Mini-Track: Observability
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Moderator: Richard Piasentin, Accedian is now part of Cisco Speakers: Craig Johnson, Forward Networks Josh Stephens, BackBox Justin Ryburn, Kentik
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Handover Parameters Self-optimization by Q-Learning in 4G NetworksMohamed Raafat OMRI محمد رأفت عمري
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NetQoS software and services help service providers, government agencies, and large enterprises – including half of the Fortune 100 – improve the delivery of applications over wide area networks by enabling them to monitor application service levels, troubleshoot problems quickly, and plan for change. Representative NetQoS customers include Chevron, Lockheed Martin, American Express, Hilton Hotels, Siemens, Boeing, Deutsche Telekom, NASA, and Barclays Global Investors. Headquartered in Austin, Texas, NetQoS has R&D centers in Austin and Raleigh, N.C., and regional sales offices in London and Singapore.
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Automating a World-Class Technology Conference; Behind the Scenes of CiscoLive
Automating a World-Class Technology Conference; Behind the Scenes of CiscoLiveNetwork Automation Forum
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questions. The purpose is to know in real-time what is happening on the network at a detailed level, and
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- Upgrading desktop PCs to Windows 7 and standardizing the server OS.
- Implementing a new network architecture with 8 subnets, layer 3 switches, two routers, firewall, and cabling upgrades.
- Choosing protocols like OSPF, EIGRP, and TCP/IP to improve routing and performance.
- Enhancing security with features like firewalls, VLANs, and intrusion detection.
- Analyzing the design using simulation software to optimize performance before implementation.
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Network Intrusion Detection and Prevention Systems (NIDPSs) are used to detect, prevent and
report evidence of attacks and malicious traffic. Our paper presents a study where we used open
source NIDPS software. We show that NIDPS detection performance can be weak in the face of
high-speed and high-load traffic in terms of missed alerts and missed logs. To counteract this
problem, we have proposed and evaluated a solution that utilizes QoS, queues and parallel
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Our approach designs a novel QoS architecture to organise and improve throughput-forwardplan
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Leonid semakov abstract
Why Operations Support Systems (OSS) Are Not the Answer to All? Enhanced Network Performance Evaluation with Mobile Probes", Leonid Semakov, Product Engineer, Rohde & Schwarz, Germany
Performance Evaluation of a Network Using Simulation Tools or Packet Tracer
Today, the importance of information and accessing information is increasing rapidly. With the advancement of technology, one of the greatest means of achieving knowledge are, computers have entered in many areas of our lives. But the most important of them are the communication fields. This study will be a practical guide for understanding how to assemble and analyze various parameters in network performance evaluation and when designing a network what is necessary to looking for to remove the consequences of degrading performance. Therefore, what can you do in a network performance evaluation using simulation tools such as Network Simulation or Packet tracer and how various parameters can be brought together successfully? CCNA, CCNP, HCNA and HCNP educational level has been used and important setting has been simulated one by one. At the result this is a good guide for a local or wide area network. Finally, the performance issues precautions described. Considering the necessary parameters, imaginary networks were designed and evaluated both in CISCO Packet Tracer and Huawei's eNSP simulation program. But it should not be left unsaid that the networks have been designed and evaluated in free virtual environments, not in a real laboratory. Therefore, it is impossible to make actual performance appraisal and output as there is no actual data available.
Handover Parameters Self-optimization by Q-Learning in 4G Networks
Handover Parameters Self-optimization by Q-Learning in 4G NetworksMohamed Raafat OMRI محمد رأفت عمري
With more and more customers using mobile communications it is important for the service providers to give their customers the best Quality-of-Service (QoS) they can afford. Many
providers have taken to improving their networks and make them more appealing to customers. One such improvement that providers can deliver to their customers is to enhance
reliability of the network meaning that customers' calls are less likely to be dropped by the network.
This dissertation explores improving the reliability of a 4G network by optimizing the parameters used in handover. The process of handover within mobile communication networks is very important since it allows users to move around freely while still staying connected to the network. The most important parameters used in the handover process are
the Time-to-Trigger (TTT) and Hysteresis (hys). These parameters are used to determine whether a base station is better than the serving base station by enough offset to warrant a handover taking place. The challenge in optimizing the handover parameters is that there is a fine balance that needs to be struck between calls being dropped due to a handover failing and the connection switching back and forth between two base stations, unnecessarily, wasting the
network resources. In this project, we propose to use a machine learning technique known as Q-Learning to optimize the handover parameters by generating a policy that can be followed to adjust the parameters as needed. It was found that the implemented Q-Learning algorithm was capable of improving the Handover performance by minimizing the chosen Handover related Key Performance Indicators (KPI).NetQoS Performance Center Overview
NetQoS software and services help service providers, government agencies, and large enterprises – including half of the Fortune 100 – improve the delivery of applications over wide area networks by enabling them to monitor application service levels, troubleshoot problems quickly, and plan for change. Representative NetQoS customers include Chevron, Lockheed Martin, American Express, Hilton Hotels, Siemens, Boeing, Deutsche Telekom, NASA, and Barclays Global Investors. Headquartered in Austin, Texas, NetQoS has R&D centers in Austin and Raleigh, N.C., and regional sales offices in London and Singapore.
Industry progress towards a next gen oss for a virtualized network
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