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SolarWinds is a major software company based in Tulsa, Okla., which provides system management tools for network and infrastructure monitoring, and other technical services to hundreds of thousands of organizations around the world. Among the company's products is an IT performance monitoring system called Orion.
As an IT monitoring system, SolarWinds Orion has privileged access to IT systems to obtain log and system performance data. It is that privileged position and its wide deployment that made SolarWinds a lucrative and attractive target.
SolarWinds® NetFlow Traffic Analyzer (NTA) is a multi-purpose tool useful for a wide range of bandwidth monitoring and management purposes. It integrates with SolarWinds Network Performance Monitor (NPM) to provide a comprehensive network monitoring tool.
Essentially, NTA collects two kinds of data: interface-level flow data and CBQoS (Class Based Quality of Service) data. It then looks at the performance data NPM collects from SNMP and WMI sources and combines the two data streams to produce one set of data you can analyze and examine. This data can be processed into graphs and reports, so you can have complete visibility into your network, in both a historical sense and in terms of its current status.
In addition, the combination of NPM and NTA yields a selection of tools for multi-vendor networks. This includes network traffic and performance analysis, bandwidth monitoring, customizable reports, and a range of alerts and alerting options, as well as the ability to optimize the policies you apply to your networks and infrastructure.
NTA can also perform network traffic analysis. By collecting large amounts of data on your bandwidth flows, it can easily baseline performance, and as a result help you troubleshoot issues and discover the root cause as quickly as possible. It provides your performance data in a customizable single-pane-of-glass dashboard.
When dealing with management, you likely need to furnish reports showing your IT team is optimizing network performance, fixing issues, and keeping bandwidth flows constant. You can use the reports provided by NTA for optimizing and managing bandwidth, including determining your actual needs when compared to your projected or expected ones.
One of the first things you should do is configure NPM and NTA to collect as much data as possible, to identify clear baselines for your network performance. Below are the key best practices for using these tools.
All these tools help you gain greater insight into how your network is being used and how it functions, so you can optimize performance and troubleshoot issues as they arise. They can be used alongside NTA and NPM to complement their functionality.
WhatsUp Gold provides the same kind of functionality as NPM used in tandem with the NTA module. It includes some useful core features, including tools to check network device availability and track bandwidth usage, and several automation tools to help you configure network setups more easily, manage assets, and monitor logs and events.
ManageEngine OpManager is a powerful network monitoring software that provides deep visibility into the performance of your routers, switches, firewalls, load balancers, wireless LAN controllers, servers, VMs, printers, and storage devices. It is an easy-to-use and affordable network monitoring solution that allows you to drill down to the root cause of an issue and eliminate it.
Most IT shops use multiple network monitoring tools to monitor and manage network operations. However, in case of any fault, they have to sift through multiple tools, GUI, graphs, and reports to get to the bottom of the issue. ManageEngine OpManager, the integrated network management software, provides real-time network monitoring and offers detailed insights

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Classical deep neural networks are limited in their ability to learn from emerging streams of training data. When trained sequentially on new or evolving tasks, their performance degrades sharply, making them inappropriate in real-world use cases. Existing methods tackle it by either storing old data samples or only updating a parameter set of deep neural networks, which, however, demands a large memory budget or spoils the flexibility of models to learn the incremented task distribution. In this paper, we shed light on an on-call transfer set to provide past experiences whenever a new task arises in the data stream. In particular, we propose a Cost-Free Incremental Learning (CF-IL) not only to replay past experiences the model has learned but also to perform this in a cost free manner. Towards this end, we introduced a memory recovery paradigm in which we query the network to synthesize past exemplars whenever a new task emerges. Thus, our method needs no extra memory for data buffering or network growing, besides calls the proposed memory recovery paradigm to provide past exemplars, named a transfer set in order to mitigate catastrophically forgetting the former tasks in the Incremental Learning (IL) setup. Moreover, in contrast with recently proposed methods, the suggested paradigm does not desire a parallel architecture since it only relies on the learner network. Compared to the state-of-the-art data techniques without buffering past data samples, CF-IL demonstrates significantly better performance on the well-known datasets whether a task oracle is available in test time (Task-IL) or not (Class-IL).
Local divisive normalization provides a phenomenological description of many nonlinear response properties of neurons across visual cortical areas. To gain insight into the utility of this operation, we studied the effects on AlexNet of a local divisive normalization between features, with learned parameters. Developing features were arranged in a line topology, with the influence between features determined by an exponential function of the distance between them. We compared an AlexNet model with no normalization or with canonical normalizations (Batch, Group, Layer) to the same models with divisive normalization added (before the canonical normalization, when those were used). The normalization was performed after the RELU in all five convolutional layers. Divisive normalization always improved performance for models with batch or group or no normalization, gen- erally by 1-2 percentage points, on both the CIFAR-100 and ImageNet databases. Divisive followed by batch normalization showed best performance. To gain in- sight into mechanisms underlying the improved performance, we examined several aspects of network representations. In the early layers both canonical and divisive normalizations reduced manifold capacities and increased average dimension of the individual categorical manifolds. In later layers the capacity was higher and manifold dimension lower for models roughly in order of their performance im- provement. We also use the Gini index, a measure of the inequality of a distribution, as a metric for sparsity of the distribution of activities within a given layer. Divisive normalization layers increase the Gini index (i.e. increase sparsity), whereas the other normalizations decrease the Gini index in their respective layers. Nonetheless, in the final layer, the sparseness of activity increases in the order of no normal- ization, divisive, combined, and canonical. We also investigate how the receptive fields (RFs) in the first convolutional layer (where RFs are most interpretable)

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