researchHQ’s Key Takeaways:
- Before shifting to an edge computing model, companies should consider whether or not it is suited to their business needs and workload type.
- Companies also need to consider the suitability of the edge and their applications in terms of performance, capacity and connectivity.
- Security and compliance can inform decisions about whether or not to consume edge computing as a managed service.
- Companies can maximize investment and drive Return on Investment (ROI) by making careful considerations about which business model is suitable for their needs.
- The variety of edge computing model cost structures makes calculating the Total Cost of Ownership integral in order to further calculate the ROI.
What is the ideal place for my workloads? Should we move to the edge? Which edge computing model fits my business needs? What are the critical factors to take into account before deciding to move to the edge? These are some of the top questions that developers and IT decision-makers are considering as they develop their edge strategy. Understanding the different types of edge computing models is an important step before you build your strategy.
There are ten factors that you must consider while evaluating your edge strategy. The ten characteristics to consider are listed below:
1. Business need
Business needs always drive toward a solution. Before we start with the design considerations, we want to ask the question: Why? A well-defined customer need articulates the need from an end-user perspective. For example, prevent failure by predicting preventive maintenance needs on a manufacturing floor machine or providing a mobile application transaction response within milliseconds. The business need translates to crucial requirements and is a lead influencer in identifying the type of workloads required to achieve the functionality. It defines the quantitative and qualitative business outcomes desired such as customer satisfaction, cost savings, agility, operational resilience, staff productivity, and other tangible results.
2. Workload type
A collection of resources that performs a business function is called a workload. Business application hosting, data sharing, backup, storage, mission-critical applications, development, and testing are common workload types. Of these different workloads, specific workloads are well suited for on-prem while others fit well for cloud regions or edges. Real-time or near real-time control loop based workloads that require quick decision making are better suited for edge workloads. The entire workloads do not have to move to the edge. Instead, workloads can also be refactored to move certain functions to the edge while keeping the rest of the cloud services.
Each application will have specific performance requirements. There might be a dominant characteristic and one or more good-to-have attributes. For example, an application might require very low latency; however, another application might require significant compute and storage needs. Traditionally, we will use edge computing resources closer to the consumption point to provide low latency. For the application that requires a massive compute, it is cost-effective to move the edge processing to a different location away from the consumption point; however, this will increase latency. Suppose we have an application that requires both low latency and massive computing requirements. A more significant edge computing resource capacity can be deployed closer to consumption to achieve the low latency need; however, this will increase costs. Using a network with lower latency to compensate for the distance between the consumption point and the edge computing resources can be considered as alternatives. A 5G wireless network is such an alternative. Finding the right balance is vital as location and capacity influence performance.
Each application will have underlying resource requirements. For example, a particular function in an embedded device might require limited capacity; however, a video analytics-based back end application might require scalable GPU resources. It is essential to consider both current and future needs of quantity and type of computing resources. A small edge appliance might serve the immediate need; however, it might not be a scalable solution for the future. In this case, the decision of the type of edge computing might change. High availability, fault tolerance, and scaling are import factors to consider along with capacity.
Applications running on edge computing resources connect to multiple systems, including sensors and backend systems. Connectivity is an essential factor in enabling seamless communication and operations. An integrated connectivity model provides a plug-and-play experience without dependence on the customer’s connectivity channel. At the same time, customers might choose to enable a private network instead of using public transport. Similarly, wireless options eliminate the need for additional dependencies such as network ports or legacy interfaces. If a connectivity model provides shorter paths, that makes it an attractive option. Application dependency also can influence connectivity choices.