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Event correlation encompasses a variety of technologies and operations. Architecturally correlation engine can be conceptualized as a set of pipes each performing particular operation. The key idea here is not some kind or "super-human" detective capability but a more modest noise reduction approach. It's also useful for implementing some types of automation when a certain sequence of events trigger response in a form of scripts like in file system threshold reached -- cleanup script launched.
Transformation (sometimes called event enrichment) is one-to-one modification of event's attributes. For example event severity, event message or other attributes can be modified.
One common type of transformation is called escalation.
Sometimes multiple events each of which has low priority reflect a worsening error condition for a system or a resource. For example the initial report about disk partition utilization above 80% can be "file system is almost full and need to be cleaned or extended". If we encounter second event reporting higher usage, more severe event is generates each time usage increases by certain threshold.
This can be combined with de-duplication (see below): the event processor does not need to report the event about particular file system event multiple times. It can merely increase the severity of the initial event to indicate that the problem has become more critical and needs to be responded to more quickly
Filtering of events is close to spam filtering and can be done with regular expression engines (see procmail ) or any utility or scripting language that have built-in regular expression (preferable Perl-based regular expressions). Simple cases also correspond to selection operation in SQL.
Filtering can (and should) be implemented as pre-processing technology for the event stream to lessen the load of "main correlator" (Tivoli pioneered this approach with its State correlation engine located on gateway). It has several forms
One example of this technique is reporting only events that have priority lower by one or two units from the top priority present in the event queue. For example, if "critical" is the top priority event currently present in the queue, then only "serious" and "errors" are presented; "warning" and "info" events are suppressed in event viewer to diminish noise. In case of Fatal even is present only critical and serious events are displayed and lower level events are suppressed.
Displaying low priority events in case high
priority events present clutters an event viewer to the point where
operators shut off the feature and rely on their intuition and user
complaints. Therefore, it is important to eliminate all low-priority
events (for example, events that do not require any escalation or operator
This is the simplest example of compression but with a unique twist: we replace a specified number of similar events with one, but add or modify a field called counter which is incremented each time identical event arrives. In a way it is both compression and simple generalization.
Despite being very simple to implement it is very useful and should always be deployed on low-level correlation stages (pre-filtering) as it can significantly reduce the load on the main correlation engine.
For example 1,000 "SMTP message cannot be delivered" events become a single events that says "message routing failed 1,000 times." This for example can be due to spam attack or due to the problem of SMTP gateway but this generalized event is definitely more useful then individual events.
More complex variant of duplication removal can be called aggregation and we will discuss it in the next classification entry.
This common variant of deduplication is called throttling. In this case events are reported only after they occur a certain number of times or if event des not disappear after a certain interval ("calm down period") is called throttling. For example, if ping failed it is usually wise to wait a certain interval and repeat the ping before "crying wolf". In case of the calm down period the events reported if any new events that contradicts this one was reported does not arrive within specified period. For example if ping disappeared and does not reappear in 10 sec the lost connectivity can be reported.
In this case we create a new, more generic event from several "low level" dissimilar events (for similar events this operation is typically called deduplication).
For example port scanning event is typically result of generalization of probes on several ports that fit a certain time and/or host distribution pattern.
One of the possible approaches to aggregating messages is syntax based methods. Often composite event is called ticket and it extends dynamically incorporating new event that fall into the ticket mask (for example all events that are registered for a particular serviceable component). for example in case of networking event one typical aggregation point is device. So if two interfaces on the device fail all corresponding event are aggregated into the device ticket.
Generalization is more advanced version of aggregation and involves some additional information about the topology of the system, not just a sequence of events in the event window. For example if both events about HTTP and FTP connectivity failures are arrives then reasonable generalization would be connectivity/TCP_stack. Another example is that if several servers on the same network segment reports troubles it can be generalized to the router that serves this segment (for example site router).
This is an important form of filtering which has a considerable practical value and often is discussed outside of filtering. If the new, arriving event finds out that an event which is a related to current event is present in the event queue, it can remove it (for example, "server is up" event should close all "server is down" events, connectivity restored should close all collectivity lost events and so on.
This method can be helpful if one event is always followed by several others or if sequence of events suggest particular scenario. There is special discipline called temporal logic that helps thinking about such sequences using special diagrams. Time-linking is often combined with suppression: for example any event during maintenance window can be assigned very low priority or completely filtered out.
Typical examples of time-based relationships can include the following:
See also Interval Temporal Logic
This is important case of generalization and is typically used for networking events. For correct correlation of networking events it is important to understand the topology of the network Topology-based correlation presuppose existence of some kind of network diagram from which one can infer how two devices are connected.
For example, topology-based correlation permit to suppress the events which occur when elements downstream from a known problem are unreachable). One of first implementation was found in IBM Netview:
Router fault isolation
Router fault isolation is a root-cause analysis feature built in to Tivoli NetView. In the event of a serious network problem, router fault isolation can immediately focus on the failing device and mark affected network regions as unreachable. In response, Tivoli NetView can reduce networking polling of the affected networks, potentially reducing overall event traffic. Without a router fault isolation feature, customizing this function could be expensive and time-consuming. This feature can help simplify root-cause analysis of network failures.
The most basic form of topology-based correlation involves suppressing messages that are side effects of failures on the higher level of network topology.
For example, if a router experience problems, then the stream of alerts from downstream devices can be partially or completely filtered out for the period problem on the router exists.
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