Edge Computing vs Cloud Computing: Which One Is Right for Your Business?

Cloud computing is a centralized delivery model for computing services over the internet, where businesses can obtain data storage, processing power, and applications from third-party providers’ remote servers rather than local servers or personal devices.

Cloud computing has centralized data processing since data is shipped to far-away data centers for processing, resources are scalable in the sense that they will adjust based on need, and the services are charged on a pay-as-you-go basis, which is usually advantageous for the customer by reducing the need for upfront investments. For organizations looking to optimize their cloud environment, working with specialists in aws cloud managed services can significantly improve efficiency and reduce operational overhead. These attributes significantly reduced the need for equipment hosted on-site, or on-premises, and in exchange for on-campus usage, the services offer wider network access anywhere on the internet.

The largest cloud service providers, AWS, Microsoft and Google, have built large data center infrastructure across the world, which offers businesses reliable, nationwide infrastructure without the substantial burden of investments. For Microsoft cloud environments, many organizations find that professional azure managed services help them maximize their investment and modernize operations. The centralized computing model has changed the way businesses deploy software and manage IT resources by transforming compute and storage into a service model for delivery of services while gaining unprecedented flexibility and cost appropriations.

Edge computing moves computational power and data storage closer to where the data is generated. Applications using this approach will perform their data processing locally, even when you’re using a comparatively slow in-house server.

Edge computing utilizes a decentralized processing model that brings computing near data generation, thereby reducing the latency for applications where timing is critical. This approach conserves bandwidth by decreasing the distance data is sent over a network and enhances privacy by keeping sensitive data local. Implementing edge solutions often requires specialized expertise in both networking and devops development and consulting services to ensure optimal configuration and performance. Many edge computing systems process data openly with weak connectivity and provide instantaneous data analysis for better, real-time decision-making.

Edge computing will be most beneficial for time-sensitive applications such as autonomous vehicles, industrial automation, and IoT devices. As the number of devices connected to your network increases exponentially, edge computing versus cloud computing becomes an important consideration for any enterprise concerned with optimizing their network development.

Deciding whether to use edge computing or cloud computing isn’t always black and white, and it may require analysis to determine which option is the best fit for your business needs.

Use the cloud when it is important to consolidate data when analyzing busted datasets across multiple data sources, if you require flexibility of resources based on demand, or if you need to use resources globally across a distributed team. Many businesses find that partnering with providers of comprehensive managed cloud services helps them navigate this complex landscape more effectively. Cloud applications are also good options when there are budget challenges for capital expenditures and for applications where latency is not problematic.

Use edge computing when real-time processing is important, when you are working in environments with certainty of network connectivity, or when you have concerns about the cost of bandwidth to transmit a high volume of data. Edge applications make the most sense when the data privacy requirements mandate that sensitive information must be kept local and when the application will have critical outcomes based on millisecond performance.

Many businesses find the best option is a hybrid solution of both edge and cloud computing. This allows immediate processing of time-sensitive data at the edge, and the cloud can be utilized for longer-term storage of complex analytics. A hybrid option allows for economy of resources in the combined compute environments, will handle different connectivity circumstances, and support both capital and operational expenditure.

The connection between cloud computing and edge computing has transformed significantly over time. Originally, cloud computing was a groundbreaking approach of moving computing away from local devices and toward centralized data centers. This all-in-one environment had vast advantages in resource efficiency, maintenance, and cost savings.

But as the Internet of Things (IoT) had many applications and vast amounts of data generated, the problems with an entirely cloud-based approach began to be more visible. Bandwidth limits, latency problems, and privacy concerns promoted the evolution of edge computing as a tool for cloud computing.

Today, edge computing and cloud computing work together symbiotically. Time-critical operations are performed at the edge, while complex processing and long-term storage take place in the cloud. The two approaches can complement each other and enable the development of responsive, efficient, and scalable systems that can be designed more easily than they could be without a combination of the two.

A manufacturing company that builds automotive vehicles installed edge computing on the factory floor to monitor the performance of equipment in real-time. Specifically, sensors collect machine performance data from the production equipment, and while the data is transmitted to the edge, edge devices serve as a first layer of anomaly detection and predictive maintenance data. After realizing the initial production data, it gets sent to the cloud for long-term storage and advanced analysis that can inform optimized manufacturing processes over time.

This hybrid approach of edge versus cloud computing allowed the company to reduce downtime by 37% while improving overall equipment effectiveness by 22%. The edge devices respond to critical situations in milliseconds, while cloud systems identify long-term improvement opportunities through comprehensive data analysis.

A healthcare provider deployed edge computing devices in their critical care units to analyze patient vital signs in real-time. The edge systems can immediately detect life-threatening changes and alert medical staff without network delays. Patient information that is less sensitive to time is sent to cloud environments for retention, compliance documentation, and extending analytics and trends that help adjust treatment protocols.

By leveraging both edge and cloud computing concurrently, the hospital reduced alarm fatigue by 45% while achieving a 28% improvement in early intervention. The edge computing component affords confidentiality for the patient and produces immediate results, while cloud computing supports broader health trends among the patient population.

A retail chain leveraged edge computing in their stores to run computer vision-based systems to study customer behavior and optimize store layouts. The edge devices processed video inputs locally to maintain customer confidentiality while aggregating and anonymizing aggregated data. This processed data was sent to cloud-based environments for larger trend analysis across all stores that informed decisions related to inventory and staffing requirements.

The strategic implementation of cloud and edge computing led to a 15% increase in conversion rates and an estimated 23% improvement in inventory turnover. The edge computing component of the system produces personalized recommendations to customers in real time, while the cloud computing systems identify and reflect larger trend analyses across the retail network.

Security will be an important consideration when evaluating edge computing versus cloud computing. Each implements its unique set of challenges and benefits at the same time. Organizations must understand and balance these security considerations.

Security in cloud computing is centralized and managed by a provider with vast capabilities and knowledge. Centralized security management is simpler than distributed security management and leads to a centralized target for attack. While an organization could never afford the level of security a large provider applies to the system, the major cloud providers operate with these types of security operation costs built in—creating a layer of security against various threats.

Security in the edge computing model is much less centralized and more distributed across multiple locations/devices. While this makes security management more complex or convoluted, it reduces the impact of a security incident on a device or system, reducing the potential damage from an incident. However, edge devices still fall under threat of physical manipulation or intrusion, and many exist in locations that can be easily accessed or tampered with. Nevertheless, edge devices can help with privacy by keeping data on or near a device and not sending sensitive data to other locations or cloud environments.

A comprehensive security strategy for environments using both edge and cloud computing should include:

• End-to-end encryption for data in transit between edge and cloud
• Robust identity and access management across all environments
• Regular security updates and patch management for edge devices
• Clear data classification policies determining what’s processed at the edge versus in the cloud
• Continuous monitoring and anomaly detection throughout the distributed system

Both edge computing and cloud computing are continuing to evolve as technology changes. Below are some of the emerging trends that will lead edge computing and cloud computing:

1. 5G Integration: The introduction of 5G networks will significantly expand the capabilities of edge computing, providing a much faster, dependable connection between edge devices and resources in the cloud. The benefit of 5G will allow the development of a new generation of applications that require extremely low latency and increased bandwidth.
2. AI at the Edge: Developing AI algorithms and enhanced hardware are bringing edge devices a form of AI that will process content and information at the edge without having to send information to cloud resources. Doing this will minimize latency for critical applications.
3. Edge-Cloud Continuum: The lines between edge and cloud are starting to blur, ushering in a computing continuum, where workloads shift from one environment to the other dynamically over time as the needs of the business change in real time. This fluidity will result in better resource use across the entire computing landscape.
4. Serverless Edge Functions: The serverless computing model continues to develop in edge computing environments, allowing programmers to deploy code without the headaches of managing the underlying infrastructure. This development will drive faster and easier development and deployment across distributed systems.
5. Industry-Specific Edge Solutions: We are now seeing vertical-specific edge computing solutions emerge, i.e., edge computing solutions for healthcare, manufacturing, retail, etc. Solutions tailored to specific use cases, including hardware and software, continue to emerge.

Organizations that remain up to speed on these trends within cloud and edge computing will be better positioned to capture the right technologies for their ever-evolving business needs. 

We have solutions for each combination of cloud and edge computing deployment with our services at AppRecode. Some of our team members conduct an infrastructure assessment to help our clients gain a better understanding of their current environment and make recommendations for deployment models wherever possible. We also can implement custom cloud or edge computing solutions for our clients with fabrication and integration across the cloud and edge environments. Finally, we’re able to put in place security measures throughout the entire distributed environment. We provide ongoing management and plan for future growth of the infrastructure.

Our professionals work with your team to learn more about your specific needs and create a solution that meets a targeted balance of performance, cost, and reliability. We have a specialization in designing hybrid architectures that use the complementary advantages of edge computing and cloud computing to solve your specific business issue.

With expertise across all of the major cloud platforms and edge computing technologies, AppRecode provides organizations with the framework to navigate the difficult decisions associated with edge versus cloud computing. Our consultants are vendor neutral and are not focused on pushing products or technologies for your desired outcome, but rather the desired outcome for your business.

The choice of edge computing or cloud computing is not an all-in-one decision. Each approach offers different benefits for specific use cases, and many organizations will do well to exploit both in a strategy that interconnects them. By learning the strengths and weaknesses of each model, businesses can make more informed decisions about their operational needs as they grow and add capacity for their budget.

As data volumes increase and the need for processing in real time increases, the intentional adoption of both edge and cloud computing will become an area of competitive advantage for organizations that are future-facing. The difficulty will not be in choosing one over the other but in figuring out the right balance and a way to integrate edge computing versus cloud computing for your specific business needs.

Edge computing handles data analysis nearer to the point of generation (either on devices or a local server) to improve latency issues and bandwidth utilization. Cloud computing involves the processing of data in centralized data centers and is better for scalability and raw computing capability but with possible latency concerns.

No, edge computing is seen as an addition to cloud computing rather than a replacement. Most organizations deploy both computing types in combination and layered deployments to use the edge for time-sensitive processing and the cloud for data storage, advanced analytics, and similar applications that do not require real-time processing responses.

The cost-effective model varies, depending on the use case. Cloud computing usually reduces capital expenditures but can have a higher total cost of operational expenditures over time. Edge computing usually incurs higher initial Capex expenditures but can dramatically reduce bandwidth and data transfer costs for data-intensive applications.

Yes, a hybrid model is particularly common that includes both edge and cloud computing. Businesses can use edge computing for time-sensitive data processing and the cloud for data storage, advanced analytics, and less time-sensitive applications. Hybrid also provides additional benefits from both edge and cloud computing models.

Industries that benefit most from the utilization of edge computing include those industries that have a time-sensitive application or that generate data. Manufacturing can utilize edge computing for real-time system monitoring, health care can apply use cases for patient monitoring systems, transportation would be applicable for autonomous vehicles, retail stores and distribution centers for in-store analytics, and telecommunication industries would be the final use case example for optimizing their network.