Avoid outages and downtime and solve performance problems faster with AssureNow Performance Management Monitoring.
AssureNow Performance Management Monitoring allows you to capture device/application Key Performance Indicators (KPIs), combine multiple KPIs into Key Quality Indicators (KQIs), apply thresholds to both KPIs and KQIs for monitoring, and send alerts to the appropriate department for resolution. KQIs provide you with higher level, associative status information than is available from tracking an individual device’s KPIs.
AssureNow Pollers collect data directly from devices:
AssureNow network performance monitoring leverages its metric data store to identify, visualize and notify you of negative trends in the performance of your device/application/service to proactively prevent outages and manage capacity.
- using SNMP, WMI, ICMP, UDP, JMX, SOAP, etc. Flow collectors are used to collect and process NetFlow v5 & v9, S-Flow, J-Flow, NetStream, IPFix data.
- Transactional pollers are used to gather response time statistics for services (http, https, DNS, POP3, IMAP, etc. and applications.
- Collectors and Agents are used to gather data directly from element management systems or other tools.
- Performance indicators and statistics are polled at intervals ranging from 1 second to 30 minutes with real-time reporting and threshold-based alerts.
Complete end-to-end views of network availability, performance, and utilization can be displayed in real-time through a "single-pane-of-glass". Metrics can be monitored from any technology, system, or device type, with full multi-tenancy. Most network performance solutions can only support a single customer per instance when providing external access to real-time views. AssureNow’s multi-tenant capability helps reduce system/server sprawl using role based access control to share resources.
Big Data Processing and Storage
By grouping KPIs into higher-level KQIs and processing and correlating metrics, the number of data points is reduced dramatically and attention can be focused on service availability and quality agreements. Analyzing high-level KQIs encourages business-oriented results and keeps managers from getting lost in big data noise. Millions of active KPIs or metrics are gathered every second, and managing this large volume of data by separating the important information from the noise is essential. Sophisticated analysis, correlation, and consolidation engines turn complex analyses into simple graphs and reports and enable the correlation of large volumes of cross-domain KPIs into fewer KQIs that can be used to take automated action and reduce manual effort. To conserve performance data storage resources, AssureNow provides a user tunable consolidation facility to rotate the retention of daily, weekly, monthly, and yearly data for compliance reporting, historical analysis and capacity planning.
AssureNow's out-of-the-box automated alerting engines proactively detect issues by analyzing millions of active and historical KPI and KQI metrics per second to help you learn about problems before they become outages. This is done using four main types of thresholds:
1. Simple static thresholds notify operations (or third-party systems) when a violation has occurred and monitor for outages or utilization issues.
2. Missing data thresholds detect data integrity issues such as devices that are not sending data.
3. Trending and linear regression thresholds predict failures or detect capacity problems by determining whether a value is increasing or decreasing over time compared to a closest-fit line.
4. Abnormal thresholds use heuristics to analyze trends, such as latency shifts, so that imminent failures can be identified before service is impacted.
Troubleshooting and mean-time-to-repair (MTTR) can be reduced substantially using heuristics to analyze and convert historical data into insightful information. This can be done by researching problems to see if they have occurred before and/or if there are associated trouble tickets that contain resolution details that can be leveraged and inserted into the built-in knowledgebase.
Effective Capacity Management
With historical monitoring data in a single database, effective capacity management becomes possible and can be automated using the trend alerting engine, to notify stakeholders or customers proactively if more resources will be required.
Historical reports and dashboards simplify analysis and provide quick health snapshots. Reports on any collected data, regardless of source are easy to design, build, schedule, and share. Ad hoc reporting functionality compares single or multiple metrics on the same graph which is useful for visually comparing similar metrics such as comparing CPU utilization on multiple servers or bandwidth on multiple WAN links. It allows you to specify a custom start/stop time so you can have more/less data than is reflected in default 24 hour graphs.