Organisations that leverage virtualisation as a means to provision and reallocate pooled resources still face the challenge of gaining an intimate level of knowledge of application behaviour and runtime requirements. It's a pity, isn't it, that operating systems and virtualised infrastructure solutions can't just know what applications need and allocate resources to fit, instead of requiring a lot of observation and scripting. Virtual infrastructure will undoubtedly take on ever-smarter heuristics for automating the distribution of computing, storage and communications resources. But setting this up as the only alternative to scripted agility presumes that software within a virtual container will always play a passive role in the structuring and optimisation of its operating environment. I submit that to extract ever more value from virtualisation, software must take an active role. However, I still hold to my original belief that software, including operating systems, should never be aware that it is running in a virtual setting. I want to see software, even system software (an OS is now an application), get out of the business of querying its environment to set a start-up and, worse, a continuous operating state. Doing this severely limits the ability of tasks to leap around the network at will, because an OS freaks out if it finds that its universe has changed in one clock tick. In the least disruptive case, if its ceilings were raised, the OS instance (and, therefore, the mix of applications running under it) would take no advantage of the greater headroom afforded by, say, a hop from a machine with 2GB of RAM to one with 32GB. So how can software be a partner in the shaping of its virtual environment without trying to wire in awareness of it? Clearly, software must be able to query subordinate software to ascertain its needs. The technology exists now to do this at start-up. When commercial software, or software written to commercial standards, is compiled, optimisation now includes steps that give the compiler a wealth of information about the application's runtime behaviour. One is auto-parallelisation. This stage of optimisation identifies linear execution paths that can be safely split apart and run as parallel threads. That's some serious science, but the larger the application is, the more opportunities there are for auto-parallelisation, and on multicore systems, the win can be enormous. The analysis that a compiler must perform to identify latent independent tasks could go a long way towards helping a VM manager decide how an application can be scattered across a pool of computing resources. If the ideal virtual infrastructure is a grid, then the ideal unit of mobile workload is the thread. If the compiler finds that an application is monolithic, this information, too, could be valuable, signalling that a process can be moved only as a whole. I'm more excited about two technologies that apply runtime analysis to the goal of optimisation. A two-step optimisation technique involves compiling the application with instrumentation for detailed runtime profiling that produces a detailed log of the application's behaviour. This log, plus the source and object code, is pushed through the compiler a second time, and the resulting analysis creates potential for optimisation bounded by only the intelligence in the compiler. If this intelligence were available at runtime, then a virtualisation engine wouldn't need to wonder so much about whether a process, thread, block of memory, open file handle or network socket could be safely relocated. The kind of surprises that complicate planning and automated reallocation of resources would be significantly reduced.
- Free Whitepaper! Learn how IT is evolving from producer to enabler, and fostering collaboration around analytics.
- Free Whitepaper! The 5 criteria to help you select the right analytics platform for your organization.
- Free Whitepaper! Learn how to create an analytics environment that is governed, scalable and self-serve.