The global digital ad spending market topped half a trillion dollars in 2022 and is forecast to grow another 12% in 2023 to $600 billion. The AdTech market that supports all that ad spending is growing even faster, already exceeding $886 billion and forecast for a compound annual growth rate (CAGR) of 13.7% through 2030.

With trillions of dollars being spent on ad placements, targeting, ad intelligence, measurement, and optimization, optimal performance is a necessity to compete in this data-intense environment. While creative, AI innovations, and AdTech platforms grab the headlines, an integral — but often overlooked — component of leveraging a high-performance data infrastructure is application performance.

Reliable and optimal performance must be a top priority.

The Impact of Application Performance on Ad Intelligence and Measurement

Slow or unstable applications can significantly impact the effectiveness of ad intelligence and measurement products. For example, a delay in data processing and analytics can mean missed opportunities in a business where speed is a key component in success. Poor performance can also lead to incomplete analytics and reporting, which hurts efforts to gain insights and make data-driven decisions.

Application bottlenecks and downtime also directly affect the user experience for marketers and advertisers. Laggy dashboards, long load times, and errors frustrate everyone involved in the decision chain and greatly reduce productivity.

Ensuring optimal application performance is crucial for delivering a smooth user experience and maintaining high levels of productivity. In a high-stakes competitive environment, you simply cannot afford a poor user experience putting your business at risk.

Optimizing Ad Intelligence and Measurement Products

No matter which aspect of ad intelligence and measurement your business handles, you need efficient runtime optimization, code quality, and resource management — especially for real-time bidding (RTB). When these initiatives are prioritized, businesses can achieve significant cost savings and improve scalability, user experience, and data integrity.

Real-Time Bidding and Microsecond Decisioning

RTB requires deep data analysis and automated decisioning in microseconds. Even small performance issues can impact speed and cause significant issues. For example, Google ad networks require bid requests sent in ranges from 80 to 100 milliseconds and throttles bidders that cannot achieve these ranges consistently.

Since you also have to account for transmission time and any network latency, the decisioning process has to happen in even less time. Slow application performance can undermine even the best strategic plans for RTB.

Runtime Optimization in Workload Management

Whether you are operating on-prem, in the cloud, or in a hybrid environment, optimizing workload runtimes is crucial for ad intelligence and measurement. You need a way to produce optimal response time and throughput on every machine even when running workloads at peak times.

Typically, when first created, runtime environments were structured to cover a wide range of scenarios, but modern production environments work differently and in distinct repetitive patterns. Runtime optimization can deliver better performance with smaller cluster sizes, using fewer compute resources and lowering costs.

Continuous Profiling to Improve Code Quality

Code quality is key to user experience. Inefficient code and bloat can lead to performance lags and higher resource consumption. Code profiling and optimization can help reduce technical debt to improve speed and stability.

By continuously profiling your code, you can fine-tune and optimize the most resource-consuming parts of your code to improve performance and reduce costs. For example, if your ad intelligence product is experiencing high latency, bloated costs or slow job completion time, then profiling can allow you to identify the workloads that are taking up an inordinate amount of resources, thereby improving performance and reducing costs.

Resource Management and Capacity Consumption

Cloud resources can get expensive quickly, especially if they are not used efficiently. Nearly half of enterprises say they find it challenging to control cloud costs. Unused compute instances, idle virtual machines or clusters, and abandoned clusters can waste money.

Imagine an ad intelligence product experiencing a sudden surge in ad requests during a major event. Without effective resource management, it might struggle to handle the increased workload, leading to delays in ad analysis and reporting, potentially missing out on valuable insights for advertisers seeking to capitalize on the event.

Resource utilization must be tightly controlled to perform optimally and manage costs efficiently. Dynamic scaling and right-sizing resources can handle peak demand and avoid overprovisioning and wasted spending. An automated system to manage clusters, nodes, and workloads can optimize your ad intelligence and measurement products.

Maximizing elastic environments such as EC2 and EKS can allow you to right-size capacity on demand. Leveraging reserved instances and spot pricing models autonomously can result in significant savings.

Continuous Optimization Delivers Results for AdTech Companies

Deploying an autonomous, continuous workload optimization solution creates the efficiencies you need to improve performance and reduce costs. Here are just two real-world examples:

• In less than a week, Intel Tiber App-Level Optimization was able to improve Sharethrough’s CPU utilization by 26% that ultimately led to more than 17% cost reduction on EC2, without requiring any additional engineering efforts or resources.
• AdTech company ironSource handles some 25 billion ad requests daily on AWS, utilizing 2,000 spot instances and five Kubernetes clusters. Continuous optimization increased throughput by 29%, reduces instance count by 29%, and delivered a 25% decrease in overall cloud costs.

By aggressively managing and optimizing runtimes, code bases, and resources, AdTech companies can see significant results.

Intel Tiber App-Level Optimization’s Autonomous, Continuous Workload Optimization

Intel Tiber App-Level Optimization offers an AI-powered solution to optimize application performance by combining real-time monitoring with autonomous, continuous workload optimization. Machine learning algorithms analyze resource consumption, automatically adapt configurations, and proactively prevent incidents without any code changes required.

• Reduced infrastructure costs by right-sizing overprovisioned resources
• Faster processing of high volumes of data from multiple sources
• Lower latency for real-time bidding (RTB) and ad serving
• Improved stability during traffic spikes and events
• Higher throughput for parallel data processing
• Proactive prevention of performance issues before they impact users

Intel Tiber App-Level Optimization’s real-time, continuous orchestration tunes Big Data workloads regardless of the systems and infrastructure you use. By keeping applications running smoothly and efficiently, Intel Tiber App-Level Optimization empowers AdTech companies to deliver the best possible performance, analytics, and cost-efficiencies for users with no code changes required.Contact Intel Tiber App-Level Optimization today to request a demo.