Credit Suisse Case Study
With trading volumes doubling every year, the bank needed to find a way to support growth projections for its mission-critical Java application, or risk severe business consequences.
The Azul solution provides unprecedented performance and scalability for the bank's Java applications.
Dramatic improvements in application capacity enable Credit Suisse to meet and improve SLAs even with growing user demand
Faster application response times help the bank avoid millions in regulatory penalties
Reduced IT complexity lowers overall TCO
Improved end-user experience for internal and external applications increases customer satisfaction and employee productivity
Credit Suisse Powers Trading Revenue Growth With Azul
When your clients include some of the world's most well-known companies, wealthy individuals, and institutional investors, your mission-critical trading applications had better perform flawlessly. For this global banking and financial services firm, whose trading values were doubling every year, ensuring high application performance was beginning to be a problem. The bank, active in over 50 countries and employing approximately 40,000 people, could no longer ensure its trading applications would deliver their intended business outcomes. This posed significant risk to the firm. Trading applications provide a consistent revenue stream and are the lifeblood of the business. If these and its other Java™ applications suffer from poor scalability or response times, the entire bank suffers from loss of revenue, loss of margin, and added cost due to compliance reporting penalties.
For example, its Fixed Income program, which takes trades from customers and routes them to the market, could only support 1000 trades per second. With user demand growing rapidly, the bank could start losing customers if this application was unable to scale with the business. The bank's application for buying and selling trading companies' debt also had performance problems. It could only support eight simultaneous users with 24-second response times on average. This was unacceptable. A key business requirement of this application is the ability to handle split second buy/sell decisions.
Clearly, the bank needed to optimize its trading applications or risk the business consequences, and it had to act fast. The banking industry is highly competitive and financial institutions are always looking for ways to steal customers away from a competitor. To solve this challenge, the company had to find a way to make its Java environments run faster while at the same time making it easier for the company to manage. It considered the traditional horizontal/scale-out or vertical/scale-up models, but these approaches would be costly and add huge amounts of IT complexity.
The company needed a new technology that would deliver extraordinary performance, break free from application memory constraints, and fit into the bank's IT environment with minimal disruption.
Azul technology delivers breakthrough performance
For help, the bank turned to Azul Systems. After performing due diligence, the bank opted for a solution based on Azul technology that provides unprecedented capacity and scalability for the bank's Java-based workloads. Azul technology does not require disruptive changes to applications and eliminates speculative capacity planning.
Azul innovations free applications from memory constraints by providing the ability to use massive heaps (up to hundreds of GBs) per application instance, without garbage collection pauses. Azul's unique Pauseless Garbage Collection algorithm offers parallel concurrent garbage collection and eliminates application pause times, clearing the path for more predictable and consistent SLAs.
Trading applications meeting and exceeding SLAs
As a result of the differentiated Azul approach, the bank has dramatically improved the response times and scalability of its mission-critical applications. Today, the bank can ensure that its trading applications will continue to deliver predictable results in the face of growing demand and that its customer SLAs will remain intact.
In addition, the bank has enough spare computing capacity to handle unexpected requirements. And long pause times, poor throughput, and inconsistent responses are things of the past. For example, the bank's Fixed Income trading application now supports 6000 trades per second, with flat, consistent response times. Before, it maxed out at 1000 trades per second. The bank's application for buying and selling trading companies' debt also showed dramatic performance improvements. Now, running on the Azul JVM (and with virtually no changes to the application), this trading application scales to 88 simultaneous users – with just 2-second response times. Previously, it could only handle eight users with 24-second response times.
At the same time, the improved response times for key internal-facing applications allows the bank to submit regulatory reporting in a more timely fashion and avoid severe financial penalties from the SEC. Those fines could have of exceeded millions of dollars and put the company in jeopardy on Wall Street.
Moving forward, the Azul JVM will help the bank as it begins building its next-generation data center. The bank now has a technology strategy to support a centralized, shared, or utility computing model. With Azul, the bank can guarantee scalability, while reducing operational complexity across all data centers globally. This will pave the way for the global banking and financial services firm to achieve its most important objective: to become the world's premier bank.
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