In this era of the emerging digitized, mobilized, and cloudified enterprises, the concept of the "composable business" is the most critical piece which ties everything together. The digital enterprise is here, and its prime characteristic is that is essentially detaches and segregates existing businesses and reassembles them according to market demands. Every industry, from transportation to eyewear is up for disruption, and developers are in the forefront of this movement. In turn, these developers are under intense pressure to accelerate time to market. The composable enterprise approach requires a reconsideration of traditional models of the entire IT organization. These organization and their processes need to be broken up into components that follow certain key design principles such as The Minimal Functions with least Dependencies, portability, Shared Knowledge, Predictable Contracts and Maximized Human Value. The last three bullet points encapsulate the very definition of DevOps [3]. The concept of better integration between Development andOperations is a valuable objective. The goal is to foster measurable incremental cultural change to derive most overall value out of the union of people, process and technology. But the cultural issues, reward models, and risk allocation create obvious barriers in attaining those goals. The common industry belief is to use the composable enterprise framework to build a platform using the right tools and you will have attained DevOps nirvana. In this paper we will explore valuable lessons learned from our mistakes in tool centric adoption of IT Infrastructure Library (ITIL) [8] . We will also show how we applied those lessons to develop a lightweight composable/contextual DevOps framework that learns and measure itself to avoid those cultural pitfalls.

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The IBM Corp invents, develops and manufactures IT products, including computer systems and software, system networks, storage hardware and microelectronic products. The IBM CIO Global Infrastructure Architecture and Strategy (CIO GIAS) team is responsible for running and optimizing IBM's internal IT operations which include more than 500,000 users connecting to over 20,000 servers across 170 countries. The solution described below, implemented between 2008-2010, is a response to managing software licenses in one of the world's largest distributed enterprises.

With cloud eclipsing the $100B mark, it is clear that the main driver is no longer strictly cost savings. The focus now is to exploit the cloud for innovation, utilizing the agility to expand resources to quickly build out new designs, products, simulations and analysis. As the cloud lowers the unit cost of IT and improves agility, the time to market for applications will improve significantly. Companies will use this agility and speed as competitive advantage. An example of the agility is the adoption by enterprises of the software-defined datacenter (SDDC)[3] model, which allows for the rapid build of environments with composable infrastructures. With adoption of the SDDC model, intelligent and automated management of theSDDC is an immediate priority, required to support the changing workloads and dynamic patterns of the enterprise. Often, security and compliance become an 'after thought', bolted on later when problems arise. In this paper, we will discuss our experience in developing and deploying a centralized management system for public, as well as an Openstack [4] based cloud platform in SoftLayer, with an innovative, analytics-driven 'security compliance as a service' that constantly adjusts to varying compliance requirements based on workload, security and compliance requirements.In this paper we will also focus on techniques we have developed for capturing and replaying the previous state of a failing client virtual machine (VM) image, roll back, and then re-execute to analyze failures related to security or compliance. This technique contributes to agility, since failing VM's with security issues can quickly be analyzed and brought back online; this is often not the case with security problems, where analysis and forensics can take several days/weeks.

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