A variety of vital digital services — including that streaming service with ridiculously large catalogs of video content, or that data service that delivers information about its analytics — leverage multiple dependent systems or machines behaving as clusters under the umbrella of distributed computing. Without a doubt, distributed systems are game changers; they provide us a way to respond, and in fact, provide us a way to improve our abilities as technology advances onward, trying to keep pace with the exponentially increasing demands of increasingly complex ecosystems. However, with that capability comes expense — distributed systems being resource hogs or just plain overengineered — they can, in fact, be extremely inefficient. Therefore, is there a way to engineer systems that are smarter, more efficient, and less variable with respect to actual delivery time? This is where machine learning enters. Machine learning is not just a fancy buzzword; machine learning is a useful tool to predict demand, improve existing business processes, and ultimately develop distributed systems that do not just work, but work. The Data Deluge: Too Much Information, Too Little Time The Data Deluge: Too Much Information, Too Little Time Over the last decade, the amount of digital data we generate has increased dramatically. Every day we generate over 2.5 quintillion bytes of data! We can no longer analyze, store, or understand data in the same way we used to or on this scale. Thinking, working, and understanding the data at this size and structure present us with a number of technical issues we will have to consider for the long term, and we should develop solutions that will allow us to actively utilize it to train our models. Working within distributed systems complicates our attempts to relate; not only do we have the size of data to relate to, but we are relating to a distributed image as well — with organizations of multiple machines or guarantees, multiple sites, multiple user loads, and complex system user loads related to their interaction. Breaking Down Data Silos Breaking Down Data Silos Data silos, where data is held in one or another system that governs what that one system can or cannot do outside of that system. Data points from all sources can certainly hold highly inconsistent baseline quality or product differences. The pressures upon the (traditional) methods of analysis will present considerable challenges to your data analysis platform and efforts, ultimately resulting in forcing you to log into the potential risk of ensuring only 'nice' or good data is accessed! This kind of data frequently challenges conventional single-machine learning approaches. One way of thinking about this data would be through distributed machine learning. Imagine imparting knowledge to one group of students — or potentially many — in a classroom, as opposed to each student one at a time. This can be a much more complicated problem, but certainly worthy of consideration. Smarter Data Centers: Intelligent Decisions Drive Sustainability Smarter Data Centers: Intelligent Decisions Drive Sustainability Data centers are a vital component of the connected world, allowing for an increase in global access to applications and services through increased resource and energy consumption. Historically, operation management has led to a focus on uptime, and we are now seeing a shift to a more sustainable model of operation management. Edge computing — which by definition is processing closer to the edge of creation — presents a larger opportunity for efficiency between resource utilization, optimization, and resiliency/sustainability. Edge computing enables the processing and interpretation of data at the edge, closer to the point of creation, so it does not need to move as much data to cloud data centers, thereby reducing related energy and latency costs. Optimizing Resource Allocation Optimizing Resource Allocation This is where machine learning comes in to play an advantage! ML models can predict workloads that will be needed for CPU processing; furthermore, they can recommend placements of workloads to minimize energy use and optimize overall utilization — rather than operating under conditions of 'blindness' and adding extra resources unnecessarily, all in CPU processing. Furthermore, for example, models can appropriately analyze historic data relating to CPU utilization and temperature profiles, based on predictions of use for thermal load demand. This, too, can reduce the use of conventional static cooling and highly demanding energy utilization. Final Thoughts: From Science Fiction to Engineering Reality Final Thoughts: From Science Fiction to Engineering Reality We once only imagined these things would happen — in science fiction. The future is actually now; machine learning and gigabit distributed compute are real. We are well experienced at guessing and overreaching. Algorithms are learning, adapting, and optimizing in real time — everywhere. Machine learning is beyond just efficiency. In fact, machine learning is changing how we think about compute. Machine learning is bringing distributed systems greater speed, intelligence, and thoughtfulness. The dimension of intelligence is going to be the determinant of who will thrive or struggle when we start building digital ecosystems that have different intelligent, multidimensional elements. The future happens — now, in the present. One guess at a time.
