As organizations make plans to leverage edge computing, and what edge devices are needed to make those plans successful, there are several questions IT leads usually ask. Here are some of those questions to help you understand if edge computing is the best option for you.
Edge Computing vs Cloud Computing
Edge computing and cloud computing are two different computing models with distinct architectures, purposes, and benefits. Here are the main differences between the two:
Definition: Cloud computing refers to the delivery of computing services such as servers, storage, and applications over the internet. Edge computing, on the other hand, involves processing data locally, closer to the source of data generation, such as IoT devices, sensors, and other endpoints.
Latency: Edge computing is designed to reduce latency by processing data closer to the source of generation. This results in faster response times and reduced network traffic. In contrast, cloud computing often requires data to travel over a network to a centralized server, which can introduce latency.
Bandwidth: Edge computing reduces the amount of data that needs to be transmitted to the cloud, which reduces bandwidth requirements and network congestion. In contrast, cloud computing requires high-bandwidth connections to transmit large amounts of data to and from the cloud.
Security: Edge computing can improve security by keeping data closer to the source of generation, reducing the risk of data breaches and other security threats. Cloud computing, on the other hand, requires data to be transmitted over a network to a central location, which can introduce security risks.
Scalability: Cloud computing offers greater scalability compared to edge computing since it can leverage the massive resources of cloud providers. In contrast, edge computing resources are often limited to the local device's processing power and storage capacity.
In summary, the benefits of edge computing are focused on processing data locally, closer to the source of data generation, to improve latency, reduce bandwidth requirements, and improve security. Cloud computing, on the other hand, is focused on delivering computing services over the internet, leveraging the massive resources of cloud providers to improve scalability and reduce costs.
Edge Computing vs Fog Computing
Edge computing and fog computing are both computing paradigms that aim to bring computing power closer to the end-users and devices that generate and consume data. However, there are some differences between these two approaches.
Edge computing refers to the practice of processing data on the devices themselves or on servers located at the edge of the network, closer to the data source. In edge computing, the data is processed locally, without the need for it to be transmitted to a central data center or cloud server for processing. Edge computing is typically used for applications that require low latency, such as real-time analytics, IoT devices, and industrial automation.
On the other hand, fog computing is an extension of edge computing that focuses on creating a distributed computing infrastructure that spans from the edge to the cloud. In fog computing, the data is processed not only at the edge but also on intermediate devices such as routers, gateways, and switches. This approach allows for more efficient processing of data by distributing the workload among multiple nodes in the network. Fog computing is typically used for applications that require a balance between low latency and high bandwidth, such as autonomous vehicles, smart cities, and healthcare.
In summary, the benefits of edge computing is the focus on processing data locally on the devices or servers at the edge of the network, while fog computing extends this approach by creating a distributed computing infrastructure that spans from the edge to the cloud. Both paradigms aim to improve the efficiency and performance of data processing by bringing computing power closer to the data source.
Edge Computing Examples
Edge computing refers to the practice of processing and analyzing data at or near the edge of a network, closer to the source of data generation. This approach offers several benefits, such as reduced latency, improved bandwidth usage, enhanced privacy, and offline operation capabilities. Here are some examples of edge computing applications:
Autonomous Vehicles: Edge computing enables real-time processing of sensor data within the vehicle itself, allowing for quick decision-making and reducing reliance on cloud connectivity.
Smart Cities: Edge computing is used in various smart city applications, such as intelligent traffic management, public safety monitoring, waste management optimization, and environmental sensing.
Industrial Internet of Things (IIoT): In industrial settings, edge computing facilitates local data processing and analysis, enabling real-time monitoring, predictive maintenance, and efficient resource allocation.
Telecommunications: Edge computing is utilized by telecom providers to bring computing capabilities closer to the network edge, resulting in faster data processing, lower latency, and improved quality of service.
Retail: Edge computing is employed in retail environments for tasks like inventory management, dynamic pricing, personalized marketing, and in-store analytics.
Healthcare: Edge computing can enhance healthcare services by enabling real-time analysis of patient data, remote monitoring, and quicker responses in emergency situations.
Video Surveillance: By performing video analytics at the edge, edge computing minimizes the need to transmit and store vast amounts of surveillance footage, providing faster and more efficient security monitoring.
Agriculture: Edge computing can be used in agriculture for tasks like crop monitoring, automated irrigation, livestock tracking, and optimizing resource usage based on real-time data.
Energy Grid Optimization: Edge computing enables real-time monitoring and control of energy grids, facilitating demand response, load balancing, and fault detection.
Edge AI Applications: Edge computing is often combined with artificial intelligence (AI) techniques to enable on-device AI processing for tasks like natural language processing, image recognition, and voice assistants.
These are just a few examples of how edge computing is being implemented across various industries. The potential applications of edge computing continue to expand as technology evolves.
Now that you are well-educated on edge computing, the next step is to understand what edge computing devices are available to you. Scale Computing pioneered the architecture that led to a new class of computing infrastructure, hyperconverged infrastructure (HCI). These all-in-one systems for storage, computing and networking check all the boxes for data center operations—flexible, scalable, reliable, cost-effective, and easy to use—the same benefits we experience with the cloud and now expect for on-premises edge computing.
But the edge is not the data center or the cloud in some very consequential ways. Edge locations typically have limited space and poor conditions for IT hardware; they lack on-site IT staff and may experience unreliable connectivity to the cloud or data center. Furthermore, edge deployments usually span multiple locations, sometimes in the hundreds or thousands. These characteristics demand infrastructure solutions that are naturally suited for the situation, not force fit or insufficiently adapted from products built for another purpose, including other types of hyperconverged infrastructure.
Save unnecessary hardware and software costs
Right-sized, edge-ready infrastructure combines compute, storage, virtualization, and disaster recovery into a single solution that eliminates the cost for each component individually. A standard three-node cluster provides built-in redundancy and fault tolerance at a lower price than fully-redundant, over-provisioned, duplicate systems. The unique architecture of the operating platform uses a fraction of the resources of alternatives. Get far more computing power and run more applications with a single platform using significantly lower-cost hardware.
Simplify management and free IT teams from time-consuming maintenance
From initial deployment and routine system maintenance to capacity expansion and hardware replacement, administrative tasks are easily automated and remotely executed using a centralized management platform. Enjoy automated, error-free provisioning and configuration for deploying new infrastructure without IT staff on-site. Complete system updates and exchange hardware while keeping applications online. Allow the self-monitoring, self-healing system to monitor, detect and correct problems in real time without any IT intervention. With infrastructure this easy, IT teams can stop looking after environments and spend more time supporting valuable technology initiatives.
Scale Computing Platform
SC//Platform is an all-in-one software platform for running edge applications. It replaces complex, costly, and difficult-to-manage solutions with a single, easy-to-use platform that can be deployed almost anywhere. The fully integrated compute, storage, virtualization, and disaster recovery environment simultaneously runs legacy and modern applications on the same infrastructure.
The lightweight software stack can be packaged on virtually any hardware specification, from the extremely small Intel NUC up to any size rack mount server. Therefore, SC//Platform delivers far more computing power while running on significantly smaller, lower-priced edge computing devices.
Designed for locations with no IT staff on-site, autonomous, self-healing capabilities maximize the uptime and performance of applications. Applications on SC//Platform run independently so they can survive disruption in service from the centralized cloud or data center. Advanced failure detection and mitigation technology constantly monitors for potential problems and takes corrective action automatically. As a result, applications remain online and no data is lost, even when a failure occurs.
The centralized management portal makes managing and monitoring a growing fleet of edge computing systems easier than ever. The familiar, cloud-like experience of Scale Computing Fleet Manager automates error-free provisioning and configuration of new infrastructure without the need for skilled IT staff on-site. Firmware and software updates are applied as system-managed live upgrades and hardware exchanges are automatically incorporated directly into the existing environment with no application downtime. From initial deployment and routine system maintenance to capacity expansion and hardware replacement, administrative tasks are easily automated and remotely executed. Demand on IT departments actually decreases even as more edge computing locations are added.