5G
Article | September 28, 2023
Discover the effective tools for analyzing network traffic to improve monitoring efficiency. Enhance the network's performance and raise the network's potential by choosing the appropriate tool.
Automation adoption has become critical for companies in the dynamic fields of manufacturing, logistics, and supply chain management. By minimizing costs, enhancing efficiency, and reducing downtime, automation delivers optimal value for manufacturers. However, selecting the right partner can be challenging with numerous warehouse automation companies available. This article provides the features and benefits of top ten warehouse automation tools, offering expertise in improving business operations. These data warehouse tools provide comprehensive solutions to meet diverse needs, whether through software solutions or tools.
1. Network Performance Monitoring
Datadog Network Performance Monitoring is an exceptional software tool that enhances network monitoring capabilities, providing comprehensive visibility into all components of on-prem, cloud, and hybrid environments. With minimal overhead, the tool monitors the performance of connections among hosts, services, virtual private clouds (VPCs), and other elements, enabling quick identification of network-related issues. It tracks essential network metrics such as TCP retransmits, latency, and connection churn. It also allows monitoring of traffic health between any endpoints at the app, IP address, port, or process ID (PID) layers. Datadog aids in isolating network issues within Envoy-powered service meshes and troubleshooting inefficient load balancing. The software also helps manage cloud networking costs by identifying the responsible services and teams for significant traffic spikes. Deep DNS visibility allows system-wide analysis of DNS performance without SSHing into individual machines. For granular insights, users can explore S3 buckets or RDS databases.
2. Paessler PRTG Network Monitor
Paessler PRTG Network Monitor is a powerful and user-friendly real time network monitoring tool that helps enhance network traffic analysis for industry experts. It offers central monitoring for a comprehensive view of the entire IT infrastructure in one place. With on-premises installation, users have full control over data and configuration. The software supports various technologies and protocols, with automatic network discovery simplifying setup. Feature like custom maps, real-time alerts, and customizable reports provide an overview of the network and facilitate proactive monitoring. Paessler PRTG Network Monitor allows distributed monitoring across remote locations and offers a user-friendly interface for easy installation and use. With full administrative control and perpetual licenses, users have flexibility and cost-effective access to all monitoring features. It is a reliable solution trusted by businesses worldwide for efficient network monitoring, providing industry experts with enhanced visibility, control, and peace of mind.
3. OpManager Plus
OpManager Plus is a robust enterprise network monitoring software that optimizes network performance and ensures an error-free network. It offers real-time health, availability, and performance monitoring, enabling proactive and reactive monitoring techniques. OpManager Plus provides critical metrics for traffic analysis, such as packet loss, response time, resource utilization, errors, and network traffic via SNMP. It includes in-depth server monitoring features, enabling efficient monitoring of servers, routers, switches, and firewalls. The software leverages Cisco IPSLA for WAN and VoIP monitoring, visualizing paths, resolving outages, and diagnosing poor performance. OpManager Plus also offers built-in network troubleshooting tools like ICMP Ping, Traceroute, Switch Port Mapper, SNMP MIB Browser, and Telnet/SSH, empowering industry experts with the necessary tools for first and second-level troubleshooting. It is one of the best network monitoring tools for industry professionals that enhance performance, ensure network availability, and enable efficient troubleshooting.
4. Domotz
A powerful network performance monitoring tool, Domotz revolutionizes network monitoring and management. With its streamlined IT operations, it enhances efficiency and strengthens security measures. As one in many top network monitoring tools, it offers unmatched visibility into digital assets and data flows, empowering users with critical insights from their networks. Its feature-rich software including network autodiscovery, automated mapping, IT asset inventory, and SNMP-based device monitoring caters to MSPs, System Integrators, IT and Security Professionals. These features facilitate effective traffic analysis and resource monitoring. Domotz also offers integrated capabilities like configuration management, versioning, backup, and seamless integrations with PSA systems. By leveraging Domotz, industry experts gain a powerful tool to monitor network traffic, optimize performance, and strengthen overall security.
5. checkmk
checkmk is a powerful network performance monitoring tool that enhances network monitoring and management. Its latest version, checkmk 2.2, provides a comprehensive solution for monitoring hybrid IT infrastructures, including native cloud applications and OpenShift support. The software offers real-time monitoring of dynamic cloud workloads, allowing users to map their cloud infrastructure, monitor cloud-native services, and track resource consumption and costs. checkmk seamlessly integrates with major cloud platforms like AWS, Azure, and Google Cloud Platform, offering cloud-specific dashboards and easy cloud deployment. The monitoring agents of checkmk support push mode for autonomous data transmission and auto-registration for real-time host addition and service discovery. Industry experts benefit from the robust features of checkmk for effective traffic analysis, improved cloud monitoring, and simplified implementation in dynamic cloud environments. checkmk is a reliable choice among network management tools for optimizing network performance and ensuring efficient cloud monitoring.
6. NinjaOne RMM
NinjaOne RM, recognized as the top-rated network performance monitoring tool. It offers powerful and user-friendly RMM software that efficiently manages client endpoints. With NinjaOne, industry experts can monitor, support, and control a wide range of devices, servers, virtual machines, and networking devices from a single interface. The real-time network monitoring software features include alert notifications, automated patching for enhanced security, one-click device actions for streamlined management, secure remote access for fast support, endpoint task automation to optimize workflows, and a self-service portal for client end-users. Its scalability, intuitive UI, and comprehensive support make it one of the best network monitoring tools. Industry experts benefit from improved technician efficiency, reduced complexities, and enhanced service delivery, leading to greater profitability and customer satisfaction.
7. Fortra’s Intermapper
Fortra's Intermapper is an intuitive network mapping tool to monitor network traffic autodiscovery features. The network map is constantly refreshed, ensuring an up-to-date representation of any network. This software offers SNMP-based network monitoring, performance alerts, and a capacity planning tool. By implementing Intermapper, industry experts can efficiently analyze network traffic and detect performance issues in real-time. The tool allows users to set performance threshold levels and receive alerts via email or text, ensuring proactive monitoring and reducing the need for constant screen monitoring.
8. Site24x7 Network Monitoring
Site24x7 is one of the leading tools to monitor network traffic that enhances network monitoring capabilities with its comprehensive features. With granular visibility at the device and interface levels, Site24x7 enables thorough network performance monitoring. The tool offers intelligent features for deep observability, proactive insights, and root cause analysis, all within a single window. This cloud-based solution help users have complete control over their network. It monitors network traffic, allowing users to analyze traffic, packets, errors, and discards, providing actionable insights for optimizing network performance. Additionally, Site24x7 offers automatic network discovery, real-time application and device monitoring, and powerful reporting, analytics, and alerts.
9. Nagios Core
Nagios Core is one of the top network security monitoring tools that serves as an event scheduler, processor, and alert manager. It offers a robust architecture and extensive scope for monitoring various elements. With its APIs, Nagios Core allows easy extension and customization to meet specific monitoring requirements. Implemented as a highly efficient C-based daemon, it delivers optimal performance on Linux and Unix systems. Nagios Core provides a solid foundation for network security monitoring, enabling industry experts to analyze network traffic, detect issues, and receive timely alerts. Its scalability and flexibility features make it a valuable tool for enhancing network traffic analysis and ensuring the security and stability of IT infrastructures.
10. Catchpoint Network Experience
Catchpoint Network Experience, part of the Catchpoint platform, is a comprehensive network performance monitoring tool that ensures the successful delivery of web applications. It offers key features such as internet connectivity checks, BGP assessments, and the ability to assess ISPs and other services. By monitoring network performance, Catchpoint helps identify the root cause of performance issues and ensures optimal delivery of web systems. It analyzes virtual networks, identifies performance issues with remote access and site-to-site VPNs, and traces connections from LAN to web servers. Industry experts can benefit from its website delivery performance tracking, root cause analysis, and virtual network performance monitoring capabilities, making it an invaluable tool for enhancing network monitoring and optimizing web application performance.
Final Thoughts
Maintaining a robust and efficient IT infrastructure relies heavily on effective network monitoring. Integrating server, application, storage, and network management functions enables companies to deliver services seamlessly to end users. A comprehensive suite of network monitoring tools is essential for addressing various aspects of networking technology, including physical components, security measures, and environmental conditions. The ultimate goal is to proactively identify and mitigate potential network issues, reducing their impact on user productivity. In the event of failure, prompt detection and resolution become crucial, and a responsive help desk plays a valuable role in quickly receiving user alerts and uncovering previously undetected problems. By leveraging top network traffic analysis tools, businesses can elevate their network monitoring capabilities and establish a solid foundation for optimal performance and unwavering reliability.
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5G
Article | May 18, 2023
With the emergence of new technologies, the networking field is transforming rapidly. The epicenter of networking has shifted to clouds from datacenters. Similarly, the focus of networking has also moved towards mobile devices. In the upcoming years, tech trends will hugely impact the way a business operates and bring the rise of Industry 4.0.
Top Networking Tech Trends
1. 5G and WI-FI 6
Undoubtedly, the deployment of next-generation wireless networking will be around the corner. In the arena of mobile devices, 5G is set to rewrite the new technological possibilities. It will uncover the true power of augmented reality and IoT.
On the other hand, the next journey of the WI-FI Standard - WI-FI 6 or 802.11ax will become the step for a non-stop innovative world. It will add density, flexibility, scalability, and efficiency for increasing the internet speed of multiple connected devices. That will in return improve the working capabilities of businesses.
2. SD-WAN
As the name suggested, SD-WAN is the software-defined approach for managing WANs. It can lower operating costs while amplifying the usage of resources in multiple deployments. It increases the security level for applications and enables admin to use bandwidth efficiently. It will become the standard format for wide area networks and will help in connecting public cloud resources and branch offices.
3. Secure Access Service Edge (SASE)
SASE is a new networking technology that converges functions of different security and network solutions into one global cloud service. It is an architectural alteration of networking and security that supports IT to offer prompt, holistic, and versatile service to the digital business. It amplifies the security postures, improves access performance, and diminishes operational complexity. It helps organizations to develop new products faster and respond to business needs or changes.
4. IoT/Edge Networking
In comparison to traditional cloud computing, edge computing is the idea to bring data and computers much closer to the end-users. It reduces the need for long-distance communication among client and server, and lessen the cost of bandwidth. It will remain to achieve drift in companies while they decentralize their networks.
5. Automation in Networking
Network automation is the process that automates security and network to maximize the functionality and efficiency of the network. It will help IT companies to deploy applications faster. It is set to take the digital transformation to the next step by automation of network and security operations. It reduces the risk of downtime and failure of the network while making the management faster, simpler, and easier.
Connecting to Future Networking
Based on the trends that will reshape the networking world, we are going to see a significant change in the tech landscape. 2021 will be transformative for every person around the world. Several long-held concepts and infrastructure will be replaced by new ones making the network a vital asset to the business. Besides, the organizations are ready to take advantage of them in a way that was never imagined before. For any question or concern, have an IT consultation from the experienced.
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5G
Article | June 9, 2022
Applications of AI/ML
Modern businesses are adopting Artificial Intelligence (AI) that encompasses disciplines like machine learning (ML), natural language processing (NLP), evolutionary computation, etc., to increase their productivity and management capabilities.
Companies like Qualcomm are using AI and machine learning to improve their customer experience.
“Across many industries, we are currently experiencing the creation of intelligent machines that is using AI to simulate smart behavior.”
-Dr. Vinesh Sukumar, Senior Director- Head of AI/ML Product Management at Qualcomm, ( in an interview with Media7)
The application of machine learning in networking is swiftly taking shape. However, as the problems in modern computer networks are getting tedious to handle, AI tools are being introduced to hard-carry their smooth functioning.
Let’s take a look at how network complexity impacts businesses:
Difference in Network Parameters
Different client devices like laptops, smartphones, CCTV cameras, etc., are connected to a single network. However, their requirements and parameters are different. Therefore, the IT team of the business needs to meet them without compromising the functionality and security of the network.
Users Prefer Wireless Networks
Wireless networks are more complex than wired ones. They perform dynamically depending on the number of users, applications, and other variables.
Impact of Cloud Computing
Most applications are now cloud-based, and such a network has multiple data entry points and requires more support.
User Experience
Deciphering root cause analysis, finding correlation and solutions becomes tedious without an AI/ML model. Complex patterns remain unanalyzed, and this creates a vacuum between the customer and the business.
What Does ML Bring to the Table?
Machine learning applications in networking correlate to solving four types of network problems: clustering, extraction, regression, and classification.
For classification and regression, ML clusters similar data and creates a gap between data groups. It then successfully maps a new set of data to a pre-set continuously valued output. As for extraction, it easily establishes a statistical relationship between the data it analyzes.
Machine learning applications in networking encompass the following:
Automation and Cognitive Computing
ML enables automation in data processing by eliminating the human error factor and constantly improving with time. It analyzes data, improves the productivity, security, and health of the network. Cognitive computing allows processing diverse data sets, detecting and finding root causes and common traits within the system.
Network Monitoring & Security
Network monitoring is used to solve problems in a large dataset by deciphering the hidden pattern in the data. It then predicts the outcome for clustered data, malware attacks, or impending network failure. It recognizes impending threats in time and sends out warnings. ML uses anomaly-based intrusion, misuse-based intrusion, or hybrid intrusion to prevent misuse, modification, unauthorized access, or malfunction.
Traffic Prediction, Classification, and Routing
Network traffic prediction is important to handle any mishaps proactively. Network analysis in machine learning is done by using Time Series Forecasting (TSF). By using a regression model solution, TSF finds a correlation between the traffic volume in the future and the traffic previously observed.
Traffic classification ensures Quality of Service (QoS), planning ahead for capacity, security, performance analysis, etc. It helps with proper resource utilization by pinpointing unnecessary traffic in a critical application.
Factors like cost-effectiveness, link utilization, operational capabilities, and policies are also considered by the ML model.
Congestion Control
ML models control the number of packets that enter a network to ensure that the network is stable, fairly utilize resources, and follow queue management employed for congestion control.
Efficiently Managing Resources
ML efficiently manages network resources like the CPU, frequency, switches, memory, routers, etc., by using analytical decision-making.
ML Learning Curve
ML models learn in the following ways:
Pitfalls
Like any other technology, machine learning application in networking comes with pitfalls and limitations. Here are a few:
Data Quality
The efficiency of an ML model is based on the quality, quantity, and diversity of data it processes so it can deduce patterns or identify root causes. Most ML models use simplistic synthetic data for training, validation, and performance. The same cannot be said about practical settings because the data comes from different applications and services and is more complex.
Feasibility
There are scalability and feasibility issues because each network and application is different. Moreover, there are no set standards for uniformity for implementation which makes it hard to set benchmarks or best practices. Control over autonomic networks is distributed and remains limited based on the vendor’s specific devices.
Predictive Analysis and Its Cost
Network analysis and machine learning prediction require additional accurate and effective monitoring investments. Moreover, fault management may have some potholes as there may be a scarcity of normal fault data.
High FPR (False Positive Rates)
Anomaly detection by ML in networking has not created enough buzz in the industry because it generates high FPRs during operations. Also, no detailed anomaly report is generated, so no anomaly history log can be maintained.
Striking a Balance
ML requires time to learn and mitigate issues. It is difficult to identify, in advance, how complex the ML’s approach will be. Striking a balance between the performance and computational cost is difficult. Deciphering comprehensive evaluation metrics is also a tedious task.
No Theoretical Model
There is no theoretical model, in turn, a unified theory, for ML in networking, so each network may have to be learned separately. The current machine learning applications in networking are made keeping in mind certain applications. Over time, more research to tailor ML for certain networks needs to be done. Cross-domain experts who understand both ML and networking are also rare.
Solutions
Software Defined Networking (SDN)
CISCO helped PwC Italy set up a secure network at their new twenty-eight-floor tower with the help of their SD-Access product. PwC wanted a secure, robust network with increased Wi-Fi and wired connectivity for their 3000 employees by streamlining network operations.
“We needed a robust and highly reliable wireless network infrastructure that’s as advanced as the tower itself.”
-Simone Demaria,Network Architect and Infrastructure Manager at PwC Italy
By applying Software Defined Network (SDN), IT personnel can remotely govern network policies in real-time through open interfaces, so traffic engineering is easily possible. SDN also contributes to network virtualization.
SDN supports the upcoming 5G ecosystem. When combined with NFV and VNF, SDN can revolutionize networking.
Going Beyond Traffic Volume & Prediction
To tackle the limitations that TSF-based traffic prediction models have, leveraging features beyond traffic prediction and concentrating on traffic interpolation and sampling could be viable. Research is ongoing on this possibility.
Summing It Up
As the influx of data keeps on increasing, the complexity of networks will increase in tandem. For successfully implementing ML for streamlining networking, the ML approaches we are aware of today need to be upgraded to accommodate multi-layer networks and multi-tenancy so autonomic networking can be a reality.
FAQs
How Can ML Help in Making Networking Smarter?
ML can streamline the network by automation, threat detection, and improving its performance.
How Complex Is Integrating ML into Networking?
The complexity depends on the type of network you are integrating it into.
What to Keep in Mind Before Using Ml in Networking?
Consider investment costs, data availability, feasibility, and scalability.
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Article | June 24, 2021
The next phase of our newly redesigned Tableau Partner Network is officially here. Originally announced during the Global Partner Summit at Tableau Conference 2019, and launched in September 2020, we built the Tableau Partner Network (TPN) to enable our global ecosystem to meet evolving customer needs and deliver exceptional customer experiences. The Tableau Partner Network is an analytics-focused ecosystem that complements Salesforce’s partner ecosystem.
With this latest phase, we’ve unlocked new partner branding to showcase our partners’ commitment and expertise. Customers now have a more transparent view of the commitment and quality level of Tableau’s partners by business model track (Reseller, Services, and Technology) and performance level (Premier, Select, and Member), as well as by country groupings versus a single global qualification. These changes make it easier for customers to find and confidently work with the right Tableau partner, knowing they meet Tableau’s standards and are local if desired.
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