As telcos continue to scale their cloud deployments, they are also seeking ways to streamline operational control by managing these systems with a higher degree of predictability. At Red Hat Summit 2017, May 2-4 in Boston, join us for a presentation on ways to do just that.
In this talk on May 2 from 4:30-5:15 p.m., we will present a production, multi-tenant (containerized) compute and software-defined (Red Hat Ceph and Red Hat Gluster) storage microservices architecture, discuss the typical workloads that are imposed on the system and examine key events that are generated by discrete components that comprise the overall system. The presenters include Narendra Narang, Principal specialist solutions architect for cloud storage at Red Hat, and Daniel Smith, who runs operations operations and infrastructure at Kaloom, a software startup that’s built Kaloom Flow Fabric. Kaloom’s offering is a software networking solution designed to allow virtual network functions (VNF) to run at scale using commodity hardware, merchant silicon and existing data center architecture.
Narang and Smith will propose a set of open-source technologies utilized to build a pipeline that encompasses collection of these events, appropriate storage of these events and a Kappa (Analytics) architecture that leverages an in-memory analytics engine – Apache Spark – to correlate these events in order to extract operational insight with predictability. The two contend that event data is voluminous, has numerous properties and, in some cases, may require a grouping of multiple events to create a more complex event. Furthermore, the characterization and evaluation of complex events to predictively recognize and avert potential issues is highly dependent on the temporal relevance of the event data of concern.
During the presentation, Narang and Smith hope to demonstrate how simple machine learning algorithms may be applied to extract typical (affinity) and atypical (interference) patterns of resource behavior within the shared, distributed system and how building a parametric model with suitable weightings for different features may be useful in making these predictions with increased accuracy and in taking the appropriate remedial action.