Opening up the EdgeBalancing Complexity and Scale:Edge Computing & the Internet of Things

White Paper

Enterprises that do not grab first-mover advantage in Edge Computing and IoT are at serious risk of losing not only ground, but worse, market share to their competitors. Agile, virtualized infrastructure is the engine that enables new levels of openness and flexibility, breaking the bonds of traditional proprietary systems and creating opportunities to deliver new services and extend the usefulness of existing edge technologies.

As the industry moves from “clouds for humans” to “clouds for machines”, the challenge will be in translating the successes we have achieved in managing the scale-up nature of public and hybrid clouds into the scale-out approach of massively distributed devices and applications. The current DevOps tools don’t function effectively in an environment where massive diversity in scope, scale, locations, technologies, etc., are quickly becoming the norm.

To solve this problem a new orchestration framework is required – one that can abstract away the diversity and complexity described above and allow application developers to focus on business functionality and competitive advantage. Through an integrated platform approach, CPLANE has addressed these challenges, enabling enterprises and service providers to quickly and easily deploy agile cloud infrastructure, including software-defined networking for sophisticated Edge Computing and IoT applications.

In this paper, we'll look at a new breed of clouds - Edge Clouds - that leverage new intelligent orchestration capabilities. Sophisticated policies, topologies, and transaction engines combined with integrated SDN, will enable Edge Computing to deliver the promise of digital transformation.

Executive Summary

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The wait is over - Edge Computing is mainstream

The Internet of Things (IoT) is staggering, and the growth predictions are being revamped on almost a daily basis. The interaction between IoT devices and the people around them is also rapidly changing. We’re moving from an age where clouds were primarily designed for humans (e.g., web services) to a new age where clouds are designed to support machines – and the machine learning that dictates how they interact with the world around them.

No company is immune to the onslaught of competition that will be thrust upon it by early adopters of Edge technologies - technologies that will make existing business and operational models obsolete, or at least non-competitive. Here’s a few examples of industries that are embracing the Edge to change the playing field:

Industrial IoT is utilizing Edge Computing to break the historical bonds of proprietary, single vendor control systems to build flexible systems that improve manufacturing processes, enable just-in-time product customization and increase human-robot interaction and safety (“cobotics”).

Brick and mortar retailers are creating immersive experiences using gender recognition and location awareness combined with historical purchasing patterns to improve targeted advertising and increase customer loyalty.

Smart automobiles are utilizing massive arrays of onboard sensors and will soon be coupled with 5G services to improve traffic management, increase safety and create enhanced passenger experiences (e.g., seamless video transition for in-car entertainment systems).

Video surveillance systems are making data more secure through improved encryption and denaturing, and improving bandwidth utilization through compression and optimization.

Smart buildings are not only improving existing control systems, but also enhancing people/destination management, creating digital engagement and improving sustainability.

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These examples, and many more across different industries are part of a continuing drive towards digital transformation. By moving more power (compute, storage, networking) closer to the Edge, new capabilities such as digital twins create an opportunity to significantly change how we interact with and manage traditional infrastructure.

However, while Edge Computing offers the promise of latency reduction, better security and enhanced services, there is a tradeoff. Traditional computing models such as public and hybrid clouds have dealt with operational issues found in large data centers with thousands of servers, but this model simply doesn’t apply to highly-distributed Edge Computing. Without proper planning and the right solution, companies will be faced with a new problem of dealing with thousands of distributed end points.

Making the Edge more intelligent

During the past decade there was an explosion of edge devices in many different forms. Most of these devices were sensors that simply reported on current conditions within a larger, but typically single-function device. A classic example of this scenario is the first generation of smart meters. These devices used a few single-purpose sensors to report electricity consumption for homes and commercial buildings, but that was the extent of their ability. New Edge devices are becoming more intelligent, but they often still lack the processing power to perform sophisticated services or adapt to technology or regulatory changes.

Whether it’s for newer technologies that simply need more processing power and connectivity, or legacy technologies that need to be repurposed, companies need to be able to move forward without “breaking the bank.” Rather than embark on a full technology refresh (e.g., “rip and replace”), companies are quickly learning that Edge Computing strategies can be realized much more effectively by delivering processing power (compute, storage, networking) at the right place, in the right amount, and at the right time – through Edge Clouds.

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Figure 1 - Placing Edge Clouds close to Edge Computing devices enables new services that can be updated and upgraded as needed.

Let’s take a closer look at such an example – video surveillance cameras. The demand for improved surveillance must be balanced with increased requirements for security and privacy. Sending full video streams back to a central public cloud is simply not a solution. The latency, bandwidth requirements and security risks make this strategy impractical. By placing small-footprint Edge Clouds close to existing cameras, they can now perform a variety of new functions which can be continuously updated and upgraded to support new business, operational, and regulatory requirements.

Optimization

Compression

Facial Recognition

Denaturing

Creating greater flexibility and choice for Edge applications

In addition to providing resources for repurposing existing devices or increasing the capabilities of new edge computing technologies, Edge Clouds are finding their way into market segments that were once thought impenetrable, such as industrial automation. While enterprises have long-enjoyed the benefits of generic platforms (e.g., Linux, Windows, and Unix.), industrial control systems have been constrained by proprietary real-time operating systems, hardened OSes and applications from a very small number of vendors. However, there is now a movement to make these systems more open and adaptable.

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The Open Process Automation Forum (OPAF) is focused on standardizing systems architecture and management solutions for the International Society for Automation (ISA) ISA95 Level 1 and Level 2 functions that deal with basic inputs and outputs from field devices. These Level 1 & 2 functions, along with standardized control block functions are targeted to reside in generic “distributed control nodes” located on or near the device(s) they are controlling. In this new model, process control tasks can be shared among clusters of generic processors running on “off-the-shelf” hardware and application-friendly operating systems and runtimes. If a DCN fails, the impacted control functions can be swapped to another DCN, or temporarily to a backup platform which can handle the control functions until it is restored.

By using an open architecture, commodity hardware and a virtualized software platform, industrial control systems can enjoy the same benefits and pace of innovation that other industries have realized through open source communities.

Figure 2 - By using generic hardware and software platforms, industrial control systems can be made more powerful, adaptable and resilient - all at a lower cost. (Image source: OPAF, modified)

Distributed Control Node

(L1 - L3 Functions)

Manufacturing OT Data Center

(L1 - L3 Functions)

Enterprise IT Data Centers

(L4 Functions)

High Availability, Real-Time

Advanced Computing Platform

Transactional

Computing Platform

RTAC Platform

Operating Platform Business Platform

App A App B App N

Firewall

Real-Time Service Bus

DCN

FCB

FCB

DCN

FCB

FCB

DCN

FCB

FCB

DCN

FCB

FCB

DCN

Distributed Control Node (DCN)

Monitoring & Management

Runtime Operating System

Network Protocols

Process Control

Application

Other Application

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Like their predecessors, many IoT devices and applications are simple in design and function, acting as sensors or beacons. As described in the previous examples, there is a rapidly-emerging class of edge applications that are comprised of complex ecosystems. These ecosystems may be comprised of a wide variety of devices, applications and services – each of which may be in various parts of a service chain that makes up the full “transaction cycle” for the edge application.

Public cloud services such as Amazon Web Services (AWS) or Microsoft Azure provide a rich platform for the creation and management of IoT devices by enabling the placement of applications services (e.g., analytics, machine learning, etc.) at the edge. However, these solutions place a heavy reliance on the public cloud not only for life-cycle management, but also for connection to back-end applications and services. While this model does offer “one stop shopping”, it introduces constraints:

Devices must be pre-enrolled and owned by the public cloud

Data traffic is limited primarily to “north-south” flows between the device and the public cloud

Applications must adhere to a specific platform architecture

These constraints pose problems for applications that rely on interactions between multiple devices in the same or various locations, such as intelligent vehicles, drone swarms, industrial control systems, etc. To satisfy the needs of these types of applications which typically have very demanding latency requirements, Edge Clouds offer integrated networking services that enable “east-west” connectivity within and across devices and other Edge Clouds.

Connecting the Edge to create richer applications

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Be prepared to trade scale-up complexity for scale-out complexityPublic and hybrid clouds share a common characteristic – they are comprised of lots (thousands or even hundreds of thousands) of servers. These servers may range from generic whitebox servers to very sophisticated converged infrastructure models. The mega-centers that house these servers are very sophisticated and often operate on a “fail fast” model, which enables applications to be recovered quickly and then the failing components can be later replaced. The application management and DevOps tools have matured to become effective in the public and hybrid cloud world. But, these tools are typically applied to discrete deployments where the infrastructure environment is well known.

Figure 4 - Tools for application management in public and hybrid clouds are well-suited for repetitive deployment of the same application footprint on well-defined infrastructure targets.

Figure 3 - Many sensors, beacons and standalone devices rely mostly on "North-South" traffic and are suitable for public cloud implementations. More sophisticated, latency-sensitive edge applications such as intelligent cars and 5G rely heavily on both "North-South" and "East-West” traffic which can only be provided by Edge Clouds with integrated networking.

Public Cloud Public Cloud

Internet Internet

North-South Traffic North-South Traffic

East-West Traffic

5G

DevOps Tools InfrastructureApplications

Jenkins

Kubernetes

Puppet

Consul

Ansible

Chef

SaltStack

Juju

Cisco

OpenStack

Docker

VMware

Dell

Microsoft Azure

LXD

AWS

Mesosphere

IoT

Process Automation

Machine Learning

App Services

Stream Analytics

Discrete

Deployments

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Edge Computing and IoT introduce a new set of challenges for these tools. The infrastructure at the edge consists of diverse types of compute, storage and networking technologies. Edge applications are typically sensitive to “environmental factors” that are not found in typical public cloud applications, such as latency, location, proximity to other devices and perimeter security. And, the make-up of edge applications is significantly different from traditional cloud-based applications. New edge applications can be made up of tens, if not hundreds of different “microservices” that can be run in specialized physical or virtualized environments. These microservices are very dynamic in nature, often being very loosely-coupled to other applications and services, and not instantiated (bound) to the application ecosystem until needed or dictated by external events or policies.

It would be impossible to create centralized DevOps tools and profiles that could handle the entire spectrum of edge application use cases. To mitigate this problem, two key elements must be added to the Edge Cloud deployment model:

A standards-based model

(manifest) that describes the

dependencies, behaviors and

service levels of the applications

must replace the traditional

scripts, XML files, spreadsheets

and run-books that are used for

run-time deployments. The most

widely-accepted standard for these

manifests is TOSCA (Topology

and Orchestration Specification

for Cloud Applications), which is

maintained by the OASIS.

The use of an Edge Cloud orchestration platform which

abstracts the complexity of the underlying Edge Computing

infrastructure from the applications and DevOps tools. The

orchestration platform should provide a full set of services

that deploy the application to the appropriate infrastructure

by translating the manifest and then applying operational

factors such as location, policy, proximity, resource types and

availability, capacity and security. To make edge applications

fully-functional, the orchestration platform must also

automatically configure and provision connectivity between

edge applications and devices (East-West traffic) as well as

connectivity to centralized back-end services (North-South

traffic).

DevOps Tools InfrastructureApplications

Jenkins

Kubernetes

Puppet

Consul

Ansible

Chef

SaltStack

Juju

Cisco

OpenStack

Docker

VMware

Dell

Microsoft Azure

LXD

AWS

Mesosphere

IoT

Process Automation

Machine Learning

App Services

Stream Analytics

ApplicationManifest

Edge Cloud

Orchestration

Location

Policy

Proximity

Resources

Capacity

Security

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CPLANE.ai – designed from the ground up for orchestration of massively distributed Edge Clouds

CPLANE recognized early on that the proliferation of IoT and Edge Computing devices was going to present a “scaling” problem of monumental proportions. With that in mind, the design of CPLANE’s Edge Cloud orchestration solutions center around, and then extend a model for deploying global-scale networking solutions.

Heal /Change

Monitor Orchestrate

Intelligence

Inventory / Topology

CPLANE’s high-performance, automated service provisioning platform utilizes an industry-unique closed-loop orchestration model to deliver unmatched Edge Cloud deployment and management capabilities. This model ensures full life-cycle integrity for Edge Clouds, especially in deployment scenarios where service integrity is critical (e.g., industrial controls, medical services, traffic management, etc.).

Closed Loop Orchestration

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Delivered through our Multi-Site Manager (MSM) and Dynamic Virtual Networks (DVN) products, CPLANE’s platform provides a rich set of orchestration capabilities and features across the entire spectrum of cloud models, from large data centers to micro-sites:

Northbound APIs for full service management integration with existing OSS/BSS, cloud brokers, DevOps tools, etc.

Application manifest support across a variety of formats, including TOSCA

Policy management (internal and external) to ensure services integrity

Extensible topology and information model to describe and abstract applications, services, devices, dependencies, etc.

Composite workflow engine to define sophisticated deployment scenarios

Service rollback to ensure operational and service integrity

Dependency mapping and what-if scenarios to implement unique operational and application blueprints

Integrated software-defined networking for both local and wide area network topologies

Gateway services for external networking and public/hybrid connectivity

Rich device and network protocol support to deploy a wide variety southbound devices

Virtual resource provisioning via OpenStack and Docker

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Clearing the complexity hurdle

Summary

About CPLANE.ai

Complexity is the killer of Edge Cloud deployments, and it comes in many forms – technology, operational, organizational, scope and scale to name just a few. Tackling these problems with existing processes and tools designed for public clouds will get you only so far. CPLANE’s integrated Edge Cloud orchestration solutions remove the problems of complexity and scale through automation with intelligence.

While many enterprises are just catching their breath from the onslaught of cloud computing, it’s time to get moving on the next wave – Edge Clouds. While the task at hand may appear daunting, or even insurmountable, waiting is not an option.

Getting started is easier than you might think. The DevOps tools that are already in place are great for application development and management. They just need some “help” applying them to a massively distributed scale-out model. That help comes in the form of an Edge Cloud orchestration platform that can abstract away the complexities introduced by the diversity of Edge Computing and IoT.

CPLANE.ai is ready to get you started with a fully-integrated orchestration platform and key relationships with industry-leading solution providers and systems integrators.

CPLANE.ai orchestrates and manages highly-distributed clouds for Edge Computing, IoT, Industrial IoT, MEC, Fog, and intelligent edge applications. We eliminate the complexity associated with deploying cloud resources to millions of Edge Computing end points, allowing enterprises and service providers to focus on value-added business and IT services.

To learn more about our fully-integrated cloud orchestration and software-defined networking solutions, visit us at:

www.cplaneai.com

Contact us:

info@cplaneai.com

+1 408.475.4950