Introduction
Edge computing is a distributed computing model that brings computation and data storage closer to the location where they are needed, improving response times and saving bandwidth. With the rapid growth in Internet of Things (IoT) devices, small cell networks using 5G wireless technology, and microservices-based applications running containers, it is becoming easier to construct intelligence virtually anywhere. This shift allows latency-sensitive workloads to be executed just milliseconds away from the consumers of those apps.
Similarly, edge computing is changing the way developers build apps. Instead of utilizing a few large compute resources in the cloud, they can partition a single workload into smaller pieces and position code at a hierarchy of distributed clouds residing near users at the edge of the internet.
Companies like Minterminds innovative software solution providers, are already exploring how to integrate edge computing principles into modern app projects. Under the leadership of Dinesh Kumar, founder and CEO of Minterminds, the company is helping organizations adopt cutting-edge solutions that improve efficiency and user experience.
Comparison with Cloud Computing
The concept of ‘‘cloud computing’’ is so well established and accepted that applications hosted in the cloud are no longer just called ‘‘cloud applications’’ but rather just ‘‘web applications, whereas all other applications are simply considered ‘‘non-web applications. As a result, many people do not perceive ‘‘cloud’’ as a model for computing but rather as just a single location for hosting web apps. Consequently, the ‘‘edge’’ is considered most simply as the opposite of the cloud. This is not surprising because every edge location must have a connection and communication channel to the cloud.
Challenges in Implementing Edge Computing
3. Key Technologies Enabling Edge Computing
While Low Latency and Location Awareness are the two hallmarks of edge computing, the striking difference between the cloud paradigm and edge paradigm primarily lies in the architectural implementation. In the cloud paradigm, everything is located near a single central location; but in the edge paradigm, the data and services are separated into clusters, which are located at several megacities of the country that are connected through high bandwidth optical fiber links. However, these features would not have been effective without the following three permitting technologies.
IoT Devices
The growth of Industrial Internet of Things (IIoT) devices, Smart Sensors, Widely Connected Devices (WCDs), and geological surveying facilities has led to an extraordinary increase in the density of the edge requirements. Connected devices generate pilot data such as camera images or gyroscope readings that require rapid processing. These types of devices act as the main triggering factors for Latency Awareness. Connected devices in Robots and Autonomous Cars require location awareness based on their locations. These types of application domains impose extreme demands on the Edge service provisioning.
5G Networks
With the coming of the next generation (5G onwards) mobile broadband, the service providers are expecting network latency that is less than one-tenth of what is achieved in Long-Term Evolution (LTE). Life-threatening applications such as remote surgery require secure and fast information exchange at network delay. Applications such as IoT in Agriculture require Analytics at multiple locations. Supporting such applications demands network changes, clustering, and replication of the resources. The 5G network supports the concept of network slicing, which allows the network to be split logically into groups with distinct properties and allows users to connect to a specific slice of the network.
Regulatory and Compliance Issues in Edge Computing
Edge computing introduces a regulatory dimension that might initially appear less intimidating. For instance, the platform receives a text file instructing a scanner in a gas monitoring network in Australia to measure carbon monoxide levels. The platform forwards this message and, moments later, receives a message in reply—in this case, a message with the measurement results from the scanner. This interaction adheres to the system’s requirements and regulatory constraints. The physical location of the scanner can be disregarded except during emergencies, when additional information will be requested for business continuity purposes.
Conversely, a regulatory file transmitted in a similar manner to a scanner located in the United States receives a location-specific reply. This barrier stems from additional regulatory demands that require awareness of the device’s location to either deny the transmission or impose stricter filtering before the data reaches the intended destination. Such considerations are typical for equipment deployed offshore for drilling and exploration activities. From a regulatory standpoint, these devices that produce data are analogous to manufacturing plants for hazardous materials.
Organizations that adapt early will have a clear advantage in delivering the seamless, high-performance experiences users demand. Partnering with innovators in the space, such as Minterminds innovative software solution provider, can help firms future-proof their apps with edge-ready designs.
