Edge-as-a-Service (EaaS)

Introduction –

Before understanding what is “EaaS - Edge as a Service”, we must understand what “Edge Computing” is.

Under the “Edge Computing (originally named as “Fog Computing” by Cisco)”, Focus is to bring compute and data storage close to the source of data (edge location) to improve response time and save bandwidth. Bringing computation near to edge location, minimizes the amount of long-distance communication that must happen between a client and server. Cloudlets, small version of clouds, provide compute resources for low-latency demands generated by emerging sensor-based edge devise.

In the IoT age, where studies say that around 75 billion IoT devices will be installed worldwide by 2025, huge amount of data is generated and need enhanced connectivity with high bandwidth to provide fact-based decision after analysis completion on server side. This needs to ensure that data analysis is done on edge location without transferring the complete data to centralized server and users get the desired results as per QoS requirements.

Distributed Cloud Architecture also plays a major role in edge computing. Distributed cloud is the application of cloud computing technologies to interconnect data and applications served from multiple geographic locations. Distributed, in an information technology (IT) context, means that something is shared among multiple systems which may also be in different locations. Distributed cloud speeds communications for global services and enables more responsive communications for specific regions.

Edge as a Service (EaaS) provides and manages end-to-end edge infrastructure. By considering the complexity and risk involved in Edge Devices management, EaaS provides complete solution with multiple building blocks e.g.  IoT devices, connectivity components and protocols, Edge Infra and Software, Cloud (Global) Infra, and centralized tool (Management and Operation).

Features of Edge as a Service –

• Asset Discovery & Assessment of Edge Devices
• Centralized Management of Edge Devices from remote location
• Single-Click Configuration and Automate Software Lifecycle Management
• Automate Operation (Healing/Reboot)
• Predictive Analysis

Architectural Diagram  

Edge Computing Solution has multi-layered architecture which distribute the workload between Edge layer (Edge Devices), Edge Servers, and Enterprise layer (Centralized Public Cloud Server), as shown in below diagram –

Components of Edge-as-a-Service –

1. Edge Devices – These Edge or IoT devices, with limited compute resources, are equipped with sensors, AI rules and stores some data locally to perform operation at the edge location. These devices communicate among themselves and communicate to Edge Servers by using protocols like REST, OPC-UA, MODBUS etc.
2. Edge Servers – These servers control the edge devices. It can schedule, manage, and distribute workloads on devices. These servers mainly run on container (Kubernetes) or on Hyperconverged Infrastructure with microservices application, which can analysis the data before sending to the enterprise server.
3. Enterprise Servers – These servers provide high process capacity and storage space to provide enterprise-level analytics and dashboards. These servers can be hosted in the public cloud or on-premises data center.

Tools for Edge-as-a-Service –   With the advancement of Edge computing rapidly, there is a need of single intelligent and monolithic platform that provides complete EaaS solution management capability or subset of multiple tools as explained below -  

1. Operation Management Tool – Integrated tool for monitoring (Failure Detection, Event Management), Availability, Performance, Capacity, Perform operational tasks on edge devices remotely. This tool provides reporting capability and centralized dashboard.  
2. Data Analysis and Predictive Analysis Tool – This tool analysis the industry- and/or technology-specific data on Edge Location and on Enterprise site (public cloud). This tool predicts the execution time of operation and resource utilization and performance. This tool helps to mitigate issues which can cause system failure and MTTR.
3. Automation and Orchestration Tool – This tool automate operational tasks (automate software lifecycle – Patch; Firmware Updates) and orchestrate tasks for edge devices. This tool provides self-service/operation capabilities.
4. Asset Management and Discovery Tool – This tool perform the asset discovery & assessment of edge IT infrastructure and edge applications at the edge location, which helps to understand the edge environment by providing visibility of OT environment.

EaaS Security - Security concerns are increasing with the adoption of IoT and edge computing. Below are some security challenges for Edge Computing –

1)     Data Integrity – As lot of data transferred from edge devices to edge servers and to enterprise server, biggest challenge is to securely transfer the data without modification.

2)     Authenticate and Access Control on Edge Devices and Servers.

3)     Incorporating security into design of edge devices.

To overcome the above-mentioned security challenges, EaaS Solution can be secured by implementing below solutions -

1)     Encrypt the data

2)     Secure User Access Management on Edge Devices and Servers

3)     Design the Edge Devices as per security standards

Use Cases of EaaS –

References –

https://www.edgexfoundry.org

https://www.edgeir.com/service-directory/edge-as-a-service-eaas

https://www.nutanix.com/solutions/remote-and-branch-office

https://www.ibm.com/cloud/edge-application-manager

https://www.cognizant.com/cognizant-digital-systems-technology/infrastructure-services/edge-services