Network Configuration and Optimization
Preprocessing and optimization modules are used to generate an optimized configuration description. Preprocessing receives input data such as parameters related to network nodes, equipment, transmission modes, geography, and costs. This data may relate to equipment or nodes that are currently available or are planned to be built. Optimization generates a network configuration description based on the input data. In one example, a plant growth simulation algorithm is used to generate an optimal configuration description.
Using Gigamon Hawk for network configuration and optimize ing gives network administrators powerful insights. They can display data from multiple locations, endpoints, and the actual network. They can also provide individual, detailed dashboards, like those for network traffic. Gigamon Hawk is compatible with New Relic and supports JSON ingestion, which allows you to analyze your traffic from a holistic view.
Gigamon Hawk is built on a flexible visibility fabric with intelligent packet brokering for a hybrid cloud. Its visibility-as-code technology minimizes manual intervention while providing contextualized network information. Gigamon Hawk is a Nutanix Ready solution that delivers real-time visibility for hybrid cloud deployments. To learn more, visit the Gigamon Community and join the networking group.
Gigamon Hawk enables comprehensive visibility across cloud environments, from raw packets to application layer data. It helps you better manage hybrid IT infrastructures and reduce the operation overhead associated with managing hybrid networks. Gigamon Hawk also enables you to use observability platforms, such as New Relic's Observable Network (ONI) platform, to monitor and manage the performance of your network.
Eaton's Network Configuration Optimization module
Using Eaton's Network Configuration Optimization module in CYME power system analysis software will provide you with optimal switching plans that will reduce energy costs and increase network capacity. This module can be configured to use the most effective switching configurations, including dynamic network topologies. It also provides recommendations on the location of new tie points and capacitor placement. The main administrator user will retain his or her default login and password.
One of the key features of SolarWinds' network configuration and optimization management software is its ability to handle large networks easily. The software includes many features for troubleshooting large networks and has the ability to deploy and configure complex networks easily. Other features include NetPath, PerfStack, Network Insights, and Orion (r) Scalability Engines. Users can also set dependencies between events and run external programs when an alert occurs.
As part of its network monitoring capabilities, the SolarWinds Network Performance Monitor license includes hardware health monitoring. It will send alerts and reports to IT administrators based on key hardware metrics. It will also identify when it's time to replace network hardware. This network monitoring software is a multi-vendor network management solution that can scale to any vertical. With SolarWinds NPM, administrators can ensure their networks are performing optimally and prevent downtime.
Plant growth simulation algorithm
A plant growth simulation algorithm (PGSA) is a mathematical model that simulates the growth of a plant in a network. PGSA was originally designed to simulate a realistic environment and has recently been improved by implementing three new strategies. These strategies are the elitist strategy for morphactin concentration calculation, intelligent variable step size, and initial growth point selection based on the GA. The algorithms have been tested on typical trusses and single-layer lattice shells.
A refined approach has been developed for distribution network reconfiguration. This method incorporates a heuristic exchange rule from branch exchange algorithms with an algorithm for searching growth points. The proposed approach reduces the number of feasible sets of growth points in each iterative step. Moreover, the method reduces the computation time required to evaluate the objective function and calculate morphactin concentration. It has also been tested on distribution system planning.
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