Build a resilient automation fabric with RunMyJobs by Redwood, a powerful Service Orchestration and Automation Platform.
Achieve seamless automation with comprehensive design and runtime features.
Monitor your jobs and workflows at any time through centralized dashboards, reports and the RunMyJobs API.
Eliminate stalled workflows and manual intervention by building automations that self-recover and escalate to the appropriate teams.
Manage workflow schedules according to operational and service-level agreements with predictive SLA monitoring and controls.
Gartner has named Redwood as a Leader positioned furthest in Completeness of Vision in the newly released Magic Quadrant for Service Orchestration and Automation Platforms (SOAPs).
Click below to get your copy of the 2024 Gartner Magic Quadrant for SOAP report.
Rapidly get to the root cause of a performance issue or outage with reliable tracking and monitoring of your end-to-end automations and supporting systems via focused dashboards.
Eliminate uncertainty and wasted effort by configuring automations to run dynamically when the time is right.
RunMyJobs enables your team to set up an integrated framework of automation tools — a central nervous system to drive your mission-critical processes.
Deliver promised performance of critical business workflows and processes with service-level agreement monitoring.
RunMyJobs uses machine learning to monitor processes and forecast process execution time to predict potential SLA breaches against configured SLA rules.
Extract actionable insights and key metrics when you use RunMyJobs’ integrated report builder to generate ad-hoc or scheduled reports. Export comprehensive process execution history data to your BI systems for interactive analysis.
Monitoring and observability, while related, have distinct roles in IT operations. Monitoring involves tracking specific metrics and known conditions within a system. It relies on predefined alerts to notify teams about high CPU usage, latency or outages. Monitoring tools provide real-time data and are essential for quickly identifying and addressing performance issues.
On the other hand, observability is about understanding a system’s internal states based on its external outputs. It allows IT teams to gain insights into the "unknown unknowns" of complex systems. Observability platforms aggregate telemetry data, such as logs, traces and metrics, to provide a comprehensive view of system health and performance. This approach helps debug, troubleshoot and identify root causes of issues in distributed systems, microservices and cloud-native environments.
Learn about DevOps workflow automation and why continuous monitoring matters.
The three main types of monitoring are infrastructure monitoring, application performance monitoring (APM) and network monitoring. Infrastructure monitoring involves tracking the health and performance of physical and virtual IT infrastructure components, such as servers, storage and network devices. This helps identify hardware failures, resource bottlenecks and capacity issues within IT systems.
APM focuses on software application performance and availability. APM tools monitor response times, transaction throughput and user experience, providing developers and operations teams with actionable insights. This type of monitoring is crucial for detecting and troubleshooting performance issues in complex, distributed applications, including those deployed in cloud environments and using microservices or serverless architectures.
Network monitoring tracks the performance and availability of network components, including routers, switches and firewalls. It ensures that data flows efficiently across the network and identifies issues such as latency, packet loss and bandwidth utilization. Effective network monitoring is essential for maintaining the performance and reliability of both on-premises and multi-cloud infrastructures, supporting end-to-end IT operations.
Discover how to harness the power of extensibility in end-to-end automation solutions.
Proactive monitoring involves continuously observing and analyzing system performance to predict and prevent potential issues before they impact end-users. Unlike traditional monitoring, which typically reacts to incidents after they occur, proactive monitoring aims to identify real-time patterns and anomalies. Setting thresholds and leveraging advanced analytics, including AIOps, helps IT teams address issues early.
Proactive monitoring utilizes various tools and techniques, such as data collection instrumentation, metrics visualization and open-source platforms like OpenTelemetry. It often integrates with DevOps team practices, enabling operations teams and SREs to maintain system performance, reduce downtime and enhance the overall reliability of IT systems. This approach is particularly effective in complex environments, including Kubernetes and multi-cloud architectures.
Read Gartner’s predictions for workload automation and the role of scheduling, monitoring, visibility and alerting.
For effective workload automation, it’s essential to monitor several key aspects. These include job status, execution times and resource utilization. Monitoring job status helps track the progress and completion of automated tasks, ensuring that any failures or delays are promptly addressed. Execution times provide insights into performance trends, helping identify any bottlenecks or inefficiencies in the automation process.
Resource utilization is also crucial, as it involves monitoring CPU, memory and storage usage to ensure optimal performance and avoid overloading a system. Tracking dependencies between tasks and workflows is also important to prevent cascading failures and maintain the overall reliability of the automation system. By leveraging observability tools and monitoring systems, IT teams can gain comprehensive visibility into their workload automation environments and facilitate efficient and reliable operations.
Envisage the future of workload automation management.
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