When it comes to Real-Time Bidding (RTB), success is measured in milliseconds. Any latency or delay in response time results in a lost opportunity. Your systems need to work at peak efficiency to gain a competitive advantage and maximize your RTB strategy.

In this article, we will explain how fine-tuning your RTB application makes a difference and how performance enhancements can result in significant gains. In a highly-competitive environment where speed often determines winners and losers, ensuring your RTB applications perform optimally is paramount.

Optimizing RTB Applications

Transactions in ad exchanges typically take about 100 milliseconds. In that time, the RTB platform decides which ads to serve on sites. So, poor response times hold immense significance. Optimizing response times and minimizing latency is crucial in programmatic advertising.

Whether you are managing a demand-side platform (DSP), supply-side platform (SSP), data management platform (DMP), or ad exchange, optimizing your RTB applications requires enhanced throughput. Higher throughput enables applications to process a greater volume of transactions at faster speeds.

Eliminating Performance Degradation

RTB applications must process vast amounts of data rapidly. Optimizing bid strategies, targeting specific audiences and behaviors, and evaluating campaign performance must all happen in real-time. As data volumes increase, they can strain system resources.

Additional demand for computing power or poor cluster management can quickly degrade workload performance, especially at high utilization rates. Simply put, inefficient workloads create bottlenecks that impact response times and user experience. Conversely, optimizing workloads and resources increases system stability to ensure reliability and resiliency in RTB applications.

Stopping Wasted Spending

Nearly half of businesses report that they have difficulty managing cloud spending leading to unnecessary spending and as much as 30% of cloud spending is wasted. There’s a long list of ways that spending starts to spiral, including:

• Overprovisioning and idle resources
• Inefficient resource and cluster management
• Failing to shut down unattached volumes
• Paused instances
• Failure to optimize on-demand vs. reserved instances

The right workload optimization tools can stop wasted spending.

Managing Service Level Agreements

You also likely have strict SLA requirements for your clients which you must meet. Failure to meet those requirements can be costly. Not only are there typically performance guarantees that include penalty clauses, but performance failures can degrade the user experience and undermine a client’s confidence in your applications to meet required service levels.

Meeting strict SLAs takes continuous monitoring and a strategy to automate performance optimization.

Automating Optimization

When you have to manage resource allocation manually, it’s easy to miss opportunities in an ecosystem that prioritizes speed or response. With the right workload optimization solution, you can autonomously and continuously improve runtimes and capacity and uncover code inefficiencies.

Runtime Optimization

Automating runtime optimization can improve performance by 20% to 45% on all major runtimes by deploying strategies such as:

• Prioritized thread scheduling for RTB
• Lockless networking for increasing parallelism
• Inter-process communication to enhance throughput
• Intelligent connection pooling to eliminate establishment overhead
• Adaptive congestion control for evolving workloads
• Improved memory allocation based on usage patterns

Capacity Optimization

Automating capacity optimization can also provide significant improvements to throughput, job completion, and response times. Other benefits include:

• Eliminating overprovisioning
• Right-sizing workloads
• Complete visibility into capacity and resource allocation
• Reducing cluster sizes to enlarge the capacity
• Optimizing server performance
• Releasing provisioned resources

Efficient capacity optimization can scale on-demand resources and accelerate performance.

Continuous Profiling

One of the keys to continuous optimization is continuous profiling. By continually analyzing code performance across your RTB environment, you can optimize the most resource-consuming parts of your code and improve application response time.

Profiling uncovers the inefficient steps in your code that impact critical resources such as:

• CPU runtimes
• CPU consumption
• Memory allocation
• Time spent to finish flow (wall time)

Continuous profiling enables you to fine-tune your RTB applications for optimal performance by providing line-by-line visibility into the areas of your code to find the root cause of performance problems. Profiling also helps analyze code changes, the impact of increased volume, or evolving requirements so you can address any emerging inefficiency quickly.

One important thing to note with profiling, however, is that some profiling tools can themselves be resource-intensive with high overhead. Some developers also dislike code profiling because traditional profiles require modification to source code. Today, however, the best profiling solutions no longer require code changes and have minimal overhead to your RTB

Continuous and Autonomous Optimization

Intel Tiber App-Level Optimization, an Intel Company, provides advertising and marketing applications with real-time, continuous, and autonomous performance optimization. Intel Tiber App-Level Optimization solutions work on Big Data, Kubernetes, and all major runtimes.

Intel Tiber App-Level Optimization solutions can increase throughput by 5X, reduce latency by 40%, and reduce cloud cost by up to 45% with no code changes required. Request a demo and see how Intel Tiber App-Level Optimization can help you gain a competitive advantage with RTB.