AI Monitoring for Boomi: Enhancing Performance and Efficiency.
Experience a new level of efficiency and performance with AI monitoring for Boomi. Designed specifically for IT professionals, system administrators, business owners, and data analysts, this powerful tool revolutionizes the way you monitor and manage your Boomi integrations.
Eyer possess extensive knowledge about the integration market, solution and use cases, and this has been leveraged to build a complete anomaly detection package for the Boomi runtime (Atoms & Molecules)
The Eyer platform is an automated time-series observability platform that easily integrates and supports different technologies through APIs.
Eyer will integrate directly in Boomi processes, giving the user the power to act on early warnings relating to Atom & Molecule performance like throughput, latency and other metrics related to runtime processing. Some use cases are listed in chapter 3, with examples of data to act on from the anomaly alerts and how to utilise the data in a Boomi process.
Benefits of AI Monitoring for Boomi:
Experience the power of AI monitoring for Boomi and take control of your integrations like never before. Maximize performance, minimize downtime, and streamline your operations. Discover a new level of efficiency today.
Proactive Problem Detection: Identify and resolve issues before they impact your operations. Stay one step ahead with proactive monitoring.
Minimize Downtime: Swiftly address any disruptions to your integrations. Minimize downtime and keep your business running smoothly.
Streamline Operations: Optimize your workflows and streamline your operations with intelligent monitoring and analytics. Maximize efficiency and productivity.
Enhance Decision-Making: Leverage predictive analytics to gain valuable insights. Make data-driven decisions and drive better outcomes for your business.
Save Time and Effort: Automate monitoring alerts and notifications. Spend less time on manual tasks and more time on strategic initiatives.
Data to act on:
Anomaly Alerts: Receive instant notifications when anomalies are detected in your integrations, down to metrics and components. Take proactive measures to prevent disruptions.
Root Cause Analysis: Quickly identify the root cause of any issues and resolve them efficiently. Minimize downtime and keep your operations running smoothly.
TBA! Predictive Analytics: Harness the power of predictive analytics to anticipate potential bottlenecks or performance issues. Make informed decisions and optimize your workflows.
Automated Alerts: Save time and effort with automated alerts. Receive notifications directly to your preferred channels, ensuring you never miss critical updates.
Easy Integration: Seamlessly integrate AI monitoring for Boomi into your existing systems. Get up and running quickly without any disruption to your workflow.
Boomi Recipes: Leverage a library of pre-built recipes to accelerate your integration projects. Benefit from best practices and optimize your implementation.
Eyer can monitor and process runtime information from the Atoms and Molecules. To enable JMX data for Eyer, we use Influx Telegraf in combination with Jolokia, to expose the runtime performance data to Eyer. The data we fetch from the runtime are the following (example, internal JSON format in Eyer):
[{
system: "DESKTOP-S01F7CP",
nodes :{
nodetype: "operatingsystem",
data:
{
cpu_usage_system: 1.4797507788161994,
cpu_usage_user: 31.386292834890966
TotalSystemMemUsed: 5312753664,
AtomCommittedVirtualMemorySize: 327794688,
HeapMemoryUsage.committed: 134217728,
HeapMemoryUsage.init: 134217728,
HeapMemoryUsage.max: 536870912,
HeapMemoryUsage.used: 78256432,
AtomProcessCpuLoad: 0.0028079687560744176,
TotalPhysicalMemorySize: 8502923264,
timestamp: 1697127400
},
nodetype: "ExecutionManager",
data:
{
AverageExecutionQueueTime: 0,
AverageExecutionTime: 0,
LocalRunningWorkersCount: 0,
MaxQueuedExecutions: 0,
QueuedExecutionCount: 0,
QueuedExecutionEstimatedCount: 0,
QueuedExecutionTimeout: 0,
RunningExecutionCount: 0,
RunningExecutionEstimatedCount: 0,
timestamp: 1697127400
},
nodetype: "ResourceManager",
data:
{
AtomInBadState: false,
DeadlockDetected: false,
LowMemory: false,
OutOfMemory: false,
TooManyOpenFiles: false,
timestamp: 1697127400
},
nodetype: "Scheduler",
data:
{
ExecutingSchedulesCount: 0,
MissedSchedulesCount: 0,
ScheduleCount: 7,
timestamp: 1697127400
},
nodetype: "ProcessSummaryReportingService",
data:
{
PendingExecutionCount: 0,
PendingReportCount: 0,
PendingResultCount: 0,
timestamp: 1697127400
},
nodetype: "MessageQueueFactory",
data:
{
PendingMessageCount: 0,
timestamp: 1697127400
},
nodetype: "config",
data:
{
Restarting: false,
Status: "RUNNING",
timestamp: 1697127400
},
nodetype: "QueueAcknowledgement-track",
data:
{
PendingStoreMessageCount: 0,
PendingUploadMessageCount: 0,
timestamp: 1697127400
},
nodetype: "MessagePollerThread",
data:
{
connectFailureCount: 2,
deliveredMessageCount: 0,
timestamp: 1697127400
}
}
}] |
The Eyer Boomi connector allows the user to interface the anomaly & correlation engine, within a Boomi process. Time-series data from the Atoms & Molecules described in chapter 3 will be used to detect anomalies and correlations. The anomaly alerts with detailed information can then be used in Boomi processes to automatically take action and for decision making. Some use cases based on anomaly detection are listed below in section 5.
Eyer will provide Boomi recipes to get users quickly up and running.
An alert query done by the Eyer Boomi connector (see chapter 4) should always return new / updated / closed anomaly alerts since the last query (query time window). Recommended interval between queries is x minutes.
Example
Query 1 at time 0 returns:
1 new alert, id 1
1 new alert, id 2
Query 2 at time 1 returns:
1 closed alert, id 1
1 updated alert (increase nodes affected), id 2
1 new alert, id 3
Timestamp from previous query should be buffered and used in the next query from Boomi
Current query timestamp (mandatory)
Previous query timestamp (optional)
Status (if blank = all statuses, or new/updated/closed) -optional
Criticality (lower lever, criticality >=x) - optional
Boomi will iterate over each anomaly in the return message (query response), and check if the iteration should trigger custom processing in Boomi (branching, decision etc).
In the cases below, the “input control parameters” section contains what fields from the anomaly alerts response that will be used for validation / actions in Boomi (fields are selected by Boomi from the query response)
In Boomi, there will be some fields from the anomaly alert that should be stored as variables (state, nodes involved, metrics etc), to ensure further correct processing depending on the anomaly alert state.
General logging / notification if alert exceeds a certain threshold on criticality. All alert updates with criticality > x are also logged. Reports new anomaly.
Logs: whole alert with all fields
Input control parameters:
Id
Alert criticality
status = new
Timestamp
General logging / notification if alert exceeds a certain threshold on criticality. All alert updates with criticality > x are also logged. Reports updated anomaly.
Logs: whole alert with all fields
Input control parameters:
Id
criticality
status = updated
Timestamp
Last updated timestamp
General logging / notification if alert that was previously acted on, is closed.
Logs: alert closed.
Input control parameters:
Id
status = closed
Timestamp
Last updated timestamp
General logging and notification if alert contains a specific set of system(s) and node(s), and criticality > z. Logs: whole alert with all fields.
Input control parameters:
Id
Alert criticality
status = new
Timestamp
Systems affected
Nodes affected
General logging and notification if alert contains a specific set of system(s) and node(s), and criticality > z. Logs: whole alert with all fields.
Input control parameters:
Id
Alert criticality
status = updated
Timestamp
Last updated timestamp
Systems affected
Nodes affected
General logging and notification if previous alert contained a specific set of system(s) and node(s), and criticality > z.
Logs: alert closed.
Input control parameters:
Id
status = closed
Timestamp
Last updated timestamp
General logging and notification if alert contains a specific set of system(s) and node(s), and criticality > z. Logs: whole alert with all fields.
Input control parameters:
Id
Alert criticality
status = closed
Timestamp
Last updated timestamp
(Systems affected)
(Nodes affected)
Nodes criticality
General logging and notification if alert contains a specific set of system(s) and node(s), and criticality > z. Logs: whole alert with all fields.
Input control parameters:
Id
Alert criticality
status = closed
Timestamp
Last updated timestamp
(Systems affected)
(Nodes affected)
Nodes criticality
General logging and notification if alert previously contained a specific set of system(s) and node(s), and criticality > z.
Logs: alert closed.
Input control parameters:
Id
Alert criticality
status = closed
Timestamp
Last updated timestamp
(Systems affected)
(Nodes affected)
Nodes criticality
Store Id as control token, to monitor for “updated” & “closed” status. Based on alert, take needed action (routing, decision , messaging).
Input control parameters:
Id
Systems involved
Nodes involved
Timestamp
Criticality
Status = new
Metric names with anomalies
Metric types with anomalies
Metric value at the time of anomaly per metric
Metrics baseline (upper/lower) on alert per metric involved
Store Id as control token, to monitor for “closed” status. Based on alert, take needed action (routing, decision , messaging).
Input control parameters:
Id
System name
Node name
Timestamp
Last updated timestamp
Criticality
Status = updated
Metric names with anomalies
Metric types with anomalies
Metric value at the time of anomaly per metric
Metrics baseline (upper/lower) on alert per metric involved
Reverts action if alert previously contained a specific Id with criteria above. Based on alert, revert needed action (routing, decision , messaging).
Input control parameters:
Id
Timestamp
Last updated timestamp
Status = closed
Store Id as control token, to monitor for “updated” & “closed” status. Based on alert, take needed action (config change, scaling, routing, decision , messaging).
Input control parameters:
Id
System name
Node name
Timestamp
Criticality
Status = new
Metric names with anomalies
Metric types with anomalies
Metric value at the time of anomaly per metric
Metrics baseline (upper/lower) on alert per metric involved
Store Id as control token, to monitor for “closed” status. Based on alert, take needed action (config change, scaling, routing, decision , messaging).
Input control parameters:
Id
System name
Node name
Timestamp
Last updated timestamp
Criticality
Status = updated
Metric names with anomalies
Metric types with anomalies
Metric value at the time of anomaly per metric
Metrics baseline (upper/lower) on alert per metric involved
Store Id as control token, to monitor for “updated” & “closed” status. Based on alert, take needed action (config change, scaling).
Input control parameters:
Id
System name
Node name
Timestamp
status = new
Metric names with anomalies
Metric types with anomalies
Metric value at the time of anomaly per metric
Metrics baseline (upper/lower) on alert per metric involved
Store Id as control token, to monitor for “closed” status. Based on alert, take needed action (config change, scaling).
Input control parameters:
Id
System name
Node name
Timestamp
Last updated timestamp
status = updated
Metric names with anomalies
Metric types with anomalies
Metric value at the time of anomaly per metric
Metrics baseline (upper/lower) on alert per metric involved
Store Id as control token, to monitor for “updated” & “closed” status. Based on alert, take needed action (config change, scaling).
Input control parameters:
Id
System name
Node name
Timestamp
status = new
Metric names with anomalies
Metric types with anomalies
Metric value at the time of anomaly per metric
Metrics baseline (upper/lower) on alert per metric involved
Store Id as control token, to monitor for “closed” status. Based on alert, take needed action (config change, scaling).
Input control parameters:
Id
System name
Node name
Timestamp
Last updated timestamp
status = updated
Metric names with anomalies
Metric types with anomalies
Measured value on anomaly alerts per metric
Metrics baseline (upper/lower) on alert per metric involved
A Boomi process “listens” for anomaly alerts with a specific set of criterias, then stores alert fields as variables to “remember” status for correct processing.
Input control parameters:
Id
System name
Node name
Timestamp
Last updated timestamp
status = updated
Metric names with anomalies
Metric types with anomalies
Measured value on anomaly alerts per metric
Metrics baseline (upper/lower) on alert per metric involved
A Boomi process “listens” for anomaly alerts with a specific set of criterias, then stores alert fields as variables to “remember” status for correct processing. Use variable to control a flow shape to initiate endpoint throttling
Input control parameters:
Id
System name
Node name
Timestamp
Last updated timestamp
status = updated
Metric names with anomalies
Metric types with anomalies
Measured value on anomaly alerts per metric
Metrics baseline (upper/lower) on alert per metric involved
A Boomi process “listens” for anomaly alerts with a specific set of criterias, then stores alert fields as variables to “remember” status for correct processing. Use variable to set connector properties / adjust rate limits in Plans (Contracts), or if properties can not be set send an alert to specified user(s) with details so manual restrictions can be set.
Input control parameters:
Id
System name
Node name
Timestamp
Last updated timestamp
status = updated
Metric names with anomalies
Metric types with anomalies
Measured value on anomaly alerts per metric
Metrics baseline (upper/lower) on alert per metric involved
Data fields returned from the Eyer anomaly query API
Anomaly Id
Status (new / update / closed)
Anomaly alert timestamp
Last updated timestamp (if existing)
Criticality / score
Systems involved
Nodes involved
Metrics involved
Metric value at time of alert
Baseline(s) and their values per metric at time of alert
Metric type
Number of total nodes in correlation group at current alert
Number of total nodes in correlation group at previous alert
Number of total systems in the correlation group at time of alert
Number of total systems in the correlation group at time of previous alert
Anomaly alert - a set of conditions are met (x number of deviations on y metrics, on z number of nodes in a correlation group), and an alert is created. Can then be read by the Eyer Boomi connector.
Deviation - a metric is deviating from any baseline on given time
Baseline - a “normal” behaviour on any given time for a metric, described by an upper / lower limit. Learned automatically from our ML algorithms.
Correlation group - a set of correlated nodes. Correlation is automatically performed across all nodes.
Criticality - low / medium / high / critical. Based on how how many nodes of total in correlation group that has active deviations
Score - criticality is deducted from this numeric (0-100). 100 means no deviations (no impact) on any metric on nodes in a correlation group, 0 means that all metrics on all nodes in a correlation group have deviations (serious impact). Score is calculated per node, and for the whole correlation group.
Id - unique anomaly alert identifier. Will be maintained throughout the “lifecycle” of an anomaly alert (new / updated / closed)
Timestamp - the timestamp of when an anomaly alert (initial & updates) was triggered.
Status - an anomaly alert has a lifecycle, from when it is initiated until it is closed. In between “new” and “closed”, an anomaly alert can be updated if conditions are changing (more deviations on nodes in correlation group are detected, thus bigger impact).
System - typically defined by a monitoring agent (windows server agent, Boomi. agent, SQL agent etc)
Node - a logical unit with metrics (a system is also a node). For instance in a Boomi monitoring agent, there are nodes like operating_system, execution_manager, resource_manager and more.
Metric - a numeric value (KPI, performance metric) connected to a node as a time-series. Can also represent boolean values (1/0)