Predictive Analytics for IT Giving Organizations an Edge in a Rapidly Changing World

Transcription

1 Predictive Analytics for IT Giving Organizations an Edge in a Rapidly Changing World EXECUTIVE SUMMARY By Dan Kusnetzky, Distinguished Analyst Organizations find themselves facing a complex mix of applications and workloads, each of which may be based upon an extremely complex distributed, multi-tier architecture. Knowing what is going on from moment to moment is beyond the capabilities of mortals and yet if something unexpected occurs and systems slow down or fail, there are many economic consequences. If we add cloud computing and virtual datacenters to the environment, the complexity increases dramatically. The advent of highly dynamic virtualized environments has changed the game for systems management strategies. At issue are manually set rules and thresholds that, in a virtualized environment, quickly become more unwieldy and complex than even the best engineer can resolve. Today, the organization s IT staff spend much of their time fighting fires that are caused by unplanned outages, application slow downs and the like rather than being able to find and correct problems before they strike. Organizations have long used computational (mathematics-based), analytic approaches as part of their business intelligence applications to increase levels of customer loyalty, detecting fraud, or predicting how their investments are likely to fare in a rapidly changing world. Predictive analytic approaches have evolved and are now being applied to IT as enterprises address the challenges of virtualization and cloud management. Organizations need intelligent tools that can watch the complex operations of their workloads. These tools must gather the data on system, network and application operations; look for looming problems; and present what it learned in a clear, easy-to-understand way. The tools must be able to do this without human intervention and it must learn the environment without requiring costly and time-consuming configuration. WHAT IS THE ISSUE? Modern applications are no longer monolithic blocks of code running on a single server. They are now architected as distributed services that are linked together to create a specific function. These services may reside anywhere on the Internet or behind the firewall in the organization s own datacenters. Each of these distributed services might be replicated on several different systems to improve reliability, performance and resilience. The systems may be physical, virtual or cloud-based. Physical and virtual systems might be housed in the organization s datacenter or off-premise in a Document # The Kusnetzky Group 2011 The Kusnetzky Group is an independent supplier of marketing services to suppliers end user organizations and suppliers in the systems, virtualization, cloud computing and open source technology markets. Technology suppliers are among those organizations. The opinions presented in this document are based upon our research, our personal experiences and actual use of technology regardless of whether this document or the supporting research were sponsored by one or more of the Kusnetzky Group s clients. This document may not be copied in whole or in part without the written permission of the Kusnetzky Group.

2 service provider s datacenter. Virtual systems might be running on the organization s own equipment or be hosted on a system in the cloud somewhere. It is easy to understand that IT administrators find it difficult to know what is running, where it is running and if it is meeting service level objectives. Each of these systems; physical, virtual or cloud-based, is supporting application components or applications that produce enormous amounts of operational data. It is simply too much data for a human being to gather, analyze and then use to make reasonable operational decisions. The massive amount of data an IT administrator must understand is far greater than would have ever have been seen in the days of mainframe-based applications. It long ago passed what a mere mortal could understand on a real time basis. In the past, application slow downs or failures would have just had a negative impact on an organization s own staff. Now applications face customers, partners as well as employees. An application failure can have major, strategic impact on an organization and its operations. W H A T IS C A U S IN G P A IN? Today s management environments deal with application environment in a piecemeal fashion: today s complex System management tools look at systems (physical, virtual or cloud-based.) Network management tools look at the network. Application performance management tools look at applications and their components. Database management tools look into the operations of the database. Virtualization management tools look into each of the seven layers of virtualization technology in use inside of the datacenter today. Cloud management tools offered by the service providers look into how the virtual services in the cloud are operating without regard to what they are doing. For the most part, organizations rely on manual processes to integrate all of the operational data made available by these tools regardless of if they reside in a physical or virtual datacenter. They rely on the expertise and capabilities of human operators even though too many events are happening every second for a human operator to manage. Attempts have been made to automate the process of gathering together the mounds of data that are created every second so that they can be analyzed and visualized. Usually, these tools require that the organization undertake extensive script development and management. If the configuration changes and the scripts haven t been updated, the data that is collected could be useless or completely misleading. In the end, this approach simply can t offer a real time view of what s happening everywhere in the complex application environment. This often means that applications become unavailable and the IT organization faces a big challenge to determine the root cause of the problem and fix it. They often become aware of the issues only when someone calls the help desk. By then, of course, it is too late and # The Kusnetzky Group 2011 Page 2

3 applications have slowed down beyond the point of being usable or have failed completely. IT administrators need help to know instant by instant what is happening to each application, to each application component and each server, regardless of whether it is a physical server, a virtual server or out in the clouds somewhere. Without that knowledge and a firm knowledge of what is actually causing the problem, a typical IT staff member could not possibly respond fast enough to prevent slow downs or outages. The organization could easily incur significant costs to find, isolate and then fix problems that are based upon a failure of any single system or based upon the interactions among systems. They would most likely also face significant loses of revenue and, perhaps, customers as well. WHAT WOULD AN IDEAL SOLUTION LOOK LIKE? In an ideal world, tools would exist that offered a holistic view across all of the systems the organization uses in its IT infrastructure. These tools would be able to look into the operations of each system in a heterogeneous, multiplatform, multivendor environment. Furthermore, these tools must be based upon capturing, analyzing and learning from the overwhelming amounts of complex operational data produced by all of these systems. This analysis, obviously, must be based upon intelligent computational (read mathematical) algorithms rather than seat-of-the-pants judgments of even the most skilled IT administrators. The ideal solution would operate in real time, work with existing applications and management tools and offer the ability to evaluate and manage the utilization of the organization s IT resources across systems, networks, and storage. The ideal solution would work with both onpremise and cloud-based applications. This ideal tool set would make it possible to head off problems before they happen rather than merely helping the organization clean up the mess afterwards. These tools need to have the following attributes: Self-learning the tool should watch the operational environment, learn from it and go about the task of helping IT administrators. IT staff shouldn t be required to create and edit scripts, develop complex rules or consider what is a reasonable operational threshold for any given function. Adaptive the tool should continue learning as the environment runs day-by-day and adapt its internal rules, thresholds and approaches as needed. Analytical Actual operational data should be evaluated on a real time basis using intelligent computational methods rather than relying on manual methods. Cross-Platform today s datacenter (physical or virtual) relies on many different types of computer systems, operating systems, application frameworks, database management engines, networking tools. The ideal tool would be able to look into the operational data of all of these tools. # The Kusnetzky Group 2011 Page 3

4 View application solutions from end-to-end the idea tool would look at all of the different components of a complete solution while doing its analysis of operational data. Gather data across business unit; applications; datacenters (local or virtual); systems (physical, virtual or cloud-based) and then analyze every detail to determine the overall health of the environment. Correlates across business and system silos ENTER NETUITIVE PREDICTIVE ANALYTICS AND ITS BEHAVIOR LEARNING ENGINE Although there are many suppliers of management software, there are a very small number of suppliers that even come close to what would be expected of an ideal, self-learning, self-healing solution. Netuitive is one of the few suppliers seeking to offer tools that offer the capabilities needed to be included in the ideal solutions list. Its technology is based upon nine patented predictive analytic applications and technologies and is powered by its Behavior Learning Engine. Netuitive s technology gathers operational data from all available sources and then uses predictive analytics to understand the current state of the environment, forecast what is likely to happen next, identify potential problems and, finally to resolve performance issues before they cause failures or impact quality of service. This technology is already in use in hundreds of customer environments, including 8 of the 10 largest banks. If one looks at the product the company offers today, the following capabilities are available: Capacity Analysis Understand and optimize utilization of existing resources Automated Diagnostics Correlate behavior and isolate root cause across IT silos Predictive Analytics - Forecast problems 2+ hours before users are impacted Enhanced Virtual Data Center Dashboard [To be available in Q2 2011] - Gain real time, multi-tier visibility into infrastructure performance Trusted Triggers [To be available in Q3 2011] - Intelligence for autonomic computing (dynamic resource provisioning and rightsizing of infrastructure - cloud service governor) Integration Studio [To be available in Q4 2011] Input real-time performance and capacity data from any system or application USER STORIES FORTUNE 50 TELCO DEPLOYS NETUITIVE TO SUPPORT CRITICAL APPLICATIONS A Fortune 50, multi-national telecommunications company providing land-based and wireless services to consumers and businesses globally was driving the roll out of new retail and point of sales (POS) applications and ran into a snag. The increased volume of traffic was putting pressure on existing monitoring and management tools. False errors were reported and staff found it difficult to look at each issue, find the root cause and then determine if there was a real problem. # The Kusnetzky Group 2011 Page 4

5 Network administrators tested Netuitive s predictive analysis software and discovered that it forecasted potential problems up to 37 minutes before service levels suffered. Since that test, Netutive s software has been deployed to eight datacenters and is monitoring 300 mission critical applications. The company publicly reported that Netuitive is enabling it to analyze more than a million metrics simultaneously allowing it to eliminate 3,480 hours annually in service degradation representing a business savings of $18 million. TOP 10 BANK DEPLOYS NETUITIVE TO SUPPORT VIRTUAL ENVIRONMENT A major bank began pilot deployments of VMware-based virtual servers. While they bank experienced significant economic benefits, application owners were concerned that application performance would suffer in a virtual environment. The virtualization team tested dozens of tools in a search for the best solution. Netuitives analytics-based performance monitoring tool was selected because it was the only tool they found that was available now that could monitor VMware virtual machines, VMware ESX clusters, virtual desktops as well as the applications they were supporting. By 2010 the bank had virtualized 70% of their industry standard systembased environments. During the product roll out, Netuitive detected a problem 15 hours before service levels suffered. The product was able to learn about the organization s virtual machines, clusters and applications without requiring the staff to focus on an extensive configuration management project. SUMMARY Predictive analytics has become a requirement to deliver holistic visibility across platforms and vendors and enable proactive performance management in both virtualized and cloud environments. Today s application performance management and configuration management tools are no longer sufficient by themselves. Intelligent tools that can capture and analyze real time operational data are now critical requirements for today s IT environment. Organizations need to know what is happening in real time so that issues can be prevented from becoming problems. Any new tools must fit into the established environment and make it work better rather than requiring wholesale changes. For more information, please visit Netuitive ought to be considered as part of any large enterprise initiatives for performance and capacity management in large virtual private data centers and cloud management environments. # The Kusnetzky Group 2011 Page 5