As Demand for Edge Computing Accelerates, Organizations Need to Consider an Edge-to-Cloud Strategy to Drive Automation and Orchestration

Edge computing is rapidly becoming a key component of an organization's digital transformation journey. Organizations are deploying smart cameras, sensors, Internet of Things (IoT) devices, and edge gateway servers on edge networks, supported by 5G to optimize business efficiency and accelerate productivity.

There has been a slew of announcements from technology providers addressing the pressing needs of edge management, with VMware introducing its Edge Cloud Orchestrator, a network automation and orchestration tool that helps organizations install, configure, operate, and maintain edge deployments using a software-defined edge solution. Dell Technologies, as part of its Project Frontier initiative to deliver edge operations software, announced NativeEdge, an application that provides centralized management, zero-touch deployment, onboarding, and automated infrastructure and software operations from edge to cloud. Lenovo also announced its Xclarity edge-to-cloud management software, aimed at simplifying the orchestration, maintenance, and metering of all Lenovo edge-to-cloud solutions.

Amazon Web Services (AWS) provides end-to-end cloud and edge orchestration solutions such as AWS SageMaker for Machine Learning (ML) development in the cloud, AWS Outposts for edge and on-premises deployment, and AWS Snowcone, an edge device used for data storage/transfer on-the-go, suitable for rugged environments with little to no network connectivity. Microsoft Azure’s IoT Edge platform can be deployed at the edge and used to consolidate operational business data in the Azure cloud. Organizations can manage and deploy workloads from the cloud using the Azure IoT Hub platform as part of the Azure IoT Edge solution.

A weak edge-to-cloud orchestration strategy can negatively impact organizations. For example, take the retail industry. Most retailers use sensors and hand-held scanners for inventory management. Without a strategic edge-to-cloud orchestration strategy, a retailer will have challenges integrating data across various store locations into the centralized cloud inventory solutions. The retailer will not be able to have access to the latest and most accurate inventory levels, ultimately impacting customer experience.

In the financial services industry, particularly in financial trading, a seamless edge-to-cloud orchestration platform is needed to enable low-latency data processing, which is crucial for high-frequency trading. Any delays in data processing and transfer can result in missed financial opportunities and losses.

Edge-to-cloud orchestration delivers data center-like computing to the edge, helping organizations overcome network connectivity challenges such as low bandwidth and high latency, resulting in higher costs to the business. Some other benefits of deploying an edge orchestration platform include:

• Data Integration: Edge computing offers data processing closer to the source, allowing for real-time decision-making. However, organizations will still need to transfer data from edge devices to a centralized data center for further analysis or training processes. An edge-to-cloud orchestration platform facilitates the integration and management of data from the edge to an organization’s preferred cloud platform, ensuring these two environments are not working in silos.
• Privacy and Security Compliance: Storage and processing of data at the source ensures that organizations comply with local data privacy regulations, especially in terms of customer or personal data. Organizations can deploy an edge-to-cloud orchestrator platform to only transfer relevant data to the cloud, leaving sensitive data at the edge, ultimately ensuring privacy and security compliance.
• Centralized Management and Control: Organizations need to ensure processes and analysis done at the edge can integrate securely and reliably into the larger internal organization network. An edge-to-cloud orchestrator allows organizations to plan, deploy, and manage devices/workloads from the edge and have the same experience and control in both the edge and in the cloud.

In a digital-first world, edge computing is fast becoming central to any organization’s cloud strategy, with edge and cloud working together to provide a differentiating factor among organizations. Cloud hyperscalers like AWS, Microsoft Azure, Google Cloud, and Alibaba Cloud enable organizations to grow quickly and develop innovative products with fast time to market.

Cloud hyperscalers will play a big role in edge-to-cloud orchestration, along with multi-cloud solutions providers such as VMware and Red Hat. However, before deploying any edge-to-cloud orchestration platforms, careful planning will need to be assessed by organizations before execution. Some of the considerations include:

• Automation and Optimization: Business optimization and autonomous processing should be top of mind when deploying edge-to-cloud applications. For example, a heavy machinery manufacturer is looking to embark on a factory automation exercise, using scanners, cameras, and other IoT devices on the factory floor for predictive maintenance, while also replacing legacy hardware infrastructure with cloud-native solutions. Ultimately, the goal is to increase factory uptime, improve the quality of products, and increase product innovation.
• Cloud Flexibility with Local Security Compliance: Organizations in the financial industry often need to be extremely responsive to customer needs, as well as having to manage branches that are scattered worldwide. Leveraging an edge-to-cloud orchestrator allows banks to scale using cloud platforms, while ensuring sensitive customer data are stored and processed at the local source, and ensuring compliance with local data regulations and governance.

ABI Research recommends that organizations carefully evaluate edge-to-cloud orchestration solutions in the market. Some key areas of interest include open standards and frameworks for ease of integration, leveraging distributed intelligence or edge AI for lightweight analytics, and developing a plan for managing edge devices, especially in handling software and hardware updates.

Edge computing and cloud computing go hand in hand. Organizations will need to consider the recommendations above to ensure operations at the edge network integrate securely and reliably with the larger central network to fully reap the rewards of an edge-to-cloud framework.