Data Center Analytics utilize big data, machine learning, mathematical modeling, and advanced analytics technologies to enhance data center operations (micro segmentation, network insights monitoring, automated application segmentation, workload protection, automated application whitelisting, zero-trust security) functions with proactive, personal, and dynamic insights. These platforms enable the concurrent use of multiple data sources, data collection methods, analytical, and presentation technologies.
Organizations are deploying applications in multiple public and private clouds while also continuing to support legacy business applications in on-premises data centers. This trend will be accentuated and amplified by the rise of microservices, yielding a growing number of business-critical applications developed with innate portability. Indeed, as a result of microservices, developers are beginning to construct highly distributed application environments in which application tiers and data services are spread across data centers and public clouds. Because of these trends, multicloud data center operators are facing serious challenges:
The major issue in multicloud data centers is that there is literally no visibility into applications interdependencies. It is critical to understand what applications are running, and how these are interdependent. If you do not know what your apps are and what their communication patterns look like, application whitelisting based micro segmentation becomes impossible to implement.
Because apps are highly distributed, 70-80% of traffic is now east-west traffic in data centers. Traditional monitoring technologies at the edge of data centers monitor what is coming in and going out of the data center, but they do not monitor what is really inside of the data center. Hybrid cloud makes workload protection issues even worse because every app on public cloud still relies on the services that are delivered on on-premises data center infrastructure.
Given the limitations of network protocols and many network performance monitoring tools, enterprises lack adequate data-plane visibility for performance tracking, troubleshooting, and diagnostics, all of which are critical to ensure and maintain application availability, performance, and responsiveness. Data center operators require systems that can provide pervasive and real-time network insights with data-plane streaming telemetry information and also the ability to analyze such information at scale using machine-learning technologies.
Security operations teams struggle to build a secure infrastructure as modern applications are dynamic and distributed across on-premises and multicloud environments, which in turn, makes it difficult for operators to provide a secure and agile infrastructure.
Cloud workload protection platforms use application whitelisting-based micro segmentation to allow operators to control network communication within the data center and enable a zero-trust model. Building on these capabilities, these platforms enable holistic workload protection for multicloud data centers by using process analytics, vulnerability assessment, and automated remediation.
Infrastructure operations teams lack adequate data-plane visibility for performance tracking, troubleshooting, and diagnostics, all of which are critical to ensure and maintain application availability, performance, and responsiveness. Current network-monitoring offerings provide very limited visibility into data-plane traffic within the network fabric, resulting in higher mean time to repair/resolution (MTTR ) and other operational inefficiencies.
Powerful data center analytics platforms provide network operators with comprehensive and modernized approaches to actionable network insights, capable of supporting any application on any infrastructure.