Cloud is emerging as a key Industry 4.0 technology spurring digital transformation of various industries along with IoT, AI, and so on. Businesses opt for private, public, hybrid, and multi-cloud environments to seamlessly deliver services while keeping their data secure and private.
One of the biggest advantages of moving to the cloud is the lower cost of ownership and maintenance. But, is it indeed delivering as expected? Is the cloud infrastructure delivering optimized performance and effective? What are the areas of improvement?
Cloud operational metrics help get answers to questions like these and more on the performance and effectiveness of the cloud. It ensures transforming cloud operations by addressing gaps and providing value for money by measuring the key performance indicators.
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● Optimizing billing and cloud cost
● Ensuring compliance and security management
● Troubleshooting problems the moment they arise before the entire infrastructure is affected
● Managing service level agreements (SLAs)
● Managing application performance
● Properly allocate the cloud resources
Benefits of Operational Metrics
Typically, data logs generated by the cloud infrastructure or application are reviewed, monitored, and managed for detecting and responding to modifications in costs, security, and performance of the cloud environment a business is using. It helps with:
Cloud Monitoring for Performance Improvement
Some of the key aspects that need to be monitored in the cloud engineering services infrastructure include:
Cybersecurity: Although the cloud service provider does put securities in place, enterprises must ensure that their services and apps are well-protected. Cloud monitoring services help detect security gaps and breaches and ensure a secure network.
Error Detection: Cloud monitoring services can ensure performance standards by identifying errors that need to be fixed. It ensures higher uptime and availability to improve customer satisfaction.
Agility: By measuring cloud speed, cloud monitoring services provide the data needed to troubleshoot and accelerate product delivery. Businesses can optimize their website and applications to improve the speed of service delivery.
Risk Remediation: Cloud monitoring enables proactive risk remediation through the simplified implementation of continuity plans.
Reduction in Cost Leaks: Transparent cloud architecture can help reduce cost leaks due to hidden factors
Utilization of CPU and Memory: Measuring the percentage of computing units and memory will ensure optimum performance and correct utilization of resources.
Latency: The gap between the time between a customer sending a request and receiving a response may be high and can lower productivity. It is important to reduce the latency and cloud monitoring helps to detect this issue.
Failure and Repair: The Mean Time Between Failure (MTBF) and the Mean Time to Repair help anticipate failures of components and be prepared with measures to correct them proactively. This improves service delivery and availability, leading to better customer satisfaction.
● A NodeID dimension refers to the performance data for the cluster nodes and covers leader and compute nodes. CPUUtilization, ReadIOPS, and WriteIOPS are examples of such a dimension.
● ClusterIdentifier dimension such as HealthStatus and MaintenanceMode provides performance data for clusters.
Amazon Redshift Metrics to Derive Insights
Amazon Redshift users can benefit from CloudWatch metrics that provide insights into the cluster’s health and performance at the node level. Each metric is associated with many dimensions, each about a different aspect:
CloudWatch Logs provide insights from quick queries and log data collected and stored from vendor services, specific AWS services such as AWS Lambda, AWS CloudTrail, Amazon Simple Notification Service, Amazon API Gateway, or on-premises resources and proprietary applications.
Metrics from more than 70 distributed AWS applications and insights from containerized applications are also pulled into one unified view. Alarms for various issues caused by an application are consolidated into a single notification and help with root-cause analysis. The thresholds for different metrics can be customized based on their criticality. CloudWatch also helps with identifying patterns that help with the proactive resolution of issues.
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Apart from the CloudWatch metrics, Amazon Redshift’s query and load performance data, available in the Amazon Redshift console, help analyze the relationship between database performance and cluster metrics. For instance, a spike in a cluster’s CPU can be plotted in a cluster CPU graph to identify the queries that had been running at the time. Or, the review of a specific query can provide insight into how it impacted cluster metrics.
While the query and load performance data are generated when the database’s system tables are queried, customized database performance queries can also be generated.
To derive the maximum benefits from Amazon Redshift operational metrics, prioritize the activities that need to be monitored and determine which metrics impact your bottom line the most.
Indium Approach to Metrics and Insights
Indium Software is an AWS partner that offers the entire range of AWS services from database and application migration and modernization to data and analytics, DevOps and cloud performance optimization
Our team of AWS experts with experience and expertise can help enterprises leverage their cloud investments for optimal performance by setting through cloud monitoring and tracking. We work closely with our customers to understand their business goals and align metrics that can help them achieve their objectives and grow from strength to strength. By monitoring, identifying gaps, and implementing corrective actions, businesses can improve RoI from cloud investments, accelerate growth, improve customer experience, and break barriers to innovation.