SmartCloud Monitoring - Application Insight : Product Overview
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The long anticipated rise of cloud computing is finally taking hold, with analysts reporting more investment in public clouds than private clouds, and suggesting that half of all production applications will be running on public clouds in three or four years. The allure of public clouds springs from advantages like improved service scalability, reduced operational costs, and an increased focus on business goals and strategies instead of the technology needed to pursue them. However, there is a cost to that flexibility and economy, in reduced visibility of application and infrastructure health. Without direct control over the cloud infrastructure itself, traditional application performance management (APM) tools may prove impractical to deploy and manage.
Customers deploying applications to “public” clouds fall into at least two categories: enterprise customers using cost-controlled private clouds constructed to emulate public clouds, and smaller customers for whom public clouds are the production environment. In both cases, application teams don’t have the ability to deploy management servers and other monitoring components to the cloud infrastructure. Nor are they likely to have the budget or manpower to purchase and manage a typically complex APM solution. Instead, they need a lightweight solution—one that works with multiple provisioning solutions, is relatively simple to use, fast to deploy, and thus also fast to deliver value. Ideally, it would also have the flexibility and range of features required to ensure workloads are hitting target thresholds, and if they're falling short, identify and address the root problem to mitigate any business impact. As the name suggests, SmartCloud Monitoring - Application Insight allows application owners to see how their cloud-hosted applications are performing, ensuring that they’re getting the performance they expect from the cloud, and that their applications are serving customers and delivering value to the business.
SCM-AI may be purchased directly off the Web, and downloaded in minutes. Installation and configuration are equally simple, creating a buying scenario tailored to individual application owners. Application response time and Linux operating system agents are deployed into the cloud tenants’ virtual machines, which are joined by a compact “fabric node” virtual machine that provides services to the managed VMs, including VM discovery, event forwarding, distributed database integration, configuration, and other management functions, as well as a compact user interface. Each cloud tenant has their own self-contained monitoring solution, adhering to various data isolation regulations, yet it is powerful and extensible to grow as application demand increases.
Detailed monitoring capabilities are embedded in virtual machine images stored in one of the supported provisioning tools: Amazon EC2, VMware vCenter, and IBM SmartCloud Provisioning. Each time a new VM instance is provisioned from that base image, monitoring starts seamlessly, and automatically. The integration with the provisioning engine allows each new VM to be automatically discovered by the fabric node and associated with the correct business application, so existing application dashboards are updated to reflect the new virtual machine in seconds.
Key to getting the intended value from workloads executing in a public cloud is establishing what kind of demand those workloads are facing, how well the cloud is scaling (or not scaling) to meet the demand, and what kind of experience the end users of cloud-based applications are actually getting.
Despite its lightweight architecture, SmartCloud Monitoring - Application Insight delivers on all three of those disciplines. To begin with, it tracks both query volumes and user response times, displaying these in intuitive, “clickable” graphs in the application health dashboard.
Application performance metrics can be visually correlated with Linux operating system metrics, to help determine if an apparent application problem is actually being caused by resource constraint issues with the virtual machine itself.
An innovative IBM-developed distributed database is used to collect and centralize monitoring data, drawing it from each node for federated analysis. This helps reduce the bulk and complexity of the monitoring infrastructure—a particularly compelling point, since the entire goal is to improve application execution, not consume cloud resources for the monitoring process that the applications themselves might have needed.