FRAMINGHAM, 13 JANUARY 2009 - There's a growing desire among IT professionals to manage their diverse technologies according to the services they deliver, to have a predictive view of those services and a cross-silo view of the components that comprise them. This is driven by the need to prevent problems before they affect the business, quickly pinpoint root cause, put an end to the finger-pointing between the IT teams responsible for each silo, and speed mean time to repair.
Best-practice management tools can provide a "service model," a software representation of all the IT elements that are required to deliver a service. Besides visualization, the service model should have monitoring hooks into actual elements, an understanding of the relationships among those elements, policy-based thresholding for proactive warnings/alarms, event correlation, root cause analysis and alarm (symptom) suppression.
How you define an IT service depends on personal preference. An IT service may be CRM, an online shopping cart, express mail tracking, insurance benefits selection or e-mail -- essentially what the users see or experience. In all cases, the service model is comprised of a particular cross-silo collection of IT components: network devices, physical and virtual systems, databases and applications. Each component's health is monitored and its status is reflected in the service model to show its current or projected impact on the service.
IT organizations are eyeing IT service modeling because it enables them to manage technologies in the context of business impact and to prioritize corrective actions according to business priority. Because IT service modeling is a grouping of IT components, it requires the sharing of data across siloed management tools, teams and processes.
Integrated network and application management is essential. To show all IT service dependencies, IT modeling integrates infrastructure management for all silos (networks, systems and databases) with application management. In real time for each IT service the model shows the current status, trends and root cause based on: 1) real-time measurement of user experience; 2) a comparison of the time each business transaction spends in each silo as it traverses the infrastructure; and 3) health analysis of each infrastructure component and of the application itself.
User experience measurement indicates service-level agreement (SLA) compliance. End-to-end monitoring shows where (that is, in which silo) a business transaction's performance degrades or outright fails. Health analysis of each silo reveals how the underlying infrastructure may be negatively impacting the business transactions. Health analysis of the application reveals, for example, how the J2EE or .Net software environment itself may be negatively impacting the business transactions.
Infrastructure monitoring may show uptime and performance within accepted tolerances (operating level agreements), yet some applications may be violating SLAs due to poor software configuration or constrained database server resources. Hardware server utilization may be within prescribed limits, yet certain applications may be hogging server resources enough to throw others above their prescribed response times. Infrastructure may be blamed for poor performance, yet the application software itself may be poorly written.
Without holistic service modeling, there's no way to correlate all factors. Moreover, all service components may be within accepted tolerances right now, but the IT service may be at risk because performance is degrading and heading toward a fiasco. Service modeling that includes a proactive, predictive view is required.
Proactive performance management means correcting degradations before they noticeably impact user experience or automated business processes. Instrumentation must be in place to monitor infrastructure performance indicators, business transactions and SLA compliance; policies must be in place to trigger early warnings of degradation trends before they slow transactions or result in a service-crippling outage.
Key performance indicators (KPI) for infrastructure -- there are hundreds to choose from -- are measured in terms of time over threshold and deviation from normal.
Time-over-threshold rules should be implemented in a way that warns of negative KPI trends. That means indicating persistent conditions and not every transient spike or drop. So rather than sending an alarm every time a switch port reaches 100% utilization and discards packets, the threshold rules should trigger an alarm only if the port discards packets for a cumulative period of, say, 20 minutes, within an hour-long reporting window.
Deviation from normal rules combined with time-over-threshold rules trigger early warnings when KPIs persistently deviate from the business cycle's historical behavior. Higher than normal traffic on a router interface may indicate a runaway program taxing a remote server. Higher than normal CPU utilization on a server or excessive database check-pointing informs operators of an impending degradation.
In the application environment, depletion of threads and pooled objects, memory leaks, Java Database Connectivity driver-database version mismatch and bad coding can have significant effects on an IT service. To manage this, baselining and heuristic trending must be applied to enable predictive alarming.
By monitoring 100 per cent of the business transactions that traverse a network, IT gets a real understanding of user experience and transaction success (completion). Watching response trends to warn of impending SLA violations helps IT be proactive.
When user response is above its prescribed 400-millisecond SLA, how does IT most efficiently pinpoint the cause? Cross-silo IT service modeling is the best practice. First, it gives IT visibility of the infrastructure components and application elements that comprise the service. Second, it provides predictability by letting IT see the performance trends of the infrastructure and application elements that will affect the overall IT service in the future. Third, it helps IT identify root causes by correlating events that have affected service uptime or performance.
Network operation center personnel are in a good position to take advantage of this holistic view. All business transactions flow back and forth across the network -- connecting users to application servers, servers to back-end systems, databases and Web services. Learning IT service availability management best practices that combine service modeling across all silos with proactive performance monitoring puts them in the cat-bird seat.
Hayward is a senior principal product marketing manager with CA (www.ca.com).
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