Enablement Platform

 ConOps & Use Cases

Portalogiks
Products

•  Centralized computing era, place all services, apps, and data in a single place
•  Infinitely scalable, elastic compute resource pool
•  Software defined everything, delivering IaaS, PaaS, and SaaS capabilities in OPEx acquisition models
•  Examples: Amazon's AWS, Microsoft's Azure, Google Cloud Platform, and the IBM Cloud

•  Does what CDN did to static content but for distributed dynamic workloads such as containers and microservices
•  Enables distributing components of an application across multiple endpoints to reduce the latency involved in the roundtrip
•  Delivered as a managed service, customers won’t have to deal with the hardware and software maintenance
•  With increased investments in hybrid and multi-cloud environments based on Kubernetes, mainstream cloud providers may eventually offer cloud edge services as an extension of their existing platforms
•  Examples: Section.io, Volterra.io, Mimik, and Swim.ai are some delivering cloud edge capabilities.

•  MEC moves the computing of traffic and services from a centralized cloud to the edge of the network and closer to the customer
•  With 5G becoming a reality, MEC is becoming the intermediary layer between the consumers and providers of the public cloud 
•  The edge infrastructure runs within the facility of a telecom provider co-located within a data center or even a cellular tower
•  Examples: Telco companies such as Verizon MEC or AT&T MEC, and T-Mobile

•  Typically a hyperconverged infrastructure (HCI)
•  Comes with an all-in-one hardware and software stack typically managed by a vendor
•  Demands power and network resources that are available only in an environment like an enterprise data center
•  Doubles up as an IoT gateway, storage gateway, AI training and inference platform 
•  Comes with an array of GPUs or FPGAs designed for managing end-to-end machine learning pipelines including the training and deployment of models
•  Examples: AWS Snowball Edge, Azure Stack Edge, NVIDIA EGX A100 and Nutanix Acropolis

•  Represents a cluster of inexpensive machines 
•  Compute cluster is powered by an internal Graphics Processing Unit (GPU), Field Programmable Gate Arrays (FPGA), Vision Processing Unit (VPU), or an Application Specific Integrated Circuit (ASIC)
•  A cluster manager like Kubernetes is used for orchestration of the workloads and resources in the clusters
•  Examples: a Kubernetes cluster of Intel NUC machines or Zotac Mini PC connected to NVIDIA GPUs or Intel Movidius VPUs deployed in a retail store or a restaurant running NVIDIA CUDA/TensorRT or Intel OneDNN/OpenVINO models for AI acceleration

•  Runs in disconnected, mobile and remote environments such as trucks, vessels, air crafts, and windmills
•  Capable of running a full-blown operating system like Linux or Windows
•  Based on a single board computer with either ARM64/AMD64 architecture
•  Often uses a software stack and AI accelerator to speed up the inference
•  Ideal for protocol translation, data aggregation, and AI inference
•  Examples: NVIDIA Jetson Nano module running NVIDIA JetPack and TensorRT or an X86 board with Intel Movidius Myriad X Vision Processing Unit (VPU) with Intel’s OpenVINO Toolkit

•  Sensors connected to the microcontroller generate the telemetry stream that is used by a deep learning model for inference
•  The most recent incarnation of the edge computing layer running within the context of a microcontroller and a microprocessor
•  When a microcontroller is capable of running a TinyML AI model, it qualifies to be a micro-edge computing device such as OneTech's MicroAI capable of running on a programable logic controller/microcontroller
•  Examples: TensorFlow Lite running on a combination of ARM Cortex-M processor co-located with a microcontroller such as nRF52840 or Apollo3 Blue,