Autonomic computing systems are becoming a reality.
In an increasingly competitive global environment, enterprises are under extreme pressure to reduce operating costs. At the same time they must have the agility to respond to business opportunities offered by volatile markets.
Leveraging IT for competitive advantage is fundamental to a company's success, while maximizing its use of IT infrastructure is crucial to cost reduction. Moreover, architecting systems to handle peak activity and to provide adequate business continuity backup is a prerequisite that adds to both capital and operating costs.
Implementing a shared IT infrastructure, or service grid, where IT resources can be made available, dynamically, to satisfy the needs of different business units, is a compelling business proposition for CIOs. Actually implementing such an architecture is challenging, however, and often the management overhead associated with its operation cancels out any capital cost savings.
One solution is to adopt what we call an efficient computing architecture, combining the necessary provisioning, virtualization, and automation components required for the 24/7 operation of such a service grid.
In this article we focus on the automation component that manages applications running in a service grid. This component is known generically as an EMS (execution management system). It enables business-oriented requirements such as service-level objectives to be mapped onto the operational processes or strategies that orchestrate IT resources at the hardware, operating system, and application stack levels.
We examine the fundamentals of an EMS implementation we have built using a POM (process-oriented middleware) platform and its relationship to autonomic computing. POM is the next generation beyond MOM (message-oriented middleware). MOM virtualizes data distribution, whereas POM virtualizes process execution, optimizing the execution of business or, in this case, operational processes in a highly distributed computing environment.
The principal challenge for CIOs of global enterprises today is to maximize the use of their IT resources while supporting an environment that is under constant change and subject to increasing regulatory scrutiny. They are charged with reducing the total cost of ownership of their IT infrastructure - a business imperative in a global world of increasing competition and decreasing trading margins - against a backdrop of increased market volatility, rapidly changing business opportunities and customer requirements, and where operational resilience is of fundamental importance.
The requirements for implementing an enterprise-wide IT platform can be specified by these characteristics:
Traditionally, individual lines of business have taken ownership of their own IT platforms, ensuring that resources are available to meet peak workloads and support failover conditions. Although this approach satisfies the need for IT resources at any time, it is inefficient in terms of utilization and lacks flexibility and scalability at an enterprise level. In practice, IT resources often remain idle or barely utilized, at significant cost to the company.
To achieve a cost-effective approach to the provisioning of IT resources, companies are looking to build shared infrastructures, leveraging industry-standard hardware components (such as blade servers and symmetric multiprocessing clusters) capable of supporting multiple heterogeneous operating systems (e.g., Linux, Solaris, Microsoft Windows) and application environments.
These shared infrastructures, or service grids, are dynamically partitioned and allocated to meet the needs of multiple lines of business as their requirements for IT resources change. IT components within the service grid are powered up and down and repurposed as required by the application mix at any given time.
For this service grid architecture to be feasible, however, management function is required to arbitrate on resource conflicts and to determine priorities across lines of business and their application environments. Moreover, these priorities cannot be viewed as static parameters and are likely to change according to business needs and other factors such as time of day.
Underpinning this management function is a set of requirements, or SLAs (service-level agreements), that specify the IT resources needed by a line of business and the specific applications that they maintain. The service grid management function consults these SLAs and maps them to a set of priorities mandated by the business, at an enterprise level, to determine which resources to allocate at any time on a best-efforts basis.