WIND RIVER STUDIO: OPERATE

Optimization

Wind River Studio provides data and analysis to design and operate an optimized cloud platform.

Optimize your system through trend analysis.

Productized machine learning analyzes utilization trends and creates forecasts based on learned patterns, so you can identify operational risks and cost-saving opportunities.

Wind River Studio Optimization Solutions

Collection

Collect a broad and deep data set tailored to optimize your exacting processing and storage requirements. Data can be pulled across all layers of the system, including the infrastructure, cluster, services, and applications. Detailed memory, CPU, file system, and service state are gathered to ensure that the entire operational landscape is monitored.

Distribution

Subscribe only to the data targeted for machine learning analysis and visualization. Whether at the individual host level or aggregated with the local or distributed cluster, data is enriched to provide context beyond the metric and timestamp and includes tagging, transformation, and preprocessing.

Visualization of Platform Resources

Through a single pane of glass, visualize the overall health of the cloud platform implementation. Identify CPU and memory misallocations for optimization as well as a drill down to visualize hot spots, events, and anomalies.

Optimization of Cluster Resource Allocation

Align cluster and namespace resources and identify resource-driven operational risks.

Network and Storage Resource Optimization

Get visibility into resource utilization at the pod level. Plan network traffic capacity for low-latency applications. Identify file system hot spots for optimization.

Processing and Storage

Wind River® Studio includes replication indexing, which provides redundancy as robust as the system and makes the data faster for search. Lifecycle management ensures that the data is managed appropriately and persists only as it needs to.

Full-Stack Monitoring of Infrastructure and Services

Because Studio Analytics is open source components, fully integrated into Studio, it allows you to access a much larger, full-stack data set that you can use to keep your system optimized.

Wind River Studio Analytics

Analyze trends

Learn patterns, note and record abnormal behavior and create an anomaly event, forecast trends, and change behavior to adapt.

Visualize platform resources

Through a single pane of glass, visualize the overall health of the cloud platform implementation so you can always know the state of your network.

Optimize network and storage resources

Track and visualize resource utilization at the system level and drill down to the individual pod level for a more detailed view.

Wind River Studio Analytics Partner Ecosystem

Elasticsearch is a search engine based on the Lucene library. It provides a distributed, multi-tenant–capable full-text search engine with an HTTP web interface and schema-free JSON documents. 

Kibana is a data visualization and exploration tool used for log and time-series analytics, application monitoring, and operational intelligence use cases.

Apache Kafka is a distributed event store and stream-processing platform. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.

The World Is Evolving Rapidly with 5G

Service providers are expected to invest over $900B worldwide in 5G network infrastructure in the next five years. See this infographic to understand some of the key drivers and considerations.

See the Infographic »
Wind River Studio Optimization FAQs
Why should enterprises be concerned with cloud resource optimization?
Cloud resource optimization helps allocate the right resources to cloud workloads and applications. Unwarranted investments in infrastructure, applications, and resources leads to operational risks, unnecessary spend, and suboptimal business outcomes.
How does Studio Analytics help with cloud resource optimization?
Studio Analytics can collect detailed infrastructure and application resource utilization metrics to ensure that the entire operational landscape is monitored. Resource optimization recommendations can be enabled through visualizations, alerts, and anomalies resulting from comparison of historical and current operations data.
How does machine learning help with cloud resource optimization?
Cloud workloads and applications have unique and exacting infrastructure resource requirements, and they continue to evolve over time. Studio Analytics machine learning through continual analysis provides resource optimization recommendations.