IoT) protocol, may be used to cluster all network nodes according to predefined criteria. Each cluster will have a Smart Designated Node. SDN cluster management is efficient. Many intelligent nodes remain in the network. The network design spreads these signals. This paper presents an intelligent and responsive routing approach for clustered nodes in IoT networks. An existing method builds a new sub-area clustered topology. The Nodes Clustering Based on the Internet of Things (NCIoT) method improves message transmission between any two nodes. This will facilitate the secure and reliable interchange of healthcare data between professionals and patients. NCIoT is a system that organizes nodes in the Internet of Things (IoT) by grouping them together based on their proximity. It also picks SDN routes for these nodes. This approach involves selecting one option from a range of choices and preparing for likely outcomes problem addressing limitations on activities is a primary focus during the review process. Predictive inquiry employs the process of analyzing data to forecast and anticipate future events. This document provides an explanation of compact units. The Predictive Inquiry Small Packets (PISP) improved its backup system and partnered with SDN to establish a routing information table for each intelligent node, resulting in higher routing performance. Both principal and secondary roads are available for use. The simulation findings indicate that NCIoT algorithms outperform CBR protocols. Enhancements lead to a substantial 78% boost in network performance. In addition, the end-to-end latency dropped by 12.5%. The PISP methodology produces 5.9% more inquiry packets compared to alternative approaches. The algorithms are constructed and evaluated against academic ones.
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Approximation of regression-based fault minimization for network trafficTELKOMNIKA JOURNAL
This research associates three distinct approaches for computer network traffic prediction. They are the traditional stochastic gradient descent (SGD) using a few random samplings instead of the complete dataset for each iterative calculation, the gradient descent algorithm (GDA) which is a well-known optimization approach in deep learning, and the proposed method. The network traffic is computed from the traffic load (data and multimedia) of the computer network nodes via the Internet. It is apparent that the SGD is a modest iteration but can conclude suboptimal solutions. The GDA is a complicated one, can function more accurate than the SGD but difficult to manipulate parameters, such as the learning rate, the dataset granularity, and the loss function. Network traffic estimation helps improve performance and lower costs for various applications, such as an adaptive rate control, load balancing, the quality of service (QoS), fair bandwidth allocation, and anomaly detection. The proposed method confirms optimal values out of parameters using simulation to compute the minimum figure of specified loss function in each iteration.
An efficient approach on spatial big data related to wireless networks and it...eSAT Journals
Abstract
Spatial big data acts as a important key role in wireless networks applications. In that spatial and spatio temporal problems contains the distinct role in big data and it’s compared to common relational problems. If we are solving those problems means describing the three applications for spatial big data. In each applications imposing the specific design and we are developing our work on highly scalable parallel processing for spatial big data in Hadoop frameworks by using map reduce computational model. Our results show that enables highly scalable implementations of algorithms using Hadoop for the purpose of spatial data processing problems. Inspite of developing these implementations requires specialized knowledge and user friendly.
Keywords: Spatial Big Data, Hadoop, Wireless Networks, Map reduce
Grid computing or network computing is developed to make the available electric power in the similar way
as it is available for the grid. For that we just plug in the power and whoever needs power, may use it. In
grid computing if a system needs more power than available it can share the computing with other
machines connected in a grid. In this way we can use the power of a super computer without a huge cost
and the CPU cycles that were wasted previously can also be utilized. For performing grid computation in
joined computers through the internet, the software must be installed which supports grid computation on
each computer inside the VO. The software handles information queries, storage management, processing
scheduling, authentication and data encryption to ensure information security.
Enhancing Traffic Routing Inside a Network through IoT Technology & Network C...IJCNCJournal
IoT networking uses real items as stationary or mobile nodes. Mobile nodes complicate networking. Internet of Things (IoT) networks have a lot of control overhead messages because devices are mobile. These signals are generated by the constant flow of control data as such device identity, geographical positioning, node mobility, device configuration, and others. Network clustering is a popular overhead communication management method. Many cluster-based routing methods have been developed to address system restrictions. Node clustering based on the Internet of Things (IoT) protocol, may be used to cluster all network nodes according to predefined criteria. Each cluster will have a Smart Designated Node. SDN cluster management is efficient. Many intelligent nodes remain in the network. The network design spreads these signals. This paper presents an intelligent and responsive routing approach for clustered nodes in IoT networks. An existing method builds a new sub-area clustered topology. The Nodes Clustering Based on the Internet of Things (NCIoT) method improves message transmission between any two nodes. This will facilitate the secure and reliable interchange of healthcare data between professionals and patients. NCIoT is a system that organizes nodes in the Internet of Things (IoT) by grouping them together based on their proximity. It also picks SDN routes for these nodes. This approach involves selecting one option from a range of choices and preparing for likely outcomes problem addressing limitations on activities is a primary focus during the review process. Predictive inquiry employs the process of analyzing data to forecast and anticipate future events. This document provides an explanation of compact units. The Predictive Inquiry Small Packets (PISP) improved its backup system and partnered with SDN to establish a routing information table for each intelligent node, resulting in higher routing performance. Both principal and secondary roads are available for use. The simulation findings indicate that NCIoT algorithms outperform CBR protocols. Enhancements lead to a substantial 78% boost in network performance. In addition, the end-to-end latency dropped by 12.5%. The PISP methodology produces 5.9% more inquiry packets compared to alternative approaches. The algorithms are constructed and evaluated against academic ones.
Enhancing Traffic Routing Inside a Network through IoT Technology & Network C...IJCNCJournal
IoT networking uses real items as stationary or mobile nodes. Mobile nodes complicate networking. Internet of Things (IoT) networks have a lot of control overhead messages because devices are mobile. These signals are generated by the constant flow of control data as such device identity, geographical positioning, node mobility, device configuration, and others. Network clustering is a popular overhead communication management method. Many cluster-based routing methods have been developed to address system restrictions. Node clustering based on the Internet of Things (IoT) protocol, may be used to cluster all network nodes according to predefined criteria. Each cluster will have a Smart Designated Node. SDN cluster management is efficient. Many intelligent nodes remain in the network. The network design spreads these signals. This paper presents an intelligent and responsive routing approach for clustered nodes in IoT networks. An existing method builds a new sub-area clustered topology. The Nodes Clustering Based on the Internet of Things (NCIoT) method improves message transmission between any two nodes. This will facilitate the secure and reliable interchange of healthcare data between professionals and patients. NCIoT is a system that organizes nodes in the Internet of Things (IoT) by grouping them together based on their proximity. It also picks SDN routes for these nodes. This approach involves selecting one option from a range of choices and preparing for likely outcomes problem addressing limitations on activities is a primary focus during the review process. Predictive inquiry employs the process of analyzing data to forecast and anticipate future events. This document provides an explanation of compact units. The Predictive Inquiry Small Packets (PISP) improved its backup system and partnered with SDN to establish a routing information table for each intelligent node, resulting in higher routing performance. Both principal and secondary roads are available for use. The simulation findings indicate that NCIoT algorithms outperform CBR protocols. Enhancements lead to a substantial 78% boost in network performance. In addition, the end-to-end latency dropped by 12.5%. The PISP methodology produces 5.9% more inquiry packets compared to alternative approaches. The algorithms are constructed and evaluated against academic ones.
final Year Projects, Final Year Projects in Chennai, Software Projects, Embedded Projects, Microcontrollers Projects, DSP Projects, VLSI Projects, Matlab Projects, Java Projects, .NET Projects, IEEE Projects, IEEE 2009 Projects, IEEE 2009 Projects, Software, IEEE 2009 Projects, Embedded, Software IEEE 2009 Projects, Embedded IEEE 2009 Projects, Final Year Project Titles, Final Year Project Reports, Final Year Project Review, Robotics Projects, Mechanical Projects, Electrical Projects, Power Electronics Projects, Power System Projects, Model Projects, Java Projects, J2EE Projects, Engineering Projects, Student Projects, Engineering College Projects, MCA Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, Wireless Networks Projects, Network Security Projects, Networking Projects, final year projects, ieee projects, student projects, college projects, ieee projects in chennai, java projects, software ieee projects, embedded ieee projects, "ieee2009projects", "final year projects", "ieee projects", "Engineering Projects", "Final Year Projects in Chennai", "Final year Projects at Chennai", Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, Final Year Java Projects, Final Year ASP.NET Projects, Final Year VB.NET Projects, Final Year C# Projects, Final Year Visual C++ Projects, Final Year Matlab Projects, Final Year NS2 Projects, Final Year C Projects, Final Year Microcontroller Projects, Final Year ATMEL Projects, Final Year PIC Projects, Final Year ARM Projects, Final Year DSP Projects, Final Year VLSI Projects, Final Year FPGA Projects, Final Year CPLD Projects, Final Year Power Electronics Projects, Final Year Electrical Projects, Final Year Robotics Projects, Final Year Solor Projects, Final Year MEMS Projects, Final Year J2EE Projects, Final Year J2ME Projects, Final Year AJAX Projects, Final Year Structs Projects, Final Year EJB Projects, Final Year Real Time Projects, Final Year Live Projects, Final Year Student Projects, Final Year Engineering Projects, Final Year MCA Projects, Final Year MBA Projects, Final Year College Projects, Final Year BE Projects, Final Year BTech Projects, Final Year ME Projects, Final Year MTech Projects, Final Year M.Sc Projects, IEEE Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, IEEE 2009 Java Projects, IEEE 2009 ASP.NET Projects, IEEE 2009 VB.NET Projects, IEEE 2009 C# Projects, IEEE 2009 Visual C++ Projects, IEEE 2009 Matlab Projects, IEEE 2009 NS2 Projects, IEEE 2009 C Projects, IEEE 2009 Microcontroller Projects, IEEE 2009 ATMEL Projects, IEEE 2009 PIC Projects, IEEE 2009 ARM Projects, IEEE 2009 DSP Projects, IEEE 2009 VLSI Projects, IEEE 2009 FPGA Projects, IEEE 2009 CPLD Projects, IEEE 2009 Power Electronics Projects, IEEE 2009 Electrical Projects, IEEE 2009 Robotics Projects, IEEE 2009 Solor Projects, IEEE 2009 MEMS Projects, IEEE 2009 J2EE P
Approximation of regression-based fault minimization for network trafficTELKOMNIKA JOURNAL
This research associates three distinct approaches for computer network traffic prediction. They are the traditional stochastic gradient descent (SGD) using a few random samplings instead of the complete dataset for each iterative calculation, the gradient descent algorithm (GDA) which is a well-known optimization approach in deep learning, and the proposed method. The network traffic is computed from the traffic load (data and multimedia) of the computer network nodes via the Internet. It is apparent that the SGD is a modest iteration but can conclude suboptimal solutions. The GDA is a complicated one, can function more accurate than the SGD but difficult to manipulate parameters, such as the learning rate, the dataset granularity, and the loss function. Network traffic estimation helps improve performance and lower costs for various applications, such as an adaptive rate control, load balancing, the quality of service (QoS), fair bandwidth allocation, and anomaly detection. The proposed method confirms optimal values out of parameters using simulation to compute the minimum figure of specified loss function in each iteration.
An efficient approach on spatial big data related to wireless networks and it...eSAT Journals
Abstract
Spatial big data acts as a important key role in wireless networks applications. In that spatial and spatio temporal problems contains the distinct role in big data and it’s compared to common relational problems. If we are solving those problems means describing the three applications for spatial big data. In each applications imposing the specific design and we are developing our work on highly scalable parallel processing for spatial big data in Hadoop frameworks by using map reduce computational model. Our results show that enables highly scalable implementations of algorithms using Hadoop for the purpose of spatial data processing problems. Inspite of developing these implementations requires specialized knowledge and user friendly.
Keywords: Spatial Big Data, Hadoop, Wireless Networks, Map reduce
Grid computing or network computing is developed to make the available electric power in the similar way
as it is available for the grid. For that we just plug in the power and whoever needs power, may use it. In
grid computing if a system needs more power than available it can share the computing with other
machines connected in a grid. In this way we can use the power of a super computer without a huge cost
and the CPU cycles that were wasted previously can also be utilized. For performing grid computation in
joined computers through the internet, the software must be installed which supports grid computation on
each computer inside the VO. The software handles information queries, storage management, processing
scheduling, authentication and data encryption to ensure information security.
Load Balance in Data Center SDN Networks IJECEIAES
In the last two decades, networks had been changed according to the rapid changing in its requirements. The current Data Center Networks have large number of hosts (tens or thousands) with special needs of bandwidth as the cloud network and the multimedia content computing is increased. The conventional Data Center Networks (DCNs) are highlighted by the increased number of users and bandwidth requirements which in turn have many implementation limitations. The current networking devices with its control a nd forwarding planes coupling result in network architectures are not suitable for dynamic computing and storage needs. Software Defined networking (SDN) is introduced to change this notion of traditional networks by decoupling control and forwarding planes. So, due to the rapid increase in the number of applications, websites, storage space, and some of the network resources are being underutilized due to static routing mechanisms. To overcome these limitations, a Software Defined Network based Openflow Data Center network architecture is used to obtain better performance parameters and implementing traffic load balancing function. The load balancing distributes the traffic requests over the connected servers, to diminish network congestions, and reduce un derutilization problem of servers. As a result, SDN is developed to afford more effective configuration, enhanced performance, and more flexibility to deal with huge network designs.
Novel holistic architecture for analytical operation on sensory data relayed...IJECEIAES
With increasing adoption of the sensor-based application, there is an exponential rise of the sensory data that eventually take the shape of the big data. However, the practicality of executing high end analytical operation over the resource-constrained big data has never being studied closely. After reviewing existing approaches, it is explored that there is no cost-effective schemes of big data analytics over large scale sensory data processiing that can be directly used as a service. Therefore, the propsoed system introduces a holistic architecture where streamed data after performing extraction of knowedge can be offered in the form of services. Implemented in MATLAB, the proposed study uses a very simplistic approach considering energy constrained of the sensor nodes to find that proposed system offers better accuracy, reduced mining duration (i.e. faster response time), and reduced memory dependencies to prove that it offers cost effective analytical solution in contrast to existing system.
Implementing Machine Learning Algorithms for Predictive Network Maintenance i...ijwmn
With the evolution of fifth generation (5G) network technologies, network maintenance strategies have become increasingly complex, necessitating the use of predictive analysis enabled by Machine Learning (ML) algorithms. This paper emphasizes exploring how ML algorithms can further enhance predictive maintenance in 5G and future networks. It reviews the current literature on this interdisciplinary topic, identifying key ML models such as Decision Trees, Neural Networks, and Support Vector Machines, and discussing their benefits and limitations. Special attention is given to the methodologies in applying these models, handling of data stages, and the training process. Major challenges in implementing ML in the context of network maintenance, such as data privacy, data gathering, model training, and generalizability, are discussed. Furthermore, the research aims to go beyond predicting maintenance needs to introduce a proactive approach in improving overall network performance and pre-empting potential issues based on ML predictions. The paper also discusses possible future trends including advancements in ML algorithms, Automated Machine Learning (AutoML), Explainable AI, and others. The objective is to provide a comprehensive understanding of the current ML-based predictive maintenance field and outline possibilities for future research. The study finds that the application of ML algorithms continues to show promise in transforming the landscape of network management by improving predictive maintenance and proactive performance enhancement strategies. It remains a challenging yet important area in the context of 5G networks.
Implementing Machine Learning Algorithms for Predictive Network Maintenance i...ijwmn
With the evolution of fifth generation (5G) network technologies, network maintenance strategies have become increasingly complex, necessitating the use of predictive analysis enabled by Machine Learning (ML) algorithms. This paper emphasizes exploring how ML algorithms can further enhance predictive maintenance in 5G and future networks. It reviews the current literature on this interdisciplinary topic, identifying key ML models such as Decision Trees, Neural Networks, and Support Vector Machines, and discussing their benefits and limitations. Special attention is given to the methodologies in applying these models, handling of data stages, and the training process. Major challenges in implementing ML in the context of network maintenance, such as data privacy, data gathering, model training, and generalizability, are discussed. Furthermore, the research aims to go beyond predicting maintenance needs to introduce a proactive approach in improving overall network performance and pre-empting potential issues based on ML predictions. The paper also discusses possible future trends including advancements in ML algorithms, Automated Machine Learning (AutoML), Explainable AI, and others. The objective is to provide a comprehensive understanding of the current ML-based predictive maintenance field and outline possibilities for future research. The study finds that the application of ML algorithms continues to show promise in transforming the landscape of network management by improving predictive maintenance and proactive performance enhancement strategies. It remains a challenging yet important area in the context of 5G networks.
Insights on critical energy efficiency approaches in internet-ofthings applic...IJECEIAES
Internet-of-things (IoT) is one of the proliferated technologies that result in a larger scale of connection among different computational devices. However, establishing such a connection requires a fault-tolerant routing scheme. The existing routing scheme results in communication but does not address various problems directly linked with energy consumption. Cross layer-based scheme and optimization schemes are frequently used scheme for improving the energy efficiency performance in IoT. Therefore, this paper investigates the approaches where cross-layer-based schemes are used to retain energy efficiencies among resource-constrained devices. The paper discusses the effectivity of the approaches used to optimize network performance in IoT applications. The study outcome of this paper showcase that there are various open-end issues, which is required to be addressed effectively in order to improve the performance of application associated with the IoT system.
BIG DATA NETWORKING: REQUIREMENTS, ARCHITECTURE AND ISSUESijwmn
A flexible, efficient and secure networking architecture is required in order to process big data. However,
existing network architectures are mostly unable to handle big data. As big data pushes network resources
to the limits it results in network congestion, poor performance, and detrimental user experiences. This
paper presents the current state-of-the-art research challenges and possible solutions on big data
networking theory. More specifically, we present the state of networking issues of big data related to
capacity, management and data processing. We also present the architectures of MapReduce and Hadoop
paradigm with research challenges, fabric networks and software defined networks (SDN) that are used to
handle today’s idly growing digital world and compare and contrast them to identify relevant problems and
solutions.
BIG DATA NETWORKING: REQUIREMENTS, ARCHITECTURE AND ISSUESijwmn
A flexible, efficient and secure networking architecture is required in order to process big data. However,
existing network architectures are mostly unable to handle big data. As big data pushes network resources
to the limits it results in network congestion, poor performance, and detrimental user experiences. This
paper presents the current state-of-the-art research challenges and possible solutions on big data
networking theory. More specifically, we present the state of networking issues of big data related to
capacity, management and data processing. We also present the architectures of MapReduce and Hadoop
paradigm with research challenges, fabric networks and software defined networks (SDN) that are used to
handle today’s idly growing digital world and compare and contrast them to identify relevant problems and
solutions.
BIG DATA NETWORKING: REQUIREMENTS, ARCHITECTURE AND ISSUESijwmn
A flexible, efficient and secure networking architecture is required in order to process big data. However, existing network architectures are mostly unable to handle big data. As big data pushes network resources
to the limits it results in network congestion, poor performance, and detrimental user experiences. This paper presents the current state-of-the-art research challenges and possible solutions on big data networking theory. More specifically, we present the state of networking issues of big data related to
capacity, management and data processing. We also present the architectures of MapReduce and Hadoop paradigm with research challenges, fabric networks and software defined networks (SDN) that are used to handle today’s idly growing digital world and compare and contrast them to identify relevant problems and solutions.
THE DEVELOPMENT AND STUDY OF THE METHODS AND ALGORITHMS FOR THE CLASSIFICATIO...IJCNCJournal
This paper represents the results of the research, which have allowed us to develop a hybrid
approach to the processing, classification, and control of traffic routes. The approach enables to
identify traffic flows in the virtual data center in real-time systems. Our solution is based on the
methods of data mining and machine learning, which enable to classify traffic more accurately
according to more criteria and parameters. As a practical result, the paper represents the
algorithmic solution of the classification of the traffic flows of cloud applications and services
embodied in a module for the controller of the software-defined network. This solution enables to
increase the efficiency of handling user requests to cloud applications and reduce the response
time, which has a positive effect on the quality of service in the network of the virtual data center
Resource Consideration in Internet of Things A Perspective Viewijtsrd
The ubiquitous computing and its applications at different levels of abstraction are possible mainly by virtualization. Most of its applications are becoming pervasive with each passing day and with the growing trend of embedding computational and networking capabilities in everyday objects of use by a common man. Virtualization provides many opportunities for research in IoT since most of the IoT applications are resource constrained. Therefore, there is a need for an approach that shall manage the resources of the IoT ecosystem. Virtualization is one such approach that can play an important role in maximizing resource utilization and managing the resources of IoT applications. This paper presents a survey of Virtualization and the Internet of Things. The paper also discusses the role of virtualization in IoT resource management. Rishikesh Sahani | Prof. Avinash Sharma ""Resource Consideration in Internet of Things: A Perspective View"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23694.pdf
Paper URL: https://www.ijtsrd.com/computer-science/world-wide-web/23694/resource-consideration-in-internet-of-things-a-perspective-view/rishikesh-sahani
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Big data traffic management in vehicular ad-hoc network IJECEIAES
Today, the world has experienced a new trend with regard to data system management, traditional database management tools have become outdated and they will no longer be able to process the mass of data generated by different systems, that's why big data is there to process this mass of data to bring out crucial information hidden in this data, and without big data technologies the treatment is very difficult to manage; among the domains that uses big data technologies is vehicular ad-hoc network to manage their voluminous data. In this article, we establish in the first step a method that allow to detect anomalies or accidents within the road and compute the time spent in each road section in real time, which permit us to obtain a database having the estimated time spent in all sections in real time, this will serve us to send to the vehicles the right estimated time of arrival all along their journey and the optimal route to attain their destination. This database is useful to utilize it like inputs for machine learning to predict the places and times where the probability of accidents is higher. The experimental results prove that our method permits us to avoid congestions and apportion the load of vehicles in all roads effectively, also it contributes to road safety.
Virtual Machine Allocation Policy in Cloud Computing Environment using CloudSim IJECEIAES
Cloud computing has been widely accepted by the researchers for the web applications. During the past years, distributed computing replaced the centralized computing and finally turned towards the cloud computing. One can see lots of applications of cloud computing like online sale and purchase, social networking web pages, country wide virtual classes, digital libraries, sharing of pathological research labs, supercomputing and many more. Creating and allocating VMs to applications use virtualization concept. Resource allocates policies and load balancing polices play an important role in managing and allocating resources as per application request in a cloud computing environment. Cloud analyst is a GUI tool that simulates the cloud-computing environment. In the present work, the cloud servers are arranged through step network and a UML model for a minimization of energy consumption by processor, dynamic random access memory, hard disk, electrical components and mother board is developed. A well Unified Modeling Language is used for design of a class diagram. Response time and internet characteristics have been demonstrated and computed results are depicted in the form of tables and graphs using the cloud analyst simulation tool.
Campus realities: forecasting user bandwidth utilization using Monte Carlo si...IJECEIAES
Adequate network design, planning, and improvement are pertinent in a campus network as the use of smart devices is escalating. Underinvesting and overinvesting in campus network devices lead to low network performance and low resource utilization respectively. Due to this fact, it becomes very necessary to ascertain if the current network capacity satisfies the available bandwidth requirement. The bandwidth demand varies from different times and periods as the number of connected devices is on the increase. Thus, emphasizing the need for adequate bandwidth forecast. This paper presents a Monte Carlo simulation model that forecast user bandwidth utilization in a campus network. This helps in planning campus network design and upgrade to deliver available content in a period of high and normal traffic load.
Design and Implementation of Smart congestion control systemdbpublications
The frequent traffic jams at major junctions
call for an efficient traffic management
system in place. The resulting wastage of
time and increase in pollution levels can be
eliminated on a city-wide scale by these
systems.
The project proposes to implement
an intelligent traffic controller using real
time image processing. The image
sequences from a camera are analyzed using
thresholding method to find the density.
Subsequently, the number of vehicles at
the intersection is evaluated and traffic is
efficiently managed. The project also
proposes to implement a real-time
emergency vehicle detection system. In case
an emergency vehicle is detected, the lane is
given priority over all the others. Hardware
control is done by microcontroller.
Designing and configuring context-aware semantic web applicationsTELKOMNIKA JOURNAL
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Due to the emerging technological advances, cyber-attacks continue to hamper information systems. The changing dimensionality of cyber threat landscape compel security experts to devise novel approaches to address the problem of network intrusion detection. Machine learning algorithms are extensively used to detect intrusions by dint of their remarkable predictive power. This work presents an ensemble approach for network intrusion detection using a concept called Stacking. As per the popular no free lunch theorem of machine learning, employing single classifier for a problem at hand may not be ideal to achieve generalization. Therefore, the proposed work on network intrusion detection emphasizes upon a combinative approach to improve performance. A robust processing paradigm called Graphlab Create, capable of upholding massive data has been used to implement the proposed methodology. Two benchmark datasets like UNSW NB-15 and UGR’ 16 datasets are considered to demonstrate the validity of predictions. Empirical investigation has illustrated that the performance of the proposed approach has been reasonably good. The contribution of the proposed approach lies in its finesse to generate fewer misclassifications pertaining to various attack vectors considered in the study.
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Computer simulations are without a doubt a useful methodology that allows to explore research queries and develop prototypes at lower costs and timeframes than those required in hardware processes. The simulation tools used in cognitive radio networks (CRN) are undergoing an active process. Currently, there is no stable simulator that enables to characterize every element of the cognitive cycle and the available tools are a framework for discrete-event software. This work presents the spectral mobility simulator in CRN called “App MultiColl-DCRN”, developed with MATLAB’s app designer. In contrast with other frameworks, the simulator uses real spectral occupancy data and simultaneously analyzes features regarding spectral mobility, decision-making, multi-user access, collaborative scenarios and decentralized architectures. Performance metrics include bandwidth, throughput level, number of failed handoffs, number of total handoffs, number of handoffs with interference, number of anticipated handoffs and number of perfect handoffs. The assessment of the simulator involves three scenarios: the first and second scenarios present a collaborative structure using the multi-criteria optimization and compromise solution (VIKOR) decision-making model and the naïve Bayes prediction technique respectively. The third scenario presents a multi-user structure and uses simple additive weighting (SAW) as a decision-making technique. The present development represents a contribution in the cognitive radio network field since there is currently no software with the same features.
8th International Conference on Soft Computing, Mathematics and Control (SMC ...josephjonse
8th International Conference on Soft Computing, Mathematics and Control (SMC 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications impacts and challenges of Soft Computing, Mathematics and Control. The conference documents practical and theoretical results which make a fundamental contribution for the development of Soft Computing, Mathematics and Control. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
Implementation of Pipelined Architecture for Physical Downlink Channels of 3G...josephjonse
LTE (Long Term Evolution) is a high data rate, low latency and packet optimized radio access technology designed to support roaming Internet access via cell phones and handheld devices in 3G and 4G networks. This paper mainly focuses on to improve the processing speed and decrease the maximum delay of the downlink channels using the pipelined buffer controlled technique. This paper proposes Pipelined buffer controlled Architecture for both transmitter and receiver for Physical Downlink channels of 3GPP-LTE. The transmitter architecture comprises Bit Scrambling, Modulation mapping, Layer mapping, Precoding and Resource element mapping modules. The receiver architecture comprises Demapping from resource elements, Decoding, Comparing and Detection, Delayer mapping and Descrambling modules as described in LTE specifications. In addition to these, buffers are included in both transmitter and receiver architectures. Modelsim is used for simulation, synthesis and implementation are achieved using PlanAhead13.2 tool on Virtex-5, xc5vlx50tff1136-1 device board is used. Implemented results are discussed in terms of RTL design, FPGA editor, Power estimation and Resource estimation.
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relationships of children with their teachers and peers as a key aspect of integration for students with a migration
background. The study has led to several crucial findings. It emphasizes the significance of speaking Colloquial
Moroccan Arabic (Darija) and being part of a community for effective integration. Moreover, it reveals that the
use of Modern Standard Arabic as the language of instruction in schools is a source of frustration for students,
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Trend-Based Networking Driven by Big Data Telemetry for Sdn and Traditional Networks
1. International Journal of Next-Generation Networks (IJNGN) Vol.11, No.1, March 2019
DOI : 10.5121/ijngn.2019.11101 1
TREND-BASED NETWORKING DRIVEN BY BIG
DATA TELEMETRY FOR SDN AND TRADITIONAL
NETWORKS
Ankur Jain1
, Arohi Gupta2
, Ashutosh Gupta3
, Dewang Gedia4
, Leidy Pérez
5
, Levi
Perigo6
, Rahil Gandotra7
and Sanjay Murthy8
Interdisciplinary Telecom Program, University of Colorado Boulder, USA
ABSTRACT
Organizations face a challenge of accurately analyzing network data and providing automated action
based on the observed trend. This trend-based analytics is beneficial to minimize the downtime and
improve the performance of the network services, but organizations use different network management
tools to understand and visualize the network traffic with limited abilities to dynamically optimize the
network. This research focuses on the development of an intelligent system that leverages big data
telemetry analysis in Platform for Network Data Analytics (PNDA) to enable comprehensive trend-
based networking decisions. The results include a graphical user interface (GUI) done via a web
application for effortless management of all subsystems, and the system and application developed in
this research demonstrate the true potential for a scalable system capable of effectively benchmarking
the network to set the expected behavior for comparison and trend analysis. Moreover, this research
provides a proof of concept of how trend analysis results are actioned in both a traditional network and
a software-defined network (SDN) to achieve dynamic, automated load balancing.
KEYWORDS
Ansible, Big Data, Collectd, Jupyter, Kafka, Logstash, Network Analysis, PNDA, Python, OpenFlow, Ryu,
SNMP, Software-defined Networking, Trend Analysis
1. INTRODUCTION
In the early-to-mid 1990s the Internet’s growth changed the rules and due process in the daily
lives of users all around the globe. What started as email for scientists and early file transfer
systems, is now what moves every fiber of society regardless of culture, expertise or even direct
exposure to connectivity [1]. This vast global adoption led to a shift in paradigm from
manageable quantities of network data to a massive volume of traffic. Automation and
orchestration applications such as virtual load balancers have become critical to cloud and data
center control systems [2, 13, 15].
According to Cisco’s figures for global IP growth for mobile and fixed networks, presented in
their Visual Networking Index (VNI) Forecast report [3], the number of global Internet users will
reach 4.6 billion by 2021 with 27.1 billion network devices, generating over 3.3 zettabytes
of IP traffic per year. Therefore, the Internet has entered big data telemetry territory, kicking off,
as Cisco coined it first, The Zettabyte Era [4].
The main challenges that network managers are facing are scalability, analytics and actioning of
results. Numerous Network Management System (NMS) tools have been developed with the goal
2. International Journal of Next-Generation Networks (IJNGN) Vol.11, No.1, March 2019
2
of making data reveal the status of large networks. However, there is little automated action on
the analytics results, which often include metrics on observable trends in network operations [5,
16].
The solution presented in this paper is an intelligent system that leverages the scalability and
analytics capabilities of the Platform for Network Data Analytics (PNDA), to then enable
automated networking decisions to be executed in the underlying network topologies.
The remainder of the paper is organized as follows: Section II provides a review of the existing
body of knowledge, state of the art applications, and how our scheme extends it. Sections III and
IV describe the methodology and results of our experiment respectively. Section V concludes our
research and addresses scope for future enhancements.
2. RELATED WORK
When it comes to network analytics, there have been related efforts around providing a network
manager with a global view of the performance of their network. The common output is a visual
representation of data via dashboards and an alarm functionality for unexpected changes in
performance. The end goal being passive monitoring that serves as an aid to network operations.
However, the demand for functionality besides monitoring is overwhelming [6, 18, 19, 20, 21].
Enterprise Management Associates (EMA) did a research study titled “Network Management
Megatrends 2016: Managing Networks in the Age of the Internet of Things, Hybrid Clouds and
Advanced Network Analytics” [7]. Their premise was in this big data era, enterprise network
managers are starting to have “higher aspirations” for the applications of the telemetry data
collected from their networks. The results showed that, at the time of the study, advanced
analytics tools have a deep impact in not only network operations, but in business applications.
Network security monitoring resulted in the main advantage sought after with 38% of their data
set, closely followed by network optimization as the second most popular use case with 32% of
support. Their work not only backs up the multiple use cases for the research in this study, but
also the increasing demand in network optimization driven by big data, specifically, big data
telemetry. Big data telemetry defined as the robust collection of enormous quantities of network
data and its aggregation onto one centralized big data analytics platform for analysis, which is the
key decision driver of the architecture designed for the system presented in this paper.
Furthermore, there are previous efforts aimed at providing a solution for a network topology that
includes both traditional and software-defined networking (SDN) topologies [13, 15].
Additionally, network engineering practices face a major challenge of network optimization in
this hybrid network scenario [8, 16].
One of these initiatives was brought forward by the Polytechnic School of Engineering from New
York University. Their emphasis was to develop a congestion-aware single link failure recovery
system for hybrid SDN [9], in order to achieve fast failover recovery and load balancing post
recovery. The way they achieved this was by developing a heuristic algorithm that utilizes SDN
devices in the recovery path that have a global view of the network to formulate recovery routes.
The key difference from their work to the solution presented in this research is that they solved
for a hybrid network, one with both traditional and SDN devices interfaced together, versus
having a mixed solution by having the two network topologies be independent of each other, like
the one this research focuses on.
In terms of automated load balancing, a team from the Ryazan State Radio Engineering
University worked on developing an improved model of multipath adaptive routing in computer
networks with a focus on load balancing capabilities [10]. Their approach on load balancing was
3. International Journal of Next-Generation Networks (IJNGN) Vol.11, No.1, March 2019
3
heavily focused on the indicator of jitter optimization to select an optimal route. Their work
resulted in an algorithm capable of acting as a baseline for new routing protocols, in contrast with
the research for this study which presents a holistic user-oriented solution by integrating multiple
subsystems to achieve a trend-based load balancing platform equipped with big data analytics and
a Graphical User Interface (GUI) for ease of management.
In the article “A Dynamic Bandwidth Allocation Algorithm in Mobile Networks with Big Data of
Users and Networks” [11] the authors utilize big data telemetry collected from the network to
“cluster users by analyzing their closeness, both geographical and social, such that users in the
same cluster share a wireless channel for downloading contents from the base station.”
Effectively showing how leveraging big data for dynamic resource allocation optimizes network
management practices. However, the work done in this research differentiates itself from their
research by having the core of the decision-making capabilities designed around trend analysis
over time, creating an intelligent predictive system, versus real time resource allocation.
This research aims to answer the research question: “Can big data collection, indexing,
filtering, aggregation, benchmarking, and trend based analysis and actioning be service-
chained to collectively provide a centralized big data analytics platform for telemetry data
ingestion and analysis?” This research question was strategically divided into sub-problems to
answer the primary research question:
a. Can SNMP and OpenFlow be used to fetch network-wide telemetry data from traditional
and software-defined networks respectively and store this information in a centralized
database?
b. Can Logstash be used to filter and sort the data metrics to serve as input to PNDA’s data
streaming interface, Kafka? If so, can Jupyter Notebook streamline the data from Kafka
interface to OpenTSDB through PNDA’s network analysis tools?
c. Can statistical benchmarking be performed on the stored telemetry data by real-time
monitoring to identify trends and take appropriate action?
The novel contribution of this paper is to design and implement a network architecture and an
application that can be used together in a hybrid network to perform trend-based networking
through analytics and proactive network monitoring. Furthermore, this research developed a self-
sufficient system capable of making intelligent decisions through leveraging a powerful big data
network analytics tool to load balance traffic in both SDN and traditional networks based on
observable trends in link utilization. This research is beneficial to organizations because not only
does it analyze network traffic, but also dynamically takes a corrective action for the identified
network trend; thus, achieving trend-based, automated network optimization for better overall
network performance.
3. RESEARCH OVERVIEW
The design solution for this system is developed for scalability, analytics, and actioning of results.
All subsystems are carefully integrated to ensure telemetry collection is able to scale to massive
data sets, given all hardware specifications are adjusted with the increase in traffic expectancy.
The core component driving the design of all subsystems was PNDA, acting as the centralized big
data analytics platform for telemetry data ingestion and analysis. See Figure 1.
4. International Journal of Next-Generation Networks (IJNGN) Vol.11, No.1, March 2019
4
Fig. 1. Test Architecture
A. Can SNMP and OpenFlow be used to fetch network-wide telemetry data from traditional
and software-defined networks respectively to store it in a centralized database?
The first step in the system is to perform the telemetry data collection from the underlying
network topologies. This was achieved by designing a network configuration that meets the
design requirements of SNMP data collection from traditional networks and switch statistics
retrieval from SDN. SNMP data is collected from the traditional network using a systems
statistics collection daemon (CollectD) to set frequency of data collection and tag traffic with the
host’s source IP. The traditional network topology can be seen in Figure 2, where SNMP agents
have been set up in each device.
Fig. 2. Traditional Network Topology
The SDN topology is shown in Figure 3. To collect switch statistics from it, it is necessary to use
a REST API and the OpenFlow protocol to fetch counters from each SDN switch via network
flows. To meet this requirement, the selection of the SDN controller was based on the best fit
REST API for the purpose, resulting in the implementation of the Ryu SDN controller. The
resulting network architecture design enables a scalable telemetry data collection subsystem for
both SDN and traditional networks.
5. International Journal of Next-Generation Networks (IJNGN) Vol.11, No.1, March 2019
5
Fig. 3. SDN Topology
B. Can Logstash be used to filter and sort the data metrics to serve as input to PNDA’s data
streaming interface, Kafka? If so, can Jupyter Notebook streamline the data from Kafka
interface to OpenTSDB through PNDA’s network analysis tools?
The second step the system follows is to filter all telemetry data and prepare it for the big data
analytics tool, PNDA. The design requirement was to have all raw data, collected from both
networks, be filtered and formatted for seamless ingestion into PNDA. The tool selected for this
development was Logstash, a data processing pipeline used to ingest data from different sources
into a common platform with the required format [12].
For the traditional network, this is done by taking the data coming from the network via a
CollectD configuration file written specifically for this project, and sending it to Logstash’s input
plugin. The SDN is capable of sending data directly into the same plugin without being filtered
through CollectD.
Logstash was configured to run the data through its filter plugin and sort through it to get only
specific metrics such as outPkts and outOctets. This data set is then sent to the output plugin,
which serializes and encodes it using a codec named ‘avro-codec’, a requirement to ensure
compatibility with the following subsystem. This sequence allows the system to send to PNDA’s
data streaming interface, Kafka, only valuable and properly formatted data, which in turn
streamlines the process and reduces the workload on the server [17].
Fig. 4. Logstash Indexing Instance
6. International Journal of Next-Generation Networks (IJNGN) Vol.11, No.1, March 2019
6
The third step built into the system’s workflow is the subsystem to aggregate all data coming
from Logstash to PNDA. At this point, all data collected from both SDN and the traditional
network is ready for PNDA’s ingestion. The main design requirement for this stage is to get data
from Kafka, the data streaming interface in charge of parsing through the data in real time, to
PNDA’s own Time Series Data Base, OpenTSDB. To do this, the main development was to
implement an original Jupyter notebook.
A Jupyter notebook is the format of python algorithm Kafka is capable of integrating with. It
contains all commands on what to do with input, data incoming from Logstash, and where to
store the output. This stage is coded to go from Kafka, through PNDA’s network analysis tools,
to finally store results in OpenTSDB and make them available via Impala, a standard SQL
interface used to query data from PNDA. See Figure 5.
Fig. 5. PNDA Cluster
This results in data being ran through PNDA’s analytics engine and getting ready to be utilized
for benchmarking and trend analysis in the following subsystems.
C. Can statistical benchmarking be performed on the stored telemetry data by real-time
monitoring to identify the trend and take appropriate action?
The fourth step for the integrated system to go through consists of leveraging PNDA’s analytics
to set a benchmark for both network configurations and to determine what behavior in the active
topologies constitutes a trend that needs action in time versus changes that are one time
occurrences. The development required for this sub-system was a GUI, see Figure 6, with two
main components: a Python back-end architecture and a bootstrap front-end development. The
front-end design, see Figure 7, is a comprehensive user-centric web application designed to
address network engineers’ needs of centralized resources to manage a system of this magnitude.
To avoid duplicating efforts, it also has a redirect tab to PNDA’s own dashboard for analytics, see
Figure 8.
7. International Journal of Next-Generation Networks (IJNGN) Vol.11, No.1, March 2019
7
Fig. 6. Graphical User Interface (GUI) block diagram
Fig. 7. GUI Front-End
Fig. 8. PNDA’s Dashboard
8. International Journal of Next-Generation Networks (IJNGN) Vol.11, No.1, March 2019
8
The back-end scheme is the engine that integrates all subsystems up to this step by performing
two global tasks, benchmarking and trend analysis. Benchmarking runs all commands to execute
subsystems one through three to then consume data analytics coming from PNDA and execute
the algorithm’s section developed to evaluate network behavior without intervention. This unit of
the code is set to query all telemetry data for an established time period and frequency, to then
run a statistical distribution of data and get the mean value of link utilization for the main output
ports in the system. It will also set a threshold of interface bandwidth to separate “normal traffic,”
from “high traffic,” which will also be used as a trend indicator. This process will repeat to reset
the benchmark with a user determined frequency. See Figure 9.
For trend analysis, the code was developed to identify link utilization trends that will be utilized
for load balancing. It takes the statistical values and threshold established during benchmarking to
find the standard deviation of new incoming traffic, all while monitoring the interface bandwidth
threshold. Once traffic crosses the threshold and deviates significantly from mean value of traffic,
the code will raise a flag to mark the trend, until traffic lowers back to below the threshold or
within regular values, and the change that needs to be addressed is established in a timeframe.
This process effectively enables the back-end to action a specific trend and make the right
decision to load balance the system during the right period of time.
Once data is collected, filtered, ran through big data analytics and submitted for benchmarking to
identify the trend, the system is ready for the call to action on the latest trend spotted by the back-
end, all while providing visibility in the front-end. The call to action of a trend-based networking
decision is executed by pushing the appropriate data structure to the correct devices in each
topology. For the traditional network, this is done via an open-source library that enables SSH to
routers, Netmiko. Which will send the appropriate route-map CLI commands to the key affected
router. The route-map contains the CLI commands necessary for traditional routers to associate a
lower local preference for the specific affected subnet and a higher local preference for the new
path, which determines where traffic is routed during the timeframe needed. For the SDN
topology, the same concept is used, with the difference of flows entries being pushed to the SDN
instead of route-maps.
.
Fig. 9. Benchmarking Code Flow
9. International Journal of Next-Generation Networks (IJNGN) Vol.11, No.1, March 2019
9
OpenFlow flow entries will determine the change in path for traffic during the timeframe
established. Once a decision has been made and pushed to the network, the user can choose to
have the decision be effective for a period of time and then revert it to reevaluate the status over
time in their network. The logic followed by the code is detailed in Figure 10.
Fig. 10. Trend-Based Networking Decision Block Diagram
When all steps are performed in an integrated, seamless manner, the end result is a trend-based
networking system that utilizes powerful tools, such as PNDA for big data telemetry analysis, to
provide the user with the most powerful interpretation and usage of their network data. Not only
does the user get all monitoring benefits from PNDA’s built in functionality, but also gets a GUI
on which to track trends and actions taken in the network to keep traffic running without
congestion or packet loss, with minimal human intervention.
4. RESULTS AND ANALYSIS
A. Physical Test Setup
Two servers were utilized to distribute all components of the system as seen in Figure 11. The
hardware and software specifications are broken down in Table 1 and Table 2 respectively.
Multiple levels of virtualization and different types of servers were used to complete this physical
setup. The Server 1 is a bare metal server running Ubuntu 16.01 as its Virtualization Layer 0. On
this Ubuntu Server is where VirtualBox is set up, making Virtualization Layer 1. Then,
VirtualBox has Ubuntu 14.04 installed as Virtualization Layer 2, which contains the PNDA VM
and the GUI as its logical components. Server 2 is dedicated to network virtualized topologies.
This bare metal server runs VMware Esxi 6.5 as its Virtualization Layer 0 and two Virtual
Machines (VMs) running Ubuntu 16.01, Virtualization Layer 1, for each network design.
10. International Journal of Next-Generation Networks (IJNGN) Vol.11, No.1, March 2019
10
Fig. 11. Physical setup
Table I. Hardware Specifications
Device
Hardware Specifications
Make Model CPU Memory
12 CPUs x
PowerEdge
Intel(R)
Servers Dell Inc Xeon(R) CPU 32 GB
R430 E5-2603 v3 @
1.60GHz
Table II. Software Specifications
11. International Journal of Next-Generation Networks (IJNGN) Vol.11, No.1, March 2019
11
The first VM has the traditional network topology generated using GNS3. The second VM has
the SDN components, in which the Ryu controller and Mininet are set up to establish the
topology. These two VMs are connected to each other using a virtual switch, and further
connected to a router which allows connectivity to the Internet using port forwarding.
B. Proof of Concept
Establish Benchmark
In order to determine the mean value of link utilization of output ports, it was necessary to first
perform telemetry data collection and analysis of the network in a period of 72 hours, in order to
get the baseline behavior. This was achieved by implementing the sub-system integration from
step 1 through step 4 of the workflow described in section III A, Figure 1.
First, traffic was generated using a network performance tool called iPerf 3. The frequency was
set to every 1 hour, 24 samples per day, and 72 samples in total. The network topologies were set
up as displayed in Figures 2 and 3.
The assumption made for traffic generation was that the highest activity hours are between
business hours, 8 AM to 5 PM, with activity decreasing significantly outside of that time frame.
Then for step two, as traffic was generated, it was aggregated by CollectD to Logshtash’s input
plugin for the traditional network and directly from the RYU controller REST API. Logstash ran
the data through its filter plug in to relay inPkts, outPkts, inOctets, outOctets, interface name and
time stamps to PNDA’s Kafka interface using the pnda-avro plugin. See Figure 12 for a sample
of the output coming from Logstash’s output plugin.
Fig. 12. Output from Logstash’s Output Plugin
Now the system is at step three and traffic has arrived to PNDA. Once the Kafka interface detects
the data input, a Jupyter query takes the telemetry data and sends it to the OpenTSDB database
for storage. A snippet of data stored in OpenTSDB can be seen in Figure 13. This shows how
once telemetry data gets to this stage, it is already separated by type. For this snippet, the display
is a snapshot in time for outOctets.
12. International Journal of Next-Generation Networks (IJNGN) Vol.11, No.1, March 2019
12
Fig. 13. Snippet of outPkts in OpenTSDB
Once telemetry data is analyzed and separated by type of input or output, comes the fourth step in
the systems sequence, which consists of the back-end application retrieving data logs from
Impala to set the benchmark for trend analysis. The results of the benchmarking in the back-end
are shown in Figure 14.
Fig. 14. Benchmarking performed in the back-end
From the graph, it can be observed how the data samples were collected for three (3) days with a
frequency of once per hour. A mean value was calculated for every hour separately. This mean
value is used as the benchmark for that particular hour and will be used in the trend based
analysis in future. These results enable the second round of testing, which will entail taking this
benchmark to identify trends and send automated corrective action back to the topologies,
fulfilling the last step in the system’s workflow.
5. CONCLUSIONS AND FUTURE WORK
In this paper, all steps to achieve the full integration of the trend-based networking system driven
by big data telemetry for SDN and traditional networks have been described. It has been proven
that benchmarking the network using all the subsystems built in the design solution is possible
and effective. Moreover, the prospect of the tests conducted in this research can further be
calibrated based on the desired use-case for automated load balancing and trend-based
networking. The contributions of this research relies on the algorithms carefully developed to run
subsystems at the top of their performance, while integrating seamlessly to create a fully
integrated system. The user gets both the benefits of monitoring all the data PNDA is analyzing
via PNDA’s own dashboard, plus an automated trend-based load balancing system administered
via the system’ own GUI with little human intervention needed.
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This design sets the foundation for the next generation of network management system tools. The
number of applications that can be built on top of this work is only limited by what application
the user envisions in their network. Some of the next steps and applications that could be
implemented are:
A. With the benchmark set in the performed test along with the mean value and link utilization
threshold, the next step is to collect data again over a span of seven days, this time picked
tentatively as a generic number for testing purposes. The frequency of collection will be the same
used in benchmarking, once an hour, to keep compatibility between data sets. Once the telemetry
data set has gone through all the same subsystems that the benchmarking test used, it will be time
to identify the trend by analyzing the standard deviation of the data sample for every hour against
its counterpart in the benchmark data set, following the system logic detailed in Figure 10.
B. Expand the scope of network metrics used for trend-based decisions: link utilization was used
as a proof of concept. This network can be scaled to include any network metric compatible with
the data collection system, such as CPU utilization, bandwidth, and latency.
C. Machine Learning for Network Trends: leveraging PNDA’s big data capabilities, it is possible
to keep a comprehensive archive of all trends identified and have a machine learning algorithm
running in the back end to start predicting network behavior before the trend even occurs.
D. Evolve from load balancing via prefix to load balancing via multipath: our current solution
implements a prefix-centric load balancing approach. If this is taken one step further, multipath
BGP can be implemented for further load balancing capabilities.
E. Connecting traditional and SDN: while this solution is oriented towards SDN and traditional
networks operating independently, it can be developed to support both networks communicating
enabling all hosts pertaining to both networks to achieve end-to-end connectivity.
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AUTHORS
Ankur Jain is a graduate student from the University of Colorado Boulder with
a major in Network Engineering. He currently works as an Associate Systems
Engineer at Juniper Networks.
Arohi Gupta is a graduate student from the Interdisciplinary Telecom Program,
University of Colorado Boulder. She received her bachelor’s degree in
Electronics and Telecommunications Engineering, and currently works as
Software Engineer 3 at Juniper Networks.
Ashutosh Gupta is a graduate student from the University of Colorado Boulder
with a major in Network Engineering. He received his bachelor’s degree in
Electronics and Telecommunications Engineering, and currently works as
SDN/NFV Software Engineer 3 at Juniper Networks.
Dewang Gedia is a Ph.D. candidate at the Interdisciplinary Telecom Program,
University of Colorado Boulder. He received his master’s degree in Network
Engineering, and has primary research focus in network functions virtualization
and software-defined networks domain.
Leidy Pérez is a graduate student from the Interdisciplinary Telecom Program,
University of Colorado Boulder. She received her bachelor’s degree in
Telecommunications Engineering, and currently works as Strategic Operations
Manager at Zayo Group.
Dr. Levi Perigo is a Scholar in Residence and Professor of Network Engineering
at the Interdisciplinary Telecom Program, University of Colorado Boulder. His
interests are in a variety of internetworking technologies such as network
automation, VoIP, IPv6, SDN/NFV, and next generation protocols. Currently, his
research focuses on implementation and best practices for network automation,
SDN, and NFV.
Rahil Gandotra is a Ph.D. student at the Interdisciplinary Telecom Program,
University of Colorado Boulder. He received his bachelor’s degree in
Telecommunications Engineering, and has primary research interests in next-
generation networking focusing on software-defined networking, network
functions virtualization, and energy-efficient networking.
Sanjay Murthy is a graduate student from the Interdisciplinary Telecom
Program, University of Colorado Boulder. He received his bachelor’s degree in
Electronics and Communications Engineering, and currently works as Production
Network Engineer at Facebook.