Download PDF
Monitoring a Complex and Elastically Scaling Cloud Infrastructure to Avoid Performance Issues
Technology Category
- Analytics & Modeling - Real Time Analytics
- Infrastructure as a Service (IaaS) - Cloud Computing
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Software
Applicable Functions
- Discrete Manufacturing
- Quality Assurance
Use Cases
- Predictive Maintenance
- Process Control & Optimization
- Real-Time Location System (RTLS)
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
The Challenge
GameChanger runs a complex and elastically scaling cloud infrastructure hosted on Amazon Web Services (AWS) to support its mobile and web-based applications. This environment includes multiple databases and services, each of which requires monitoring. Taking data from tens of thousands of sources, transforming it into reader-friendly snippets, and then pushing it to fans in real-time means GameChanger has to be ready to handle high traffic, heavy I/O and to troubleshoot issues at a moment’s notice. GameChanger first built its own infrastructure monitoring tools in-house from the open-source components Graphite and StatsD. These homegrown monitoring tools got the job done but at a steep price: they required an extra $1,000 of AWS resources and more than half an FTE’s hours each month just to keep GameChanger running.
About The Customer
GameChanger ( www.gamechanger.io ) produces a popular Android and iOS application used to record scores, statistics and more for amateur basketball, baseball and softball teams. GameChanger keeps score for tens of thousands of sports games per weekend, pushing real-time updates to fans online through texts, emails and its mobile application. The company runs a complex and elastically scaling cloud infrastructure hosted on Amazon Web Services (AWS) to support its mobile and web-based applications. This environment includes multiple databases and services, each of which requires monitoring.
The Solution
Engineers from GameChanger’s infrastructure group were the first to suggest Datadog. Savino found that Datadog’s monitoring solutions did exactly what GameChanger was trying to do in-house, but in an easy-to-use turnkey solution, at a lower cost and with no dedicated FTE time required. Datadog integrated seamlessly with AWS, MongoDB, StatsD and the many other services and databases that made up GameChanger’s infrastructure. This immediate integration made the company’s switch to Datadog quick and easy. Setting up essential monitoring displays and custom alerts was similarly straightforward. GameChanger no longer had to rely on costly and time-consuming in-house solutions; it instead could monitor its entire infrastructure with Datadog.
Operational Impact
Quantitative Benefit
Related Case Studies.
Case Study
Factor-y S.r.l. – Establishes a cost-effective, security-rich development environment with SoftLayer technology
Factor-y S.r.l., a web portal developer, was faced with the challenge of migrating its development infrastructure to a reliable cloud services provider with highly responsive technical support. The company needed a solution that would not only provide a secure and reliable environment but also support its expansion by providing resources to create and deliver innovative offerings.
Case Study
Darwin Ecosystem: Accelerating discovery and insight through cutting-edge big data and cognitive technologies
Darwin Ecosystem was founded with a unique vision of harnessing chaos theory mathematics to uncover previously hidden connections in unstructured data. The company’s algorithms can look at all the data generated by any source (such as news, RSS feeds and Twitter), and analyze how a specific set of concepts within that data are evolving over time. This is particularly valuable in situations such as business and competitive intelligence, social research, brand monitoring, legal discovery, risk mitigation and even law enforcement. A common problem in these areas is that a regular web search will only turn up the all-time most popular answers to a given question – but what the expert researcher is actually interested in is the moment-tomoment evolution of the data available on that topic. Darwin’s algorithm is computationally intensive, and the sources of data it correlates can be vast. To bring its benefits to a larger commercial audience, Darwin needed to find a way to make it scale.
Case Study
Zend accelerates, simplifies PHP development
Zend Technologies, a major contributor to the PHP open source community, needed to keep pace with emerging trends such as mobility, agile development, application lifecycle management and continuous delivery. The company needed to provide the right tools to the worldwide community of PHP developers. The challenge was to support enterprise-class capabilities from end to end, including mobile, compliance and security. The pace of business required developers to show results fast across a variety of devices without compromising quality or security.
Case Study
Delivering modern data protection with cloud scale backup from Cobalt Iron and IBM
Organizations are struggling to modernize their legacy data protection environments in the face of growing demands around new infrastructure, new applications, and budget consolidation. Virtualization and modern application development processes have significantly outgrown legacy backup architectures. In response, infrastructure teams have created multiple backup solution types to handle the varying SLAs (performance, scale, cost) required by their business sponsors. However, the sheer number and variety of solutions in this uncontrolled expansion creates huge amounts of work, threatening to overwhelm the IT team in many organizations. Today, developers may add new applications and virtual server instances by the hundreds per day without accounting for the restrictions of the existing backup infrastructure. They leverage the cloud for immediate compute and storage resources, yet rarely communicate succinctly with corporate IT to ensure that the appropriate data protection services are in place.
Case Study
Achieving near limitless scalability and flexibility with data in the cloud
Web-based publishing platform SpaceCraft found that as its client base grew, it was spending an increasing amount of time managing its databases, distracting its focus from product innovation. As its user base rapidly expanded, data volumes at SpaceCraft began to rise dramatically. Along with their main focus on maintaining and further developing a great platform for web publishing, the SpaceCraft team had the added pressure of managing the increasing quantities of data while ensuring ongoing high performance for clients.
Case Study
nViso SA – Delivers emotion recognition solutions worldwide with a scalable SoftLayer hosting solution
nViso SA, a company that provides emotion recognition solutions, was in need of a high-performance cloud hosting infrastructure. The company wanted to extend its services to a global customer base. The challenge was to find a solution that could handle the demands of their growing customer base and the need for high performance and reliability.