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The PADS Framework, for Performance Analytics and Decision Support, represents a more holistic approach to adaptive, proactive and predictive operational data management and analysis. The framework links advanced performance management and big data analytics technologies to enable organizations to gain deep and real-time visibility into, and predictive intelligence from, increasingly complex virtualized and mobile systems across the entire application delivery chain.
The PADS Framework can help organizations achieve the three return on investment (ROI) objectives: 1) reducing costs; 2) enhancing productivity; and, 3) generating incremental revenues. It can also be used to secure valuable systems and data, thereby reducing operational risk while ensuring compliance with GRC (governance, regulatory, compliance) mandates.
As IT groups acquired discrete tools that focused on a particular hardware, network or software issue, many organizations have ended up with a patchwork quilt of point solutions that do not work well together. And while each tool might be indicating that performance of a particular segment or component is “normal”, outages persist and the actual user experience continues to disappoint.
Traditional performance monitoring solutions for application, network, infrastructure and business transactions have become overwhelmed by the scale of data required to comprehensively manage application performance. The proliferation of server virtualization and the tools needed to monitor and manage virtualized dynamic infrastructures and highly distributed application architectures only expands the data points and metrics that need to be analyzed.
Vital information is often overlooked, resulting in missed opportunities to uncover hidden patterns, relationships and dependencies. Additionally, whatever data is gathered is not normalized or time synchronized, making analysis and rapid problem resolution impossible. Yet pouring more data into obsolete analytics tools only compounds the problem.
A New Holistic Approach
Enterprises need to understand what levels of performance (i.e. speed and availability) are needed from their increasingly cloud-based and mobile applications in order to deliver fast, reliable and highly satisfying end-user experiences. To better understand the properties of the components and their place in the overall application delivery chain requires a higher-level assessment of the relationships to each other as well as to the wider system and environment.
The PADS Framework connects unified next-generation performance management and operational intelligence technologies into holistic, integrated platforms that consolidate multiple previously discrete functions. These platforms work in concert, as performance data analytics provides physical and logical knowledge of the computing environment to allow for more powerful and granular data queries, discovery and manipulation.
The PADS Framework
The twin missions of the framework are to:
A comprehensive performance analytics platform provides visibility across the entire application delivery chain – from behind the firewall and out to the Web, including third-party cloud providers. The “point of delivery”, which is where the user accesses a composite application, is the only perspective from which user experience should be addressed. As such, the most relevant metric for any IT organization is not about infrastructure utilization. Instead, it is at what point of utilization the user experience begins to degrade.
The performance analytics platform incorporates network, infrastructure, application and business transaction monitoring (NPM/IPM/APM/BTM), which feeds an advanced correlation and analytics engine. A single unified view of all components that support a service facilitates the management of service delivery and problem resolution.
Within the PADS Framework, users can then feed this information about the application delivery chain and user experience upstream into an operational intelligence (OI) platform. The OI platform can then integrate this data with other types of information to improve decision making throughout the organization.
An OI platform can not only ingest data from performance analytics platforms, but a far wider variety of machine and streaming data that are in semi-structured or unstructured formats. Consolidating this data to make it readily searchable can reveal previously undetected patterns or unique events. OI platforms provide a more unified view of events, which are often delivered from multiple streams as messages, to enable more efficient correlation and analysis.
The performance analytics platform includes real-time analysis of application and service performance across both physical and virtual environments by dynamically tracking, capturing and analyzing complex service delivery transactions across multi-domain IP networks.
Deep-dive analytics allows IT organizations to be more proactive by pinpointing the root cause of problems before users call the help desk and before a visitor departs a website. Correlation and analytics engines must include key performance indicators (KPIs) as guideposts to align with critical business processes. Capabilities should include data visualization to facilitate mapping resource and application dependencies and allow modeling of applications to detect patterns and predict points of failure.
Data mining that entails analysis of data to identify trends, patterns or relationships among the operational data can be used to build predictive models. Today, modeling is being facilitated by tools that automate iterative, labor-intensive processes. Newer technologies require little or no programming and can be implemented quickly with cloud-based solutions. Predictive models can now be developed by line of business users to improve a business function or process.
The key to success for the PADS Framework is providing correlation and analytics engines that feed into customizable dashboards. The ability to quickly visualize and interpret a problem or opportunity that results in actionable decisions is how to derive the most value from the platforms that underlie the framework.
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