Workload Forecasting: Addressing Database Congestion

This blog was coauthored by Alizen Prasla, Dolton John and Shu Hsien Lee.

Dell Technologies remains at the forefront of cloud solutions, catering to a wide range of customers, from small coffee shops up to the astronauts aboard the International Space Station. Whether it’s processing your coffee order or safeguarding launch codes for a manned mission to space, managing critical data requires optimal performance of Dell’s databases.

However, a critical issue databases face is workload congestion due to multiple high-load tasks processing during peak usage hours or processing simultaneously. This congestion is like a bustling city during rush hour, where every vehicle is trying to navigate the highways to get to their destination. Just as traffic congestion can slow down the movement of vehicles in a city, database congestion can slow down the processing of tasks and queries, making it challenging for databases to efficiently handle all the requests. To address this issue, databases need a way to predict future workloads in order to allocate the right infrastructure size and the required human resources to support these workloads.

Leveraging Data Science Algorithms for Workload Forecasting

In the world of databases, data is the name of the game. Leveraging data science and historical data from their databases, Dell engineers launched Workload Advisor, a tool that forecasts future workloads with high accuracy. The tool acts as a crystal ball, allowing engineers to determine whether they need to strengthen their CPU cores or increase memory size to handle the upcoming database demands.

With the ability to drill down into hourly, daily or monthly views, users can gain insights into usage patterns and identify specific timeframes when database demand is at its peak or at its lowest. Utilizing Workload Advisor can significantly reduce instances of over-allocated or under-utilized databases.

Release Planning and Load Balancing for Zero Downtime

For development teams, this tool means improved efficiency and reduced risk during code roll outs and system updates. Users can confidently plan deployment windows that align with workload patterns, minimizing the chances of deployment-related incidents or performance bottlenecks. Similarly, database administrators can use the tool to schedule maintenance jobs during off-peak hours, ensuring critical tasks do not interfere with the database’s overall performance.

Better Resource Planning, Optimizing the Cost Efficiency and Budget Allocation

Managers can efficiently allocate team member resources based on the forecasted workload, ensuring smoother operations and providing adequate support in case incidents occur. This proactive staffing approach optimizes resource cost, as resources are used strategically based on actual needs and performance demands. Furthermore, Workload Advisor provides valuable insights to the company when making decisions regarding capital expenditure (CapEx) and operating expenditure (OpEx) allocations, leading to data-driven and informed choices.

Speeding Up Root Cause Analysis and Preventing Future Incidents

The longer it takes to detect the root cause of a problem and solve it, the more expensive the problem becomes for the customers. With the ability to detect both future forecasted spikes and spikes occurring in real-time, the root cause analysis (RCA) process is expedited, allowing teams to focus less time on investigation and more time in service restoration. Real-time spikes trigger immediate alerts to database engineers, facilitating immediate actions to limit business interruptions. For future forecasted spikes, engineers implement proactive measures and database health assessments to remove the threat of an incident before it occurs.

By leveraging data science algorithms, Workload Advisor provides accurate workload forecasting, resulting in improved performance, reduced risks during deployments, optimized cost efficiency and quicker root cause analysis. Data-driven decision-making enables our engineers to deliver consistent database performance, ensuring our customers experience minimal disruptions in an ever-evolving digital landscape.